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/gaasher/agent-loop-skills/plan-loopnpx skills add gaasher/Agent-Loop-Skills --skill plan-loopgit clone --depth 1 https://github.com/gaasher/Agent-Loop-SkillsWhat 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 | $0.00176 | $0.02604 |
| Opus 5 | $0.00088 | $0.01302 |
| Sonnet 5 | $0.00035 | $0.00521 |
| Haiku 4.5 | $0.00018 | $0.00260 |
Grade A, and why
plan-loop 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 — 159 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Plan Loop
A planning loop: it turns a prompt into a plan detailed and correct enough to hand to a lower-tier
model. The artifact is the plan — plan.md (the layout) + tasks.json (PR-sized tasks, each with
files, tests, dependencies, and atomic subtasks). The feedback signal is two-part, like the repo's other
evaluator loops: an objective gate (tools/validate_plan.py — schema shape, an acyclic dependency
graph, a valid topological order, full component coverage) and a qualitative gate (a separate
principal engineer agent that critiques alignment, decomposition, testability, and whether a junior
could execute each task without guessing). You build the plan from first principles, validate it,
critique it, and revise until the critique passes. This is the plan stage of a larger
prompt → plan → execute → debug pipeline; it stops once the plan is ready to delegate.
When to use
Use to convert a feature/bug/refactor prompt into an executable plan grounded in a real repository — when the goal is a hand-off artifact a downstream executor (or a smaller model) can implement task-by-task. The plan is only as good as its weakest task for a literal-minded implementer, so the loop optimizes for executability, not prose.
Default: ground the plan in the <repo> you are given and let the principal-engineer critique drive the
revisions. Escape hatch: if a key decision can't be resolved from the prompt or the repo, record it as an
open_question for the human rather than guessing. Not for writing the code (a downstream execute loop),
and not for research/experiment proposals (use research-proposal).
Setup
Resolve bindings interactively. If loop.run.yaml exists, load it, confirm the values in one line, and
skip to the loop. Otherwise: on Claude Code (the AskUserQuestion tool is available) infer a likely
value per binding and recommend it; on other hosts ask each as a quoted prompt. Then write
loop.run.yaml (format: examples/run.example.yaml) and confirm before creating any other files.
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 159 lines · 176 tokens per session scan A 09517e873c74
plan-loop is a skill published in the GitHub repository gaasher/Agent-Loop-Skills (161 stars, last pushed 2mo ago), licensed MIT. It adds 176 tokens to every session and 2,604 once invoked, about $0.0009 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
fixing-flaky-e2e-tests
Diagnose and fix flaky Playwright e2e tests. Use when tests fail intermittently, show timeout errors, have snapshot mismatches, or exhibit browser-specific failures.
fixing-streamlit-ci
Analyze and fix failed GitHub Actions CI jobs for the current branch/PR. Use when CI checks fail, PR checks show failures, or you need to diagnose lint/type/test errors and verify fixes locally.
updating-internal-docs
Review internal documentation (.md files) against the current codebase state and propose updates for outdated or incorrect information.
reviewing-readability
Evaluates comments, docstrings, and naming in code for readability by a developer new to the codebase — is the documentation clear and concise, and is non-obvious logic documented? Produces findings with concrete proposed rewrites (or additions where documentation is missing); the caller decides whether to apply them…
reviewing-pr-description
Evaluates a PR's title and description for readability — do they clearly and concisely convey what changed and why to a reviewer? Produces findings with concrete proposed rewrites; the caller decides whether to apply them or present them as feedback. Use when finalizing a PR or reviewing PR metadata. For code…
ppt-analysis
PPT (.pptx/.ppt) 全量解析。覆盖：所有 slide 文本/表格/图表提取、嵌入图片 caption、纯图片 slide 渲染识别、数据标签提取。.