Cross-Border E-Commerce AI Skills

55 AI-powered skill templates for cross-border e-commerce — from brand strategy to Amazon operations to DTC growth to finance & capital ops to affiliate-program building to EU channel entry to overseas-buyer prospecting + earned-media press discovery + Reddit pre-purchase VOC.

Compatible with Claude Code (~/.claude/commands/), Google Antigravity (SKILL.md), and any AI IDE with skill/prompt support.

中文说明 | English

English

What is this?

A collection of 55 AI agent skills (structured prompt templates) that automate the entire cross-border e-commerce workflow — brand strategy, market research, product selection, listing optimization, advertising, DTC site operations, finance & capital management, affiliate-program building, EU channel & market entry, social media, influencer marketing, overseas-buyer outbound prospecting, earned-media press discovery, and Reddit pre-purchase VOC.

Two formats:

• Single-file skills (45) — one .md file each, drop into your AI IDE's skill directory.
• Multi-file skill packages (5, under brand-strategy/, outbound-prospecting/, and voc-tools/) — SKILL.md + references/ + templates/ (incl. Python scripts and CSV trackers). Point your AI IDE at the package directory.

Plus 7 standalone tools under tools/ (Python utilities used by skills, also runnable independently): backlink-kol-extractor, trustpilot, linktree-expander, contact-extractor, api-pacer, fetchlib, browser-fetch.

Skill Map (58 skills across 13 chains)

                        ┌─────────────────────────────────────┐
                        │     Brand Strategy Chain (10)        │
                        │                                     │
  Market Scan ──► Track Hypothesis ──► Deep Validation ──► Strategy Plan
       │                                                       │
       │         Annual Plan ◄── Budget Ops                    │
       │                                                       ▼
       │                                    IMC Framework ──► Knowledge Base
       │                                         │
       │              A/B Compare    Chart Visualize   GTM Launch
       │                                         │
       ▼                                         ▼
  ┌──────────────────┐   ┌──────────────────┐   ┌──────────────────┐
  │ Amazon Chain (14) │   │ DTC Site (5)     │   │ Social & KOL (5) │
  │                  │   │                  │   │                  │
  │ Selection        │   │ SEO Diagnostic   │   │ TikTok Growth    │
  │ Shortlist        │   │ SEO Playbook     │   │ YouTube Ops      │
  │ Market Research  │   │ SEM Ads          │   │ Content Calendar │
  │ IP Risk          │   │ Conversion UX    │   │ Influencer Mktg  │
  │ Supplier         │   └──────────────────┘   │ User Lifecycle   │
  │ Keywords         │                          └──────────────────┘
  │ Listing Copy     │   ┌──────────────────┐   ┌──────────────────┐
  │ Main Image       │   │ Offline (1)      │   │ VOC Tools (3)    │
  │ A+ Content       │   │                  │   │                  │
  │ Compliance       │   │ US Retail        │   │ Reddit VOC (NEW) │
  │ Pre-Launch       │   └──────────────────┘   │ Trustpilot Quick │
  │ Ad Architecture  │                          │ Trustpilot Deep  │
  │ Weekly Ad Review │                          └──────────────────┘
  │ Ad Diagnosis     │
  └──────────────────┘

  ┌──────────────────────────────────────────────────────────────┐
  │ Finance & Capital (8) — NEW                                    │
  │ Unit-Economics · FX/Payout · Tax-Nexus · Cashflow             │
  │ Pricing · Reconciliation · Entity-Structure · Capital-Stack   │
  └──────────────────────────────────────────────────────────────┘

  ┌──────────────────────────────────────────────────────────────┐
  │ Affiliate & Partnership (2) — NEW                              │
  │ Readiness-Audit (affiliate's-POV mirror of CRO) ·             │
  │ Program-Ops (AI: DB → score → outreach → activate → report)   │
  └──────────────────────────────────────────────────────────────┘

  ┌──────────────────────────────────────────────────────────────┐
  │ Channel & Market Entry (2) — NEW                               │
  │ EU-Channel-Entry (SSPV · RCS · growth flywheel) ·            │
  │ Price-System & Channel-Conflict Guard                         │
  └──────────────────────────────────────────────────────────────┘

Brand Strategy Chain (11 skills)

4-Round Analysis Pipeline + Planning + Execution + Tools

Amazon Operations Chain (14 skills) — UPGRADED v3.14 (2026 algorithm & policy refresh)

DTC Site & Traffic (5 skills)

Finance & Capital (8 skills) — NEW v3.8

Operator-facing finance/CFO chain for China→US/EU DTC + Amazon sellers — the actuals / profitability / tax / cash / financing side that the planning-oriented brand-budget-ops doesn't cover. Every skill is grounded in real tools + 2026 regulations, carries an explicit YMYL disclaimer (planning aid, NOT professional tax/legal/accounting advice — verify with a licensed CPA), and tags every rate/threshold/date as point-in-time. Built and cross-validated from a dual-source research pass (multi-agent web research + an independent deep-research report).

Social Media & Content (3 skills)

VOC & Review Analysis (3 skills) — NEW v3.5 adds pre-purchase VOC

VOC tools split by decision stage. Reddit / Quora capture pre-purchase intent (still-deciding users), while Trustpilot / Amazon Review capture post-purchase experience (already-bought users). Use them together for full decision-funnel coverage.

KOL & User Operations (2 skills)

GTM & Offline (2 skills)

Affiliate & Partnership (2 skills) — NEW v3.13

Building an affiliate/partnership program from the affiliate's economics — audit first (is your product even worth promoting?), then run the AI-automated recruit → score → outreach → activate loop. Complements influencer-marketing (content/relationship lens) without overlap: this is the performance/ROI lens.

Channel & Market Entry (2 skills) — NEW v3.13

Outbound Prospecting (3 skills) — v3.4 adds press discovery

End-to-end pipelines for finding overseas B2B decision-makers, KOLs, and journalists, then converting them into ready-to-message lead sheets. Each skill is a multi-file package with SKILL.md + scripts + references + templates.

Sister skills — same 4-stage shape (Search → Enrich → Outreach → Compliance), different channels. Designed to run in parallel for the same lead set.

Tools (standalone utilities)

Standalone Python utilities under tools/. Each is a multi-file package with own SKILL.md + scripts/ + references/ + templates/. Used by skills above conditionally; also runnable independently. The three fetch tools — api-pacer, fetchlib, browser-fetch — compose into one compliant scraping pipeline (pace → waterfall → browser L3); the rest are data/enrichment utilities.

See tools/README.md for standalone usage.

Key Features

• 55 Skills, 13 Chains — Complete coverage from brand strategy to daily operations to finance & capital to affiliate-program building to EU channel entry to overseas-buyer outbound to earned-media press discovery to pre-purchase Reddit VOC
• Affiliate & Channel Systems (2026 decks) — a 2-skill affiliate chain built from the affiliate's own economics (affiliate-readiness-audit mirrors CRO — "why affiliates won't promote you"; affiliate-program-ops runs the AI recruit→score→outreach→activate loop), plus a US-entity Europe channel-entry strategy (SSPV / RCS / growth flywheel) and a multi-channel price-conflict guard. Framework skeletons only, each crediting its source deck in a methodology index
• Finance & YMYL Discipline — the 8-skill finance chain carries explicit "planning aid, not professional tax/legal/accounting advice — verify with a CPA" disclaimers, point-in-time-stamped 2026 regulations, and ⚠️-flagged estimates (no fabricated numbers)
• Data Verification Layer — Every skill includes mandatory verification; estimates are explicitly flagged with ⚠️
• Chart Visualization — 21 skills auto-generate charts (radar, bar, waterfall, scatter, funnel, etc.) via AntV API
• Semrush Integration — Brand strategy skills auto-scan local Semrush xlsx/PDF data as high-confidence source
• VOC Matrix — Mention frequency × satisfaction matrix to identify unmet needs
• GTM Flywheel — Market → Product → Marketing → Operations four-wheel evaluation
• AI Search Ready — Optimized for Amazon Rufus, COSMO knowledge graph, and GEO
• Multi-Agent Concurrency (Claude Code-native) — skills like dsite-conversion-ux orchestrate parallel recon + analysis subagents via the Workflow tool with StructuredOutput schemas, run as background tasks, and use browser-class MCP (Claude in Chrome / Preview) for live-site inspection — fan out the work, keep the conclusions
• Compliant Scraping Stack (2026-benchmarked) — a free-first, US-compliant fetch pipeline the skills share: api-pacer (pacing + backoff) → fetchlib (waterfall: curl_cffi → Jina Reader → browser → paid) → browser-fetch (browser-render L3). Honors robots + rate limits; no barrier-defeat / IP-rotation / CAPTCHA-solving / PII. Benchmarks, tier mechanics, and the full compliance red line live in each tool's SKILL.md
• Linted, not just written — every skill carries name + description frontmatter (the triggering surface), and a Skill lint CI gate blocks four regressions this repo actually hit: missing/mismatched frontmatter, the same skill living at two paths (copies drift apart silently), dead relative links, and any advice that would break the scraping red line. Run it locally with python3 scripts/lint_skills.py
• Multi-Platform — Works on Claude Code, Google Antigravity, OpenClaw, and any AI IDE

Installation

Claude Code (recommended):

git clone https://github.com/noique/cross-border-ecommerce-skills.git

# Single-file skills → ~/.claude/commands/
cp cross-border-ecommerce-skills/brand-strategy/*.md ~/.claude/commands/
cp cross-border-ecommerce-skills/amazon/*.md ~/.claude/commands/
cp cross-border-ecommerce-skills/finance/*.md ~/.claude/commands/

# Multi-file skill packages → ~/.claude/skills/ (one directory per skill)
cp -r cross-border-ecommerce-skills/outbound-prospecting/google-whatsapp-prospecting ~/.claude/skills/
cp -r cross-border-ecommerce-skills/outbound-prospecting/linkedin-prospecting ~/.claude/skills/
cp -r cross-border-ecommerce-skills/outbound-prospecting/media-press-discovery ~/.claude/skills/
cp -r cross-border-ecommerce-skills/voc-tools/reddit-voc ~/.claude/skills/
cp -r cross-border-ecommerce-skills/brand-strategy/serp-content-teardown ~/.claude/skills/
cp -r cross-border-ecommerce-skills/tools/backlink-kol-extractor ~/.claude/skills/

Google Antigravity / OpenClaw / Any AI IDE:
Copy .md files (single-file skills) or whole directories (multi-file packages) into your skill directory.

Model Requirements

Key requirements: long context (8K+ input), strong instruction following, Chinese-English bilingual, tool use / web browsing.

中文说明

这是什么？

一套 58 个跨境电商 AI 技能模板，覆盖品牌战略→选品→调研→文案→广告→独立站→财务资金→联盟营销→欧洲渠道进入→社媒→红人→线下渠道→海外开发→媒体公关→购买前 Reddit VOC 全流程自动化。

两种格式：

• 单文件技能（45 个） — 一个 .md 文件，放入 AI IDE 技能目录即可使用
• 多文件技能包（5 个，分布在 brand-strategy/、outbound-prospecting/ 和 voc-tools/） — SKILL.md + references/ + templates/（含 Python 脚本和 CSV 跟踪表），将整个目录指向 AI IDE

外加 7 个独立工具 在 tools/（Python 工具，被 skill 调用也可独立使用）：backlink-kol-extractor / trustpilot / linktree-expander / contact-extractor / api-pacer / fetchlib / browser-fetch。

技能矩阵（58 个技能，13 条链路）

核心特色

• 55 技能 × 13 链路 + 7 独立工具 — 从战略到执行到财务资金到联盟营销到欧洲渠道进入到海外开发到媒体公关到购买前 Reddit VOC 全覆盖
• 联盟与渠道体系（源自 2026 行业分享） — 从联盟客的经济账反推的 2 技能联盟链（affiliate-readiness-audit 是 CRO 的镜像——审"联盟客为什么不推你"；affiliate-program-ops 跑 AI 招募→评分→建联→激活闭环），外加美国主体的欧洲渠道进入战略（SSPV / RCS / 增长飞轮）与多渠道价格冲突护栏。只抽方法论骨架，每个技能在"参考方法论索引"注明来源分享
• 财务链 YMYL 纪律 — 8 个财务技能均带"规划辅助、非专业税务/法律/会计意见、需找 CPA 核实"免责，2026 法规打时间戳，估算标 ⚠️（不编造数字）
• 数据验证层 — 每个技能内置强制验证，推测数据标 ⚠️
• 图表可视化 — 21 个技能自动生成图表（雷达/柱状/瀑布/散点/漏斗等），调用 AntV API
• Semrush 集成 — 品牌战略技能自动扫描本地 Semrush 数据
• VOC 用户洞察 — 提及量×满意度二维分析
• GTM 飞轮 — 市场→产品→营销→运营四维评估
• AI 搜索适配 — Amazon Rufus / COSMO / GEO 优化
• 多 Agent 并发（Claude Code 原生） — dsite-conversion-ux 等技能用 Workflow 工具编排并发侦察+分析子代理（StructuredOutput schema），后台任务运行 + 浏览器类 MCP（Claude in Chrome / Preview）做实站检测——把活儿 fan out，只留结论
• 合规抓取栈（2026 实测） — 各 skill 共享的免费优先、美国合规取页流水线：api-pacer（限速 + 退避）→ fetchlib（waterfall：curl_cffi → Jina Reader → 浏览器 → 付费）→ browser-fetch（浏览器渲染 L3）。默认守 robots + 限速，不破壁 / 不换 IP / 不解 CAPTCHA / 不碰 PII。基准数据、分层机制与完整合规红线见各工具 SKILL.md
• 有 lint 兜底,不只是写完 — 每个技能都带 name + description frontmatter(技能触发面),并有 Skill lint CI 门禁拦住本仓踩过的四类回归:frontmatter 缺失/名不符实、同一技能存在于两个路径(副本会静默分叉)、相对链接失效、以及任何违反抓取红线的写法。本地跑:python3 scripts/lint_skills.py
• 多平台兼容 — Claude Code / Antigravity / OpenClaw / 任何 AI IDE

安装方式

# Claude Code 一键安装
git clone https://github.com/noique/cross-border-ecommerce-skills.git

# 单文件技能 → ~/.claude/commands/
cp cross-border-ecommerce-skills/brand-strategy/*.md ~/.claude/commands/
cp cross-border-ecommerce-skills/amazon/*.md ~/.claude/commands/
cp cross-border-ecommerce-skills/finance/*.md ~/.claude/commands/

# 多文件技能包 → ~/.claude/skills/（每个技能一个目录）
cp -r cross-border-ecommerce-skills/outbound-prospecting/google-whatsapp-prospecting ~/.claude/skills/
cp -r cross-border-ecommerce-skills/outbound-prospecting/linkedin-prospecting ~/.claude/skills/
cp -r cross-border-ecommerce-skills/outbound-prospecting/media-press-discovery ~/.claude/skills/
cp -r cross-border-ecommerce-skills/voc-tools/reddit-voc ~/.claude/skills/
cp -r cross-border-ecommerce-skills/brand-strategy/serp-content-teardown ~/.claude/skills/
cp -r cross-border-ecommerce-skills/tools/backlink-kol-extractor ~/.claude/skills/

模型要求

Changelog

v3.17 (2026-08-01) — three new skills: event marketing, crowdfunding, measurement

Three gaps closed, each found by auditing the library against outside industry material rather than by brainstorming. The recurring lesson: in all three, the most valuable part was not the framework — it was discovering that the source material's core premise had expired. Every fact below was verified against primary sources at build time; none was taken on the source's word.

• New brand-event-marketing — moment-driven marketing: the four event types by the question each answers (brand = who you are / product = what category you own / calendar = who you stand with / trend = where you're headed), a 7-step calendar-moment SOP and a 6-step trend-jacking SOP (four-plane trend read, competitor-axis reset, three value anchors, pyramid content matrix), plus a daily market-radar checklist. What the source lacked and this adds: the ambush-marketing legal gate. Newsjacking a big event is a legal act in the US — Olympic marks sit under the Ted Stevens Act with no consumer-confusion requirement (the USOPC has pursued "Olympian", "Team USA", even "Going for Gold"); FIFA's 2026 protection rests on the Lanham Act plus venue contracts and municipal clean zones rather than special legislation; and NFL enforcement is why every brand says "The Big Game". No rights, no reference — full stop. Also carries the practitioner's own measurement-honesty rule (PR counts page views not the outlet's monthly visits; creators count plays/likes/shares/comments not follower counts) and a no-hardcoded-event-dates freshness rule. Framework credited to its named speaker in the 参考方法论索引; the source's own self-reported campaign results were deliberately excluded — vendor self-reporting does not belong in a skill.
• New crowdfunding-launch — reward-based launch on Kickstarter / Indiegogo as four jobs at once (production capital, demand validation, launch awareness, distribution path), with a four-phase SOP and the post-campaign DTC flywheel. Opens with a delivery gate, because a reward campaign is an FTC-enforceable promise: the Chevalier case (the FTC's first Kickstarter action — $122k raised on a $35k goal, settled with a $111,793.71 judgment) and the iBackPack case ($800k+ across four campaigns on both platforms, nothing shipped) are in the file as the reason the rule is deliver or refund. 🔴 The stale premise caught here: Indiegogo retired Flexible Funding for new campaigns in October 2025 — both platforms are now all-or-nothing, so every "set a low goal and take the money via flexible" playbook (and every pre-2025 case screenshot showing a Flexible Goal) is teaching a route that no longer exists. Verified fee stack: Kickstarter 5% + 3% + $0.20 (small pledges 5% + $0.05, nothing at all if the goal is missed); Indiegogo 5% + ~3% + $0.30, InDemand now Late Pledge (5% native / 8% non-native). Includes a suitability gate that will tell a drop-ship or trading team its product is wrong for crowdfunding — "don't do this" is a designed output.
• New attribution-measurement — the measurement stack the library was missing: attribution, incrementality and MMM answer three different questions and get calibrated against each other rather than chosen between. Covers why summing independently-reported platform conversions always overcounts, geo-test design that starts from the minimum detectable effect that would actually change a budget decision (an under-powered test is worse than no test), open-source MMM selection between Google Meridian (Bayesian, Apache-2.0, now open to everyone, Scenario Planner added February 2026 for no-code budget modelling) and Meta Robyn (frequentist/ML, MIT, still maintained), and Bayesian calibration of MMM priors with incrementality results. 🔴 Two stale premises caught: the cookiepocalypse did not happen — Google reversed third-party-cookie deprecation in July 2024 and confirmed in April 2025 that Chrome would neither run the choice prompt nor deprecate, so any strategy whose立论 is "cookies are going away, therefore X" needs rewriting from the premise up (while not over-correcting: click-level tracking was never complete, which is the actual reason to run incrementality and MMM); and the rule-based attribution models are retiring on a live timeline — GA4 dropped first-click / linear / time-decay / position-based in November 2023, and Google Ads retired the same four in 2026 (unselectable for new conversion actions from mid-July, force-migrated to data-driven attribution by September), which means accounts still sitting on them should choose their migration rather than be migrated.
• Validated against real data, not just linted. Each skill was dry-run on real cases, and each run found a defect that was then fixed: brand-event-marketing had no operational way to grade an event's 势能 (now a table of observable signals — search-trend shape in the target country, whether the platform runs an official window, discussion breadth — with "does my category actually connect to this moment" as a veto, not a bonus); crowdfunding-launch had no way to size a pre-launch email list (now a back-solve from goal ÷ AOV ÷ list-conversion, and 🔴 the repo deliberately ships no default conversion rate — that number varies too much by category and list source for a borrowed one to be anything but precise and wrong). attribution-measurement was run against a real multi-year DTC dataset (~30k orders, six years, GA4 + GSC + keyword data all connected) and correctly refused to produce a channel-ROI ranking: no ad-spend data at all meant two of the three legs could not run, order-source coverage sat at 16-30% every year, and first-touch matched last-touch on all but 6 of 3,964 orders — so the output was "the data cannot answer which channel works" plus the gap list, which is exactly what the honest-shortfall rule exists to produce.
• Total: 58 skills across 13 chains (+3); standalone tools: 7.

v3.16 (2026-08-01) — the honesty-scaffolding sweep: reproducible scoring, honest quotas, evidence gates

Closes the two design questions parked in v3.15, then follows the thread: a gap found in one skill turned out to be a class of gap, so all 62 skills were swept for it. The pattern throughout — the repo's honesty scaffolding existed in its vocabulary but was missing at specific spots, and the spots it was missing were often the highest-consequence ones. No skills added or removed.

• Output quotas are now targets, not pass marks (#16). brand-market-scan, brand-track-hypothesis and social-content-calendar demanded "≥8 rows + ≥3 user quotes per layer", "≥10 features", "exactly 5 strategic clues, each with ≥3 lines of numeric support" — and when the data isn't there, a quota rewards filling the shape, which fights the same file's data-acquisition ledger and 推测值-labeling rules. Every quota is kept, but reframed with an honest shortfall path: state what's missing, why, and what it costs the conclusion; log it in the existing ledger; and — the load-bearing sentence — an honest shortfall counts as completing the step. Also separates 必出的章节 from 必出的结论: always emit the section, never force a conclusion the evidence won't carry.
• New docs/scoring-conventions.md — the mechanical half of reproducible scoring (#15). The repo runs four different scales (/100, 1-5 stars, six dims /30, 100-pt bands); differing scales are fine, but the same input should produce the same ranking twice. Scale-agnostic spec covering anchor-pair normalization with clamping (log for heavy-tailed inputs like BSR, reverse anchors for lower-is-better), missing values, tie-breaking, showing the arithmetic per dimension, and not manufacturing precision the inputs don't have. Wired into all four weighted-scoring skills (amazon-product-selection, amazon-product-shortlist, affiliate-readiness-audit, affiliate-program-ops).
  • The single most important rule: a missing dimension is dropped and the remaining weights renormalized — never zero-filled, never averaged — and the output must carry 本分基于 N/M 维. A 90 from 3 dimensions and a 90 from 5 mean different things, and hiding the count dresses "incomplete data" up as "scores well". Above 40% missing weight, no total is produced at all.
  • Deliberately not done: universal anchor values. "月搜索 >10,000 = 高分" is not the same claim in pet supplies as in consumer electronics. Existing thresholds are preserved verbatim as calibrate-me defaults that the skill must print; inventing universal ones would replace honest vagueness with false precision — the same disease #16 treats, pointed the other way.
• amazon-listing-copywriter — title and Item Highlights now ship as 3 candidate versions (#21). Three strategy-distinct candidates each (volume-first / long-tail-first / scenario-first) with per-version character counts, closing with one recommended version whose rationale must state what traffic it prioritizes, what it gives up, and the condition under which to switch — a bare "use version A" is defined as not completing the step. All three versions are character-counted and compliance-checked, not only the recommended one: operators pick a non-recommended version routinely, and that previously meant shipping a string nothing had validated against the ≤75 limit, the same-word ≤2 rule, or the banned-character set.
• The Item Highlights output section was missing entirely. The field is named in the skill's frontmatter and four times in its rules — "migrate long-tail and scenario words into Item Highlights" — but the report template went straight from title to bullet points. The skill had been telling the model to use a field it never asked it to write, ever since the 2026-07-27 policy created it. Section added and marked non-empty-able, since it is where everything the 75-char title had to drop now lands.
  • Both new quotas follow the #16 convention rather than re-opening it: 3 is a target. The fabrication risk here is not padded table rows but invented product facts — a third title needs a third real angle, and material / size / certification / audience are precisely what a model reaches for to manufacture one, after which those inventions ship on a live listing. Short-delivering with the reason stated (实际 2/3 版，原因：无第二人群词) completes the step, and a 多版本真实性 row was added to the compliance self-check table.
• Lint gained /docs/ and a wider negation vocabulary. docs/ joins the support-file exemption (it holds conventions, not skills), and the deception check's negation list grew (严禁 不得 避免 不披露 等于把) after it false-positived on lines warning against disguising data gaps. Re-tested: genuine 伪装成订单更新 phrasing is still caught.
• amazon-ip-risk-assessment had no rule against inventing a patent number. Found by sweeping the other 13 Amazon skills for the same defect class. It is the one skill in the chain that emits legally consequential identifiers — patent numbers, trademark registration numbers, holders, legal status — that a seller acts on with tooling money; its 数据验证（必做）held four items, all about telling things apart correctly (Assignee vs Security Interest vs Licensee, ® vs ™, Active vs Expired), and none about whether the record exists at all. Meanwhile amazon-keyword-research forbids inventing a competitor keyword and brand-market-scan forbids filling in an ASIN from memory. The lowest-stakes outputs in the repo were guarded and the highest-stakes one was not. Added: a top-of-file 🔴 evidence rule, a search-execution record (platforms reached, exact queries, date, success flag) that precedes the result tables, ✅/⚠️/❌ retrieval status + source + date columns on both the patent and trademark tables, "no hits" stated as an explicitly valid result, and — the load-bearing gate — if the search did not execute, the tables are marked ❌未获取 and the overall risk grade is withheld rather than guessed from category intuition. A fabricated US D123,456 — Active — held by X reads exactly like a real one, and the seller has no way to tell.
• amazon-ad-diagnosis flagged compliant bullets as too long. Its listing-diagnosis table carried 每条 ≤200 字符 as a 健康标准 with 问题信号 "超长 → 精简" — an unsourced number that contradicts the official 2024-08-15 policy of 10–255 characters, which amazon-compliance-review, amazon-listing-copywriter, amazon-keyword-research, amazon-market-research and amazon-pre-launch-review all carry correctly. The diagnosis would have told sellers to cut copy that was never over the limit. Corrected, with the ~1000-character total kept as a labeled soft budget that is explicitly not a diagnostic finding — the same de-mythologizing v3.14 applied everywhere else in the chain.
• Swept and clean across all 14 Amazon skills: section numbering sequential, every /amazon-* cross-reference resolves, the 2026-07-27 dual-field policy reached both gates (compliance-review and pre-launch-review do check Item Highlights — the copywriter gap above was isolated, not systemic), 数据验证（必做）present 14/14, #20's scoring conventions genuinely wired into both scoring skills (anchors printed, 本分基于 N/5 维, >40% circuit breaker), no fabricated ranking-weight percentages, and image specs consistent at ≥1600×1600.
• trustpilot-voc-quick had no "couldn't fetch" path — and a worked numeric example to copy from. Its output template specified rating, review count, star distribution, high-frequency words and a trend call, with nothing tying any of them to the fetch actually succeeding; the fallback section listed what to try (Google cache, ask the user to paste) but never what not to do when all of it fails. Trustpilot throttles hard, so failure is the common path, not the edge case. Worse, the file shipped a fully-worked real example (4.5/5, 269 reviews, 216/21/4/2/26) — a model that couldn't fetch had a plausible numeric template sitting in its own prompt. Added a 🔴 data-acquisition rule binding every figure to the fetch, an explicit ❌未获取 output block, partial-retrieval handling (don't back-infer a distribution from an overall rating), failed brands kept as ❌ rows in comparison tables instead of filled in, and the example rewritten to placeholders with a warning not to transplant any number from it. The sharp edge: this skill feeds brand-market-scan 第零步, the very report #16 hardened with "严禁用记忆或推断补齐…评论原文" — the consumer was guarded and its producer was not.
• media-press-discovery handed over pattern-guessed emails for real, named journalists with no send gate. guess_emails.py guesses [email protected] from a name and a domain; the schema even carried an email_verified column — but the quick-start ran the guesser without the --verify flag the script already supports, and nothing anywhere said don't send to unverified. A wrong guess bounces (degrading your sending domain), reaches a different real person at that outlet, or hits a spam trap. Sibling skills already knew this — linkedin-prospecting says pattern-guess → verify with NeverBounce/ZeroBounce, and contact-extractor ships confidence tiering plus --verify. Fixed: --verify promoted into both quick-start flows with its real ~60% SMTP-probe accuracy stated, email_verified documented as a hard gate rather than a note (smtp_fail = unproven, not disproven → manual queue, don't drop the journalist), a ban on reconstructing any journalist name / byline / URL / date from memory, and the CAN-SPAM / GDPR identify-yourself line the repo already enforces elsewhere.
• reddit-voc's Top 5 quotas never got the #16 treatment. "核心痛点 Top 5（带原话引用）" and "竞品翻车点 Top 5" are hard quotas in a skill whose entire value is verbatim user language — and Reddit now rate-limits and 403s routinely, so shortfall is normal. The skill's existing "永远抄用户原话，别自己改写" guards against paraphrase, not against invention, and quoting presupposes having something to quote. Added the standard shortfall path (3 real pain points → write 3 and label 实际 3/5 + reason), a ban on fabricating quotes, usernames, permalinks or upvote counts, an outright ❌未获取 report when scraping fails, and the 必出的章节 / 必出的结论 split. Also stopped the ≥2-community cross-check from becoming its own quota: single-community insights get labeled, not padded out with a lookalike.
• Swept and clean across the other 45: finance/ (8) uses a different but real convention — source table + confidence + timestamp + "须核实一手源" + disclaimer, with finance-tax-nexus-vat-diagnostic citing Council Reg (EU) 2026/382, HMRC and Wayfair and flagging its own 2026 moving targets · trustpilot-voc-deep carries a sample-size + credibility table and runs a real local scraper · linkedin-prospecting and contact-extractor already gate on verification · serp-content-teardown is deterministic by design (no LLM) · tools/ (7) are deterministic scripts · report-pdf-export and brand-chart-visualize emit no facts. Structure clean repo-wide: section numbering, cross-references and count claims all check out.
• Total: 55 skills across 13 chains (unchanged); standalone tools: 7.

v3.15 (2026-07-26) — repo hygiene: dedupe, red-line fix, frontmatter, CI

No skills added or removed; this release fixes structural debt found in an external audit, with each claim verified against the tree first.

• Deleted 14 stale duplicate skills (−2,704 lines). Every Amazon skill existed twice — amazon/<name>.md and amazon/<category>/<name>.md — and all 14 pairs had diverged. The root copies are canonical (README links only to them, and they carry two sections the subdirectory copies had lost); the subdirectory copies were referenced nowhere. Left in place, an agent grepping the repo could hit the older, capability-poorer version.
• Closed a cross-file compliance conflict. google-whatsapp-prospecting advised rotating IPs for high-volume search and rotating numbers past a ban — contradicting the repo-wide scraping red line in tools/fetchlib / tools/browser-fetch (no access-barrier defeat, no IP rotation, no CAPTCHA solving) and the skill's own rule to move to the WhatsApp Business API above 50 msgs/day. Both now point at the sanctioned path, with the instruction to cut the query set rather than the compliance.
• Frontmatter on all 53 remaining skills. name + description now present repo-wide, matching the convention the multi-file packages already used. Descriptions were authored from each file's actual contents — what it produces, when to reach for it, bilingual EN/中文 triggers, sibling cross-refs — with explicit disambiguation between near neighbours (amazon-ad-architecture vs -diagnosis vs weekly-ad-review; trustpilot-voc-quick vs -deep; dsite-seo-diagnostic vs -playbook). Support files (references/ templates/ scripts/ examples/) are deliberately exempt — they are not skills.
• New Skill lint CI gate (scripts/lint_skills.py, stdlib-only) blocking all four regressions above: frontmatter presence + name-matches-filename, duplicate skill names across paths, dead relative .md links, and scraping-red-line violations (negation-aware, so the red-line statements themselves pass). Each check was negative-tested against a seeded fault.
• Removed a disguised-consent instruction and two folklore ban thresholds. google-whatsapp-prospecting's reference doc told readers to send a "transactional-looking" opt-in request to obtain consent — deceptive on its own terms, what Meta rejects templates for, and an FTC Act §5 exposure for a US operator; and it stated 3+ blocks → warning / 5+ block + 2+ report → 24h ban as fact, which Meta does not publish. Both rewritten (identify yourself and ask plainly; enforcement described qualitatively). The lint gained two checks — deception and unpublished-threshold folklore — because none of the original four would have caught either.
• Skill count corrected 54 → 55. No skill was added: report-pdf-export had always existed and is chained by 37 other skills, but it was never listed in the skill map. It now appears in the Brand Strategy chain.
• Open design questions parked as issues rather than guessed at: #15 making the weighted scoring models reproducible (normalization / missing values / ties) and #16 the tension between output quotas and honest "insufficient evidence". Input welcome on both.
• Total: 55 skills across 13 chains (count corrected, none added); standalone tools: 7.

v3.14 (2026-07-12)

• Amazon Operations Chain (14 skills) — 2026 algorithm & policy refresh. Every fact was re-verified against first-party sources (Seller Central announcements, Amazon Ads what's new, the COSMO SIGMOD 2024 paper, Amazon Q4'25 / Q1'26 earnings) and corrected. No skills added; the flows are unchanged. Highlights:
  • Title char limit rewritten — hard-coded 200 / 125 / 80 replaced with the 2026-07-27 policy: ≤75 chars for non-media categories + the new searchable Item Highlights field (125 chars). Over-limit titles get AI-rewritten (brand owners get a 14-day Review-Listing-Changes window, others don't); the 2025-01 special-char ban + no-word-repeat rules are kept as still-stacking.
  • Rufus → "Alexa for Shopping" — renamed across the chain (Rufus brand retired 2026-05-13, folded into the main search bar; recommendation engine carried over). The mis-stated "300M active users / $120亿" is corrected to Amazon's actual cumulative-users / self-attributed annualized-incremental wording, with a "no seller-side AI attribution → don't cite % lift claims" honesty guardrail.
  • Removed a live TOS-violating tactic — the "competitor-brand back-end Search Terms" instruction (violates G23501, risks ASIN suppression) is now a prohibited-items check. (SP ad targeting of competitor terms stays — that's compliant.)
  • De-mythologized unverifiable claims — "bullets: only first 1000 bytes indexed" (no official basis; Amalytix self-test, and in characters not bytes) and COSMO "~10% of US search traffic" (a 2024 A/B-test bucket, not coverage) downgraded to labeled third-party / snapshot facts; fabricated ranking-weight percentages ("title first-80-chars = 60% weight") removed.
  • Premium A+ — kept the real (unchanged) eligibility per Amazon's 2026-05 forum-mod confirmation and added the real new Content Quality Analysis beta; explicitly flags the "$5M threshold removed / open to all / A+ Quality Score" rumors as false.
  • New 2026 realities added — SP/SB Prompts ads (auto-enrolled, CPC-billed since 2026-03-25), free self-serve AMC (since 2025-09, + a 2026-06→12 free-signals window), FBA commingling end + mandatory FNSKU (2026-03-31) & paid-prep/labeling sunset (2026-01-01), variant-review split (2026-02-12, which shifts every "review-count" threshold), agentic-shopping / AI-agent-readiness checks, a corrected image policy (G1881, no AI-specific ban) and an external-AI / robots.txt reality section.
  • Housekeeping — corrected tool references (TESS → USPTO Trademark Search; APEX scope), runtime-verify guards for referral / FBA fees, graceful fallback for the AntV chart endpoint, and removed internal build-file names from the design-inspiration notes.
• Total: 54 skills across 13 chains (unchanged); standalone tools: 7.

v3.13 (2026-07-04)

• New chain — Affiliate & Partnership (2 skills), built from three 2026 industry decks (each skill credits its source in a 参考方法论索引 table, per repo convention — framework skeletons only, no slide copy):
  • affiliate-readiness-audit — the mirror of dsite-conversion-ux (CRO): audits a product/Listing from the affiliate's wallet POV ("why affiliates won't promote you"). Affiliate ROI ledger + EPC + 3-layer arbitrage, 6-dim recruitability scorecard with score bands, admission buckets, 6 silent-rejection red flags, traffic-catch self-check + 30-day fix roadmap.
  • affiliate-program-ops — AI-automated affiliate operations pipeline: database → find candidates across 6 affiliate ecosystems → 100-pt AI scoring → standardized contact collection (10 sources) → AI personalized outreach (5-element email) → Partner Resource Hub activation → 4-layer KPI + weekly report → 30-day minimum loop. Multi-agent fan-out per ecosystem; reuses outbound-prospecting. Complements influencer-marketing (content/relationship lens) without overlap.
• New chain — Channel & Market Entry (2 skills):
  • eu-channel-market-entry — channel/distributor-driven Europe entry for US-entity brands: US-vs-EU market logic, positioning quadrant, SSPV user-value model, RCS regional-combat model across the 5 EU regions, channel enablement, overseas-org 3 stages, the 8-step growth flywheel. Org/tax hand off to finance-*. Reframed China-seller → US-entity.
  • price-system-conflict-guard — multi-channel price-system & channel-conflict guard (DTC + Amazon + distribution): cross-channel real-price diagnosis, 3-layer mechanism (unified price × promo-sync × regional protection), DTC-vs-channel boundary, T-60 promo SOP, US antitrust (MAP vs RPM) boundary.
• All four are Claude Code multi-agent-ready (Workflow fan-out + StructuredOutput schemas), US-jurisdiction framing, brand-neutral examples.
• Total: 54 skills across 13 chains; standalone tools: 7 (unchanged).

v3.12 (2026-06-29)

• New tools/browser-fetch/ — an optional shared browser-render backend for fetchlib's L3, promoting the "real browser + optional proxy" capability (the kind in tools/trustpilot) into one reusable module (pluggable engine, default Selenium; register_engine() for nodriver / camoufox / scrapling). Drop-in: fetchlib.register_backend("browser", browser_fetch.as_fetchlib_backend(engine="selenium")).
  • Benchmark-driven, honest scope: real testing (Selenium + nodriver, from a datacenter IP) showed a browser clears Trustpilot-class sites curl_cffi can't render (Selenium got real content), but no free tool — Selenium, nodriver, curl_cffi, Jina — beat Cloudflare Managed Challenge / DataDome (Muckrack, G2, cf-challenge) from a datacenter IP. The bottleneck there is IP reputation + Turnstile, not the browser library → those need a residential IP (+ maybe Camoufox/Scrapling's Turnstile solver) or a paid unblocker. So Selenium is the proven default; other engines are opt-in "validate on a residential IP first."
  • Normalizes a detected challenge page (incl. the Chinese Cloudflare interstitial "请稍候") to 403 so fetchlib flags it; selenium is a lazy import; self-test is browser-free. Also hardened fetchlib's block-detector with the Chinese + a few more challenge markers.
  • Compliance (US): renders JS + optional proxy, but does NOT solve CAPTCHAs, defeat access barriers, use botnet proxies, or handle PII (caller's call under CFAA / hiQ / CCPA-CPRA). tools/trustpilot's working Selenium is left untouched.
• Total: 50 skills across 11 chains; standalone tools: 7 (was 6).

v3.11 (2026-06-29)

• fetchlib batch 2 + serp-content-teardown migration — after a real benchmark of the free tiers against representative targets (Shopify products.json, Trustpilot, a Cloudflare JS-challenge, Muckrack, G2, from a datacenter IP):
  • serp-content-teardown/fetch_competitors.py now uses curl_cffi (real-browser TLS/JA3, free, local) with a graceful fallback to plain curl if it isn't installed — non-breaking, same fetch_manifest.json / outputs. The skill's "free + local, no paid APIs" red line is preserved (curl_cffi is a free local libcurl, not an API); no headless browser is added (it stays a deterministic offline analyzer).
  • fetchlib gains a ThompsonSelector (per-domain Beta-Bernoulli bandit, Fetcher(learn=True, selector_path=…)) that learns which tier actually succeeds per site and tries the best first — because the benchmark showed the "best backend" is site- and IP-dependent, not fixed (e.g. curl_cffi matched plain curl and 403'd on Trustpilot/Cloudflare/Muckrack/G2 from a datacenter IP, while free Jina Reader cleared Trustpilot; the hard JS-challenge sites need nodriver + a residential IP or a paid unblocker). Self-test covers the selector.
  • Honest scope: nodriver (L3) stays a documented register_backend plug-in — deferred until tested locally on a residential IP (it won't clear those sites from a datacenter IP either). tools/trustpilot's working Selenium is left untouched per its owner.
• No new skills/tools; counts unchanged (50 skills / 11 chains / 6 tools).

v3.10 (2026-06-29)

• New tools/fetchlib/ — a compliant fetch waterfall for the scraping skills, built from the 2026 dual-source scraping-stack research (multi-agent web research + a Gemini Deep Research report). Escalates one tier only on a real block (Markov-style state machine): L1 curl_cffi (TLS/JA3 impersonation, free, no JS) → L2 Jina Reader (r.jina.ai JS→clean-markdown, free*) → L3 nodriver (batch-2 register_backend plug-in) → L4 managed unblocker (paid, opt-in).
  • Control layer: api-pacer (header-adaptive pacing + full-jitter backoff) + AIMD (additive-increase / multiplicative-decrease per-domain rate — creep up, cut hard on block) + circuit breaker (cool down a target that keeps blocking) + per-fetch JSONL instrumentation.
  • Grounded conclusions: curl_cffi is the free TLS-impersonation workhorse; cloudscraper / FlareSolverr / undetected-chromedriver / puppeteer-stealth are dead/declining vs 2026 WAFs and are NOT used; "human-like" delay distributions (Gaussian etc.) are cargo-cult for read-only research — header-driven pacing + full-jitter backoff is what matters.
  • Compliance-first / red line: honors robots.txt; does NOT defeat access barriers, rotate IPs, forge fingerprints, solve CAPTCHAs, or handle PII (caller's duty); use only KYC/consent-audited proxies if any; legitimate rate-limit-respecting RESEARCH use only, NOT ToS-violating automation. Governing law is US (CFAA + state) with the hiQ / Meta-v-Bright-Data public-data safe harbor (public + logged-off + no-barrier-defeat + no-PII); GDPR / CCPA-CPRA apply only when collecting EU / California residents' personal data.
  • Stdlib-runnable (curl_cffi optional with graceful urllib fallback); depends on api-pacer. Adoption is opt-in for serp-content-teardown / media-press-discovery / trustpilot / outbound-prospecting / reddit-voc (their working scrapers unchanged). Batch 2 will add the nodriver backend + a Thompson-sampling backend selector.
• Total: 50 skills across 11 chains; standalone tools: 6 (was 5).

v3.9 (2026-06-29)

• New tools/api-pacer/ — a shared, dependency-light request pacer (adaptive rate-limiting + AWS-style full-jitter backoff) for the scraping / API skills. Paces to the server's own x-ratelimit-* headers when present (else a configured RPS budget); full-jitter (uniform) backoff on 429/503/Retry-After; stdlib-only, requests optional.
  • Why it exists — reads the REAL rate-limit budget instead of guessing a delay from a distribution. A Gaussian/uniform "human-like" sleep is not what avoids rate limits/blocks (platforms don't fit-test your delay distribution); the winners are (a) obeying the response-header budget and (b) full-jitter backoff for retries.
  • Adoption — reddit-voc gets an integration note (voc-tools/reddit-voc/references/rate-limiting.md: PRAW + pacer, read-only research framing); serp-content-teardown / media-press-discovery / trustpilot / outbound-prospecting are documented as opt-in drop-ins (their working scrapers are left unchanged).
  • Scope / red line — legitimate rate-limit-respecting research use only; NOT for ToS-violating automation (vote manipulation, spam, sockpuppets, ban evasion). It does not defeat anti-bot, rotate IPs, or forge fingerprints.
• Total: 50 skills across 11 chains; standalone tools: 5 (was 4).

v3.8 (2026-06-29)

• New finance/ chain — 8 skills (Finance & Capital) for China→US/EU DTC + Amazon sellers: finance-landed-cost-unit-economics (CM1/CM2/CM3 + break-even ROAS), finance-fx-payout-optimizer (收款/结汇/FX hedging + SAFE checklist), finance-tax-nexus-vat-diagnostic (registration-obligation map + EU €3 duty / IOSS / US nexus / EPR), finance-cashflow-runway-forecaster (13-week rolling forecast), finance-pricing-margin-guard, finance-reconciliation-bookkeeping (clearing-account-to-zero), finance-entity-structure-advisor (HK/SG/US LLC + repatriation, YMYL), finance-capital-stack-advisor (RBF/MCA true-APR + contract traps).
  • Dual-source, cross-validated — built from a multi-agent web-research pass + an independent deep-research report, then merged. Example correction surfaced by the merge: the EU low-value-parcel change is a fixed €3 customs duty (Council Reg (EU) 2026/382, applies 2026-07-01, interim to 2028) — DISTINCT from the not-yet-law ~€2 per-consignment handling fee (UCC reform, only provisionally agreed 2026-03-26).
  • YMYL-first — every finance skill carries an explicit "operator planning aid, NOT professional tax/legal/accounting/financial advice — verify with a licensed CPA before filing/binding decisions" disclaimer; all rates/thresholds/dates tagged point-in-time; estimates flagged ⚠️ hypotheses; no fabricated seller numbers.
  • Complements brand-budget-ops (forward budgeting) with the actuals / profitability / tax / cash / financing side; cross-links into Amazon ops + DTC chains.
• Total: 50 skills across 11 chains (was 42 / 10). Single-file skills: 45 (was 37).

v3.7 (2026-06-29)

• brand-strategy/dsite-conversion-ux upgraded — the CRO skill is rebuilt as a multi-agent, Claude Code-native live-site audit (was a single-pass content audit):
  • Multi-agent concurrency execution architecture — a new section documents running the skill on Claude Code's Workflow tool / subagents: Step 0 fans out 5 parallel recon agents (PDP / discovery / trust / checkout / competitor) with StructuredOutput schemas → reconPool → parallel 6-module analysis; background tasks; browser-class MCP (Claude in Chrome / Preview) for what static WebFetch can't see (console / pixel firing / sticky / mobile). Concurrency speeds it up and removes blind spots without changing data truth.
  • New Step 0 — live-site reconnaissance — turns the skill from "wait for user-supplied data" into "actually go look first," pinning page-level facts with verbatim evidence before diagnosis.
  • New §1.6 technical-health & tracking-integrity check — console errors / GA4-Pixel firing / Core Web Vitals / 404 / mobile-sticky. Broken tracking silently invalidates all downstream A/B + funnel data, so it ranks as a foundation-layer, highest-priority finding (a layer most content-only CRO audits miss).
  • New mandatory deliverables — a copy-rewrite table (current → A/B), an objection-handling table (doubt → current handling → gap → fix), and a quick-win vs high-impact dual-bucket triage with wall-clock effort estimates.
  • A/B stop-rule discipline — every test now carries sample-size + ≥7-14 days + 95% significance + a stop rule.
  • Honesty preserved — keeps the data-credibility statement; lift figures stay ⚠️ hypotheses (no fabricated funnel numbers); adds explicit WebFetch tool-boundary disclosure.
• Skill count unchanged at 42 (upgrade to an existing skill, not a new one).

v3.6 (2026-05-26)

• New brand-strategy/serp-content-teardown/ multi-file skill (DTC Site & Traffic chain) — deterministic (no-LLM) SERP/content reverse-engineering from local Semrush xlsx + competitor HTML. 8 scripts (parse_serp → fetch_competitors → analyze_structure → classify_archetypes → keyword_analysis → backlink_analysis → geo_analysis → onpage_analysis) + run_all orchestrator + shared _config (YAML topic-clusters / JSON brand-names).
  • Structure teardown: parses Semrush serp_urls → ranked blog/info-article URL pool, fetches the top competitor articles (curl + browser UA, html5lib to survive Shopify void-tag body-nesting), computes per-article metrics (words, H1/H2/H3, lists, tables, JSON-LD @type set, author byline, dates, brand-mentions/1k, authority outlinks), classifies each into 8 article archetypes (DEFINITION_QA / TUTORIAL_HOWTO / LISTICLE_TIPS / COMPARISON_VS / PILLAR_GUIDE / MYTH_DEBUNK / PRODUCT_MICROGUIDE / NEWS_EDITORIAL) + opening/closing patterns + cross-sample winning bands.
  • SEO/GEO/keyword/backlink layers: keyword distribution + core keywords + difficulty-cliff (from broad-match); backlink/authority thresholds (Page AS / Ref.Domains / Backlinks) + "weak-link winners"; AI-Overview (GEO) saturation per topic + AI-cited domains + schema readiness of cited vs non-cited; on-page SEO (title/meta/H1/canonical/internal-links/SERP-features).
  • Output: JSON artifacts + a per-topic content-strategy report (which archetype / word / H2 / schema / FAQ / opening-closing to use, which keywords to target, what authority is realistically needed, what GEO posture to take). Worked example under examples/: waterproof / stainless-steel jewelry niche (25 competitor articles).
  • Honest scope: covers the ~20-30% code-side of what wins; content quality + domain age + backlinks are the other ~70-80%. Backlink data is page-level Authority Score, not domain DR. AI-Overview citation capture is sparse. Red line: curl-only fetch, no paid APIs, no live AI-citation probing. Pairs with backlink-kol-extractor (links) and structured-data-buildout (implements the schema this skill measures).
• Total: 42 skills across 10 chains. Multi-file packages: 5 (was 4).

v3.5 (2026-05-19)

• New voc-tools/reddit-voc/ multi-file skill — pre-purchase VOC mining from Reddit (complements post-purchase trustpilot-voc-*). Methodology:
  • 4-dimension community discovery — category/brand subs (D1) + lifestyle/demographic subs (D2) + problem/help subs (D3) + values/ideology subs (D4). Single-dimension findings are not credible; cross-dimension validation required.
  • 6-class post taxonomy with business-action mapping — Recommendation / Rant / Question / Comparison / Daily / Top-All-Time. Each class maps to a primary output (Listing copy / differentiation positioning / FAQ + IPQ / vs-matrix / content calendar / brand positioning) and a secondary output.
  • Reddit slang dictionary — BIFL / YMMV / AITA / DAE / TIL / PSA / etc., grouped by VOC signal type (values / emotion intensity / moral-judgement / product-evaluation / community-platform / category-specific). Reading slang wrong = misreading user emotion and values.
  • 3 listing+ad rewrite cases — "7 pieces to clean" listing rewrite, "earbuds don't fall out" TikTok hook, "never scoop never clean" brand tagline. All sourced from real Reddit high-upvote threads, demonstrating user-language > marketer-language.
  • 2D positioning matrix (functional importance × satisfaction) — find the bottom-right "wants but unmet" quadrant. Includes simplified 30-min version and full 2-3-day data-driven version. Compatible with tools/trustpilot/ topic_modeling.py + sentiment.py for auto-labeling at scale.
  • 3 CSV templates: community-map.csv (8-15 subs across dimensions) / post-analysis.csv (30-80 post teardowns) / insight-action-map.csv (insight → action with P0/P1/P2 priority + owner + status).
  • Pairs with: /trustpilot-voc-deep (post-purchase), /amazon-market-research (post-purchase), /brand-market-scan (pre-strategy VOC input), /amazon-listing-copywriter (user-language input), /tiktok-growth (ad-hook input).
• Total: 41 skills across 10 chains. Multi-file packages: 4 (was 3).

v3.4 (2026-05-06)

• New outbound-prospecting/media-press-discovery/ multi-file skill — Muckrack-anchored journalist DB pipeline. 5 scripts (discover_journalists / find_articles / guess_emails / score_and_export / merge_partitions) + shared _fetcher.py with 4 backends (requests / remote-chrome / apify / html-dir) for Cloudflare-protected pages. Multi-machine partition-merge workflow.
• New tools/linktree-expander/ — batch-enrich Linktree handles via __NEXT_DATA__ JSON parsing. Handle-match scoring for personal_site + NON_PERSONAL_HOSTS blocklist (30+ shorteners / aggregators / docs / scheduling tools). Verified 44/45 ok on a 45-handle pilot.
• New tools/contact-extractor/ — multi-source email extraction with confidence tiering. Sources: personal_site /about /contact /press (mailto / text) + YouTube Data API v3 description + Apple Podcasts RSS owner + email pattern guess. Optional --verify SMTP MX probe / Hunter.io. Outputs ranked contact_email_1..3 + confidence (high / medium / low / none).
• tools/trustpilot/ rebuilt: modern data-* attribute selectors (replaces 110-line sibling-XPath fallback chain that broke on Trustpilot's 2026 DOM update), desktop-UA pin (Trustpilot serves snippet-only DOM to mobile UAs — major silent failure mode), ?sort=recency URL flag (relevance widget served snippet-only DOM for some brands), --cutoff_date YYYY-MM-DD arg (efficient time-bounded scrapes), --skip_ai mode bypasses broken generate_report() signature, redacted hardcoded SOCKS5 proxy creds in config.py (now env-var loaded).
• Total: 40 skills across 10 chains. Tools count: 4 (was 1).

v3.3 (2026-04-27)

• New entry-orchestrator skill dsite-seo-diagnostic (single-file, under brand-strategy/) — for live-site SEO traffic-drop diagnostics + restart roadmap.
  • 7-dimension diagnostic framework: traffic curve (algorithm-downgrade signature) / keyword loss structure / single-point-failure risk / multilingual-Markets URL pollution / backlink quality (DS distribution + anchor text pathology) / content ROI (HCU-hit detection) / KPI body audit (process-volume vs outcome).
  • Algorithm-event alignment: traffic curve auto-aligned with public Google Core / HCU / Spam Update timeline (rolling 24-month window).
  • Restart roadmap: 4-phase plan (stop-the-bleeding → cleanse → content rebuild → off-site signals) anchored on the dsite-seo-playbook 6-step framework.
  • Multi-skill orchestration: chains xlsx → dsite-seo-playbook → trustpilot-voc-deep / competitors-analysis / backlink-kol-extractor → report-pdf-export. Hides internal SKILL refs in deliverable.
  • Standard PDF deliverable (A4 landscape, deep-blue header, zebra rows, page-numbered) via report-pdf-export.
  • Use cases: traffic anomaly investigation, pre-vendor-replacement independent diagnostics, annual SEO health check, post-Core-Update impact assessment, post-migration audit, multilingual / Shopify Markets pollution audit.
• Total: 39 skills across 10 chains.

v3.2 (2026-04-26)

• New Outbound Prospecting chain (2 multi-file skill packages) under outbound-prospecting/:
  • google-whatsapp-prospecting — Google-dork → WhatsApp lead pipeline. 4-stage workflow (Search → Validate → Enrich → Outreach). 15+ search formula variants, 30+ countries with mobile-prefix narrowing + B2B platforms + time-zone send windows + compliance flags. Full GDPR / CASL / CCPA / UWG §7 / WhatsApp ToS compliance reference. Multi-language outreach playbook (EN/ES/PT/FR/AR). SerpAPI batch script + wa.me validator + lead-tracker CSV.
  • linkedin-prospecting — Google + Bing + Yandex + Wayback reverse-search of LinkedIn. Post-2024 Auth Wall workarounds. 50+ decision-maker role keywords localized into 8 languages with company-size-band gating (CEO targeting only valid for ≤500 employees). Enrichment-tool comparison (Apollo / Snov / Hunter / Lusha / Wiza) with cost + accuracy + legal context. LinkedIn ToS § 8.2 + 2026 quota table + account warm-up protocol. 4-touch outreach (Connection Request → DM → Follow-up → Channel-switch) with 12 DM templates across 5 archetypes (incl. voice-note opener), reply-handling matrix, native templates in PT/ES/FR/DE/AR/JA.
• New format: multi-file skill packages (SKILL.md + references/ + templates/ with Python helpers + CSV trackers), in addition to existing single-file .md skills.
• Total: 38 skills across 10 chains.

v3.1 (2026-04-14)

• New VOC chain (2 skills): /trustpilot-voc-quick (5-min surface scan) and /trustpilot-voc-deep (full pipeline with sentiment analysis, LDA topic modeling, and AI-powered insights)
• New tools directory: tools/trustpilot/ with Python toolkit for deep review analysis
• Total: 36 skills across 9 chains

v3.0 (2026-04-13)

• 12 new skills: annual-plan, budget-ops, gtm-launch, seo-playbook, sem-ads, conversion-ux, tiktok-growth, youtube-ops, influencer-marketing, user-lifecycle-ops, social-content-calendar, offline-retail-us
• Chart visualization: 21 skills now auto-generate charts via AntV API (radar, bar, column, pie, waterfall, scatter, line, funnel, sankey)
• Unified footers: All 36 skills now include GitHub open source link
• Total: 34 skills across 8 chains

v2.1 (2026-04-12)

• Added /brand-ab-compare SKILL
• Semrush local data auto-scan in Step 0
• Data gap reporting, minimum depth constraints
• SEO traffic model validation in Round 3
• Auto knowledge base trigger in IMC

v2.0 (2026-04-11)

• Real-time data collection layer added to 3 brand strategy SKILLs

v1.0 (2026-04-07)

• Initial release: 6 brand strategy + 14 Amazon skills (20 total)

License

Licensed under CC BY-NC 4.0.

Free to use, share, and adapt with credit.

Author

Created by Alex / 黄子阳 — WhaleBridge / 远鲸引力

• 官网 / Website — whalebridge.us
• 介绍 PPT / Deck — ckcm.us

Contact / 联系方式

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