You: investigate the trade-offs between Postgres logical replication and CDC tooling

Claude:  ✓ Reframed your question (3 hypotheses)
         ✓ Picked genre: decision (comparison + validation)
         ✓ Wrote plan.md (17 sections)
         ✓ Checked your env: 4 APIs available, 2 fallback to HTML
         ✓ Launched 4 sub-agents across 12 channels
         ✓ Saved 23 sources to sources/ with quotes
         ✓ Ran adversarial pass (3 counter-arguments)
         ✓ Report ready: research/postgres-replication-vs-cdc/2026-05-21_decision.md

What this is

A Claude Code skill that turns "research this topic" into a 11-phase pipeline with hypothesis testing, parallel sub-agent search, source triangulation, and adversarial review.

The output is a folder you can return to in a month. Every claim traces to a specific source file. The plan documents why you made every choice. No re-research needed.

New here? Start with the Quickstart — install → invoke → first result in ~5 min.

Install in 30 seconds

git clone https://github.com/Socialpranker/deepdive.git \
  ~/.claude/skills/deepdive

That's it. Now type any of these in a Claude Code session:

• "Investigate X"
• "Изучи тему"
• "Validate this hypothesis"

# Clone
git clone https://github.com/Socialpranker/deepdive.git
cd deepdive

# Package as .skill bundle
zip -r ../deepdive.skill . -x ".*" -x "*.zip"

# Upload via Claude.app → Settings → Skills → Add Skill

The 11-phase methodology is portable. Load SKILL.md + references/*.md into the LLM's context manually. Skip the sub-agent parts and use separate chat sessions per subtopic.

Full instructions →

How it works

The skill runs 11 phases in order:

| 1 | Reframing | opus / high |
| 2 | Genre & block selection | sonnet / medium |
| 3 | Plan | opus / medium |
| 3.5 | Capability Discovery | sonnet / low |
| 3.7 | Plan-review gate | sonnet / low |
| 4 | Search | sonnet / medium |
| 5 | Claims-ledger + triangulation | haiku / low |
| 5.5 | Evidence filter | sonnet / low |
| 6 | Synthesis + multi-angle red team | opus / high |
| 6.5 | Verify | haiku / low |
| 7 | Refresh targets | sonnet / medium |

Each phase runs on a model matched to its task — Opus where reasoning multiplies (1/3/6), Haiku for the parallel fan-out (4). The skill announces the routing and an estimated cost up front, once.

Every phase is transparent: you see what's happening, you confirm key decisions, and you get a folder you can return to. Want to compare models head-to-head? The eval harness scores any run on 6 axes.

What's inside

Example folder

Sample output for a typical decision-genre research:

research/<topic-slug>/
├── plan.md                              # 17-section plan
├── sources.csv                          # Index with C/R/B scoring
├── sources/                             # One file per source
│   ├── 01_vendor-docs.md                # Primary, total=14
│   ├── 02_benchmark-paper.md            # Academic, total=12
│   ├── 03_industry-report.md            # Industry, total=13
│   ├── 04_forum-thread.md               # Forum, total=9 (opposition)
│   └── ... (19 more)
├── findings/
│   ├── F1_<atomic-thesis>.md            # confidence: high
│   └── F2_<atomic-thesis>.md            # confidence: medium
└── 2026-05-21_decision.md               # Final report

Final report structure (assembled from the blocks chosen in plan.md):

## TL;DR
- Claim A holds under condition X [confidence: high]
- Claim B holds conditionally on threshold Y [confidence: medium]
- Claim C is disputed by opposition sources [confidence: low]

## Mental model
[How the underlying mechanism works...]

## Falsification criteria
What would disprove H1, H2, H3...

## Verdict conditional
Recommendation IF: <conditions met>
Different recommendation OTHERWISE: <conditions broken>

## Counter-arguments (steel-man)
CA1: "<the strongest opposing claim>" [source: s09]
     → Our answer: <conditions under which CA1 fails>
CA2: ...

Every claim is clickable to its source. A month later, you don't re-research — you read.

Contribute

The catalog is most valuable when it grows. Easy contributions:

Full contributing guide →

FAQ

Those are products — closed UI, fixed flow, opaque source selection. This is open methodology — you control every step, the protocol is markdown you can fork, the source catalog is yours to extend.

They also don't separate sources into files, don't do explicit triangulation, don't run adversarial passes, and don't produce reusable atomic theses.

Yes — on Claude Desktop with Skills enabled. Also works manually with any LLM by loading the markdown files into context (see "Use with other LLMs" below).

See the example folder above. TL;DR: a folder with plan.md + sources/NN.md per source + findings/FN.md atomic theses + final <date>_<genre>.md report.

Every claim in the final report links to a specific sources/NN.md file.

For a 5-minute "what's the latest X" question — yes. That's why shallow mode exists (5-7 sources, no sub-agents, ~15 min). The full machinery is for medium (1 hour) and deep (3 hours) when you need to actually use the output for a decision.

The file-per-source structure is the key reuse mechanism. A single research often informs 3-5 future researches because you can cite individual sources/NN.md directly.

It's structured methodology + curated catalog + reusable templates + automation.

• The 11-phase workflow forces discipline
• 460+ stat sources catalog is curated knowledge
• 105 reusable blocks compose any report shape
• Weekly auto-validation keeps the catalog alive
• 25+ upstream awesome-lists give infinite discovery layer

Prompts are an implementation detail, not the value.

Yes — MIT licensed. Use it, modify it, integrate it into products. Attribution appreciated but not required.

Use with other LLMs (Codex, Gemini, etc.)

The methodology is portable. ~70% of content is LLM-agnostic markdown templates.

To adapt:

1. Load SKILL.md + relevant references/*.md into the LLM's context
2. Replace sub-agent parallelism with separate chat sessions per subtopic
3. Manage source files (sources/NN.md) externally — LLM produces content
4. PRs welcome for codex/, gemini/, local/ adapters

На русском

Deepdive — скилл для Claude Code, превращающий «загугли это» в дисциплинированный 11-фазный процесс.

Что внутри

• 11 фаз workflow: Reframing → Genre & block selection → Plan → Capability Discovery → Plan-review gate → Поиск → Claims-ledger + триангуляция → Evidence-фильтр → Синтез + multi-angle red team → Verify → Refresh targets
• 6 жанров отчёта: qa / explainer / decision / landscape / validation / custom
• 105 блоков в 10 категориях — переиспользуемые секции с шаблонами и анти-паттернами
• 29 каналов поиска с paywall fallback протоколом (включая api-direct)
• 460+ статистических источников в 14 cross-industry + 19 отраслевых категориях
• 39+ API endpoints для programmatic доступа (free no-auth приоритетны)
• plan.md с 17 секциями для прозрачности
• Multi-angle red team из враждебных ролей (Skeptic/Contrarian/Gap-hunter) с триажем severity (обязателен для medium/deep)
• Weekly auto-validation через GitHub Actions

Установка

git clone https://github.com/Socialpranker/deepdive.git ~/.claude/skills/deepdive

Триггеры: «проведи ресёрч», «изучи тему», «копни глубоко», «deep dive»

Вклад

Каталог растёт через PRs. Самые ценные — новые источники в stat_sources/ и api_sources/. См. CONTRIBUTING.md.