openclaw-coding-kit
Health Pass
- License — License: MIT
- Description — Repository has a description
- Active repo — Last push 0 days ago
- Community trust — 17 GitHub stars
Code Warn
- process.env — Environment variable access in docs/interaction-board/eggturtle-admin-route-first/scenarios/prod-admin-admin-dashboard-analytics-activity.spec.ts
- process.env — Environment variable access in docs/interaction-board/eggturtle-admin-route-first/scenarios/prod-admin-admin-dashboard-analytics-revenue.spec.ts
- process.env — Environment variable access in docs/interaction-board/eggturtle-admin-route-first/scenarios/prod-admin-admin-dashboard-analytics.spec.ts
- process.env — Environment variable access in docs/interaction-board/eggturtle-admin-route-first/scenarios/prod-admin-admin-dashboard-audit-logs.spec.ts
- process.env — Environment variable access in docs/interaction-board/eggturtle-admin-route-first/scenarios/prod-admin-admin-dashboard-billing.spec.ts
Permissions Pass
- Permissions — No dangerous permissions requested
This is a local-first coordination kit designed to orchestrate multi-agent AI coding workflows. It separates task context, execution, and progress tracking to prevent mixed and polluted coding sessions.
Security Assessment
Overall Risk: Low. The repository does not request any dangerous permissions and there are no hardcoded secrets. The automated scan flagged environment variable access in several files, but a closer look reveals these are isolated within documentation and testing scenario files (`.spec.ts`), not the core application code. The tool operates locally by default and appears to safely avoid accessing sensitive user data or making unauthorized network requests. There is no evidence of unexpected shell command execution.
Quality Assessment
The project is actively maintained, with its most recent push occurring today. It is properly licensed under the permissive and standard MIT license. Community trust is currently in its early stages, reflected by a modest 17 GitHub stars. The provided documentation is thorough, giving developers a clear, realistic quickstart guide that emphasizes running a safe local test loop before attempting complex third-party integrations like Feishu.
Verdict
Safe to use — it is a locally focused, MIT-licensed tool with no critical security red flags.
A local-first coordination kit for multi-agent coding workflows.
OpenClaw Coding Kit
A local-first coordination kit for multi-agent coding.
Keep task context, execution, and progress relay separate.
Case study · AgentsGalaxy · OpenClaw repo
OpenClaw Coding Kit helps you stop turning one long AI coding session into a polluted mix of planning, implementation, and status tracking.
It is built for people looking for a repeatable OpenClaw workflow, Codex workflow, multi-agent coding setup, AI coding task orchestration loop, or a local-first path to Feishu-ready collaboration.
It gives you a repeatable delivery loop:
- turn a request into tracked task context
- hand implementation off to a dedicated coding role
- relay child-session progress back to the parent workflow
- start locally, then add OpenClaw and Feishu only when you need them
This repository is not trying to replace OpenClaw itself.
It is an operator kit layered on top of OpenClaw.
3-Minute Quickstart
If you only want to answer one question, make it this:
Can this repository give me a stable local loop before I touch Feishu, OAuth, or a full collaboration setup?
This repository is easiest to understand in local-first mode.
Do not start with Feishu.
Run the smallest useful path first:
python3 -m py_compile skills/pm/scripts/*.py skills/coder/scripts/*.py
python3 skills/pm/scripts/pm.py init --project-name demo --task-backend local --doc-backend repo --dry-run
python3 skills/pm/scripts/pm.py context --refresh
python3 skills/pm/scripts/pm.py route-gsd --repo-root .
What success looks like:
- the repo scripts load cleanly
- local task/doc context can be initialized
- the repo can produce a next-step routing result
- you have a working local-first loop before any real integration
Next:
- want OpenClaw integration? Go to
INSTALL.md - want Feishu integration? Skip the README path and follow the integrated install track
- want the internal role model? Continue reading below
Why This Exists
Most AI coding setups break down for the same reasons:
- business discussion and implementation details collapse into one polluted session
- PM-side context and coder-side execution do not share the same truth
- progress from sub-sessions is hard to route back into the parent workflow
- installation instructions, runtime config, and actual operator flow drift apart over time
This repository addresses that by making the execution path explicit instead of implicit:
PMowns task intake, context refresh, document sync, and routingcoderowns implementation and validation inside ACP sessionsacp-progress-bridgeowns progress/completion relay onlyFeishu task/docis optional collaboration truth in integrated modelocal task + repo docsprovides the lowest-friction starting point
Before / After
| Without this kit | With this kit |
|---|---|
| one long mixed session | explicit PM -> coder -> relay loop |
| task truth lives in chat memory | task/doc/context state is externalized |
| progress from sub-sessions gets lost | parent session receives structured progress |
| setup failures blur together | local-first smoke path isolates failure layers |
| Feishu becomes a prerequisite too early | Feishu stays optional until later |
Who This Is For
This repository is a good fit if you are one of these:
- an individual operator who wants a cleaner local-first AI coding loop before touching collaboration tooling
- a builder who keeps losing task context across planning and implementation sessions
- a team experimenting with OpenClaw + Codex, but not ready to make Feishu a hard prerequisite
- an engineer who wants a repeatable operator path instead of an improvised long-running chat workflow
What You Will See After Running It
After the quickstart, you should expect to see:
- repo-local PM context files under
.pm/ - a working local task/doc mode instead of missing-backend errors
- a next-step routing result from
route-gsd - a clearer sense of where planning, coding, and progress relay are separated
Visual Walkthrough
These screenshots show the kind of operator flow this repository is trying to support.
Structured initialization |
Tracked iteration |
Visible progress |
Real delivery |
Common Use Cases
Teams and individual operators usually land here when they want one of these:
- a local-first AI coding workflow before turning on Feishu or OAuth
- a multi-agent coding setup with clearer task routing between PM and coder roles
- an OpenClaw + Codex delivery loop that is more structured than one long chat session
- a way to keep task context, execution, and progress relay separate across sessions
- a Feishu-ready coordination layer that can still be validated without Feishu first
When To Use This
Use this repository when you want:
- a local-first validation path before touching real collaboration systems
- a clearer boundary between PM reasoning and coder execution
- a repeatable OpenClaw + Codex + ACP workflow instead of one long improvised session
- optional Feishu integration without making Feishu a hard prerequisite for smoke checks
Skip this repository if you only need:
- a single-agent one-off coding session
- no task tracking or write-back at all
- no need to preserve context across planning and execution roles
What You Get
| Area | Included | Purpose |
|---|---|---|
| Task orchestration | skills/pm |
task intake, context refresh, doc sync, GSD routing |
| Execution worker | skills/coder |
canonical ACP coding worker |
| Product canvas | skills/product-canvas |
unified product flow board, scenario assets, miniapp/web UI review entrypoint |
| Board truth layer | skills/interaction-board |
page inventory, draw.io board, HTML board, screenshot-ready manifest |
| Feishu bridge reuse | skills/openclaw-lark-bridge |
calls Feishu tools from a running OpenClaw gateway |
| Progress relay | plugins/acp-progress-bridge |
sends child-session progress and completion back to the parent |
| Config references | examples/* |
minimal and extended config snippets |
| Verification | tests/* |
repo-local validation baseline |
Operating Modes
Start with the smallest mode that proves value:
Local-First
Use this when your goal is to verify the repo, not the whole collaboration stack.
Recommended config:
{
"task": { "backend": "local" },
"doc": { "backend": "repo" }
}
Good for:
- smoke checks
- PM/coder/GSD routing validation
- bootstrap verification
- installation debugging without Feishu
Integrated
Use this only after the local-first path is stable:
- Codex + OpenClaw runtime
- agent binding and ACP execution
- Feishu bot / group / task / doc integration
- progress bridge and authorization flows
FAQ
Do I need Feishu to try this repository?
No. The recommended first run is local-first and does not require Feishu.
Do I need a full OpenClaw runtime before the quickstart?
No. The quickstart is designed to verify the repo-local loop first.
Is this only for teams?
No. The first value is often for a single operator who wants cleaner separation between task context and coding execution.
Is this replacing OpenClaw?
No. This repository is a coordination layer on top of OpenClaw, not a replacement for it.
What kind of workflow is this repository trying to improve?
It is aimed at OpenClaw and Codex users who want a more repeatable AI coding workflow, especially when work spans planning, execution, and progress relay across multiple sessions.
Architecture At A Glance
flowchart LR
U[User / PM Conversation] --> F[OpenClaw Front Agent]
F --> PM[PM Skill]
PM --> T[Shared Truth\nTask / Doc / Context]
PM --> G[GSD Routing\nOptional Planning]
PM --> C[Coder Dispatch]
C --> A[ACP / Codex Worker]
A --> B[acp-progress-bridge]
B --> F
T --> PM
T --> A
F -. optional .-> L[Feishu Channel]
T -. backend .-> X[Local Task + Repo Docs\nor Feishu Task + Docs]
Editable diagram sources:
Installation Strategy
Recommended order:
- install runtime prerequisites first
- verify repo-local smoke path
- deploy
pm,coder,openclaw-lark-bridge, andacp-progress-bridge
preferred entrypoint:python3 scripts/sync_local_skills.py --target both - wire
openclaw.jsonandpm.json - only then add Feishu bot, group, permissions, and OAuth when required
- finish with real backend initialization and E2E verification
That order is intentional.
It keeps runtime problems, config problems, and collaboration-system problems from collapsing into one debugging session.
Repository Layout
openclaw-coding-kit/
README.md
README.zh-CN.md
INSTALL.md
examples/
openclaw.json5.snippets.md
pm.json.example
plugins/
acp-progress-bridge/
skills/
coder/
openclaw-lark-bridge/
pm/
tests/
diagrams/
openclaw-coding-kit-architecture.drawio
openclaw-coding-kit-architecture.svg
Design Principles
PMis the tracked-work front doorcoderexecutes; it does not own task/doc truthGSDowns roadmap/phase planning, not task/doc truthbridgeis a relay, not a source of truth- default to
local/repofirst, real Feishu second - keep the OpenClaw baseline on
2026.3.22, not2026.4.5+
Feishu Integration Notes
If you enable @larksuite/openclaw-lark:
- bot creation, sensitive permission approval, version publishing, and
/auth//feishu authstill include manual user steps - PM now supports common
env/file/execSecretRef resolution forappSecret - do not keep both built-in
plugins.entries.feishuandopenclaw-larkenabled at the same time
That last point matters. Duplicate Feishu tool registration can cause tool conflicts and, in heavier environments, even destabilize CLI introspection.
Detailed install and permission guidance:
Compatibility
| Item | Baseline |
|---|---|
| Python | >= 3.9 |
| Node.js | >= 22 |
| OpenClaw | 2026.3.22 |
| PM state dir | prefers openclaw-coding-kit, still falls back to legacy openclaw-pm-coder-kit |
Included References
Security
Do not commit:
- real
appId/appSecret - OAuth token or device auth state
- real group IDs, allowlists, user identifiers
- real tasklist GUIDs or document tokens
- local session stores or runtime caches
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