Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/gcwing/bitfun/gstack-autoplannpx skills add GCWing/BitFun --skill gstack-autoplangit clone --depth 1 https://github.com/GCWing/BitFunWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00152 | $0.09443 |
| Opus 5 | $0.00076 | $0.04722 |
| Sonnet 5 | $0.00030 | $0.01889 |
| Haiku 4.5 | $0.00015 | $0.00944 |
Grade A, and why
autoplan scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 823 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/autoplan — Auto-Review Pipeline
One command. Rough plan in, fully reviewed plan out.
/autoplan reads the full CEO, design, eng, and DX review skill files from disk and follows them at full depth — same rigor, same sections, same methodology as running each skill manually. The only difference: intermediate AskUserQuestion calls are auto-decided using the 6 principles below. Taste decisions (where reasonable people could disagree) are surfaced at a final approval gate.
The 6 Decision Principles
These rules auto-answer every intermediate question:
- Choose completeness — Ship the whole thing. Pick the approach that covers more edge cases.
- Boil lakes — Fix everything in the blast radius (files modified by this plan + direct importers). Auto-approve expansions that are in blast radius AND < 1 day CC effort (< 5 files, no new infra).
- Pragmatic — If two options fix the same thing, pick the cleaner one. 5 seconds choosing, not 5 minutes.
- DRY — Duplicates existing functionality? Reject. Reuse what exists.
- Explicit over clever — 10-line obvious fix > 200-line abstraction. Pick what a new contributor reads in 30 seconds.
- Bias toward action — Merge > review cycles > stale deliberation. Flag concerns but don't block.
Conflict resolution (context-dependent tiebreakers):
- CEO phase: P1 (completeness) + P2 (boil lakes) dominate.
- Eng phase: P5 (explicit) + P3 (pragmatic) dominate.
- Design phase: P5 (explicit) + P1 (completeness) dominate.
Decision Classification
Every auto-decision is classified:
Mechanical — one clearly right answer. Auto-decide silently. Examples: run codex (always yes), run evals (always yes), reduce scope on a complete plan (always no).
Taste — reasonable people could disagree. Auto-decide with recommendation, but surface at the final gate. Three natural sources:
- Close approaches — top two are both viable with different tradeoffs.
- Borderline scope — in blast radius but 3-5 files, or ambiguous radius.
- outside-voice sub-agent disagreements — codex recommends differently and has a valid point.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 823 lines · 152 tokens per session scan A d5681d893468
autoplan is a skill published in the GitHub repository GCWing/BitFun (1,871 stars, last pushed 2d ago), licensed MIT. It adds 152 tokens to every session and 9,443 once invoked, about $0.0008 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
git-delegation
将所有 git 操作委托给 Manager 执行。Worker 无法直接访问 git credentials，因此任何需要认证的 git 操作（clone、push、fetch 等）都需要通过此机制委托给 Manager。.
agentteams-migrate
Analyze current OpenClaw setup and generate a migration package (ZIP) for importing into AgentTeams as a managed Worker.
workerflow-internal-workflow
Use when a QwenPaw-backed Worker needs to decide whether to do work directly, use native subagents for internal parallelism, or create a temporary QwenPaw agent with a custom AGENTS.md and skills.
find-skills
Discover and install agent skills from the open ecosystem. Use when you encounter an unfamiliar domain, framework, or workflow that you lack specialized knowledge about, or when your coordinator suggests searching for skills before starting a task.
higress-gateway-management
Manage the Higress AI Gateway via its Console API (consumers, routes, AI providers, MCP servers). Use when creating consumers, configuring routes, or managing AI gateway settings.
worker-management
Use when admin requests hand-creating or resetting a Worker, starting/stopping a Worker, managing Worker skills, enabling peer mentions, or opening a QwenPaw console. Use agentteams-find-worker only as a helper for Nacos-backed market import or when task assignment needs you to discover a suitable Worker.