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.
git clone --depth 1 https://github.com/Amey-Thakur/AI-SKILLSWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/commands/amey-thakur/ai-skills/plan-refactor)<a href="https://agentmods.dev/commands/amey-thakur/ai-skills/plan-refactor"><img src="https://agentmods.dev/badge/commands/amey-thakur/ai-skills/plan-refactor/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/commands/amey-thakur/ai-skills/plan-refactor"><img src="https://agentmods.dev/badge/commands/amey-thakur/ai-skills/plan-refactor.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What 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 5.1 | $0.00019 | $0.00250 |
| Opus 5 | $0.00010 | $0.00125 |
| Sonnet 5 | $0.00004 | $0.00050 |
| Haiku 4.5 | $0.00002 | $0.00025 |
Grade A, and why
plan-refactor 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.
What it actually says
You were invoked as a slash command. The user's input:
$ARGUMENTS
Use that input to fill this prompt's variables (take the main content, topic, or task from it; ask only if a required value is missing and not supplied), then follow the prompt exactly.
Plan a refactor of:
{code}
Target: {goal}
Use the refactoring skill and agent-refactoring-workflow for sequencing.
Produce:
- The coverage that must exist first, and what to add if it does not.
- Ordered steps, each preserving behaviour and independently committable.
- What is verified after each step.
- The point where the old structure can be removed.
- What could go wrong at each step and how it is detected.
Rules: behaviour must not change; anything that changes behaviour is a separate commit, stated as such. No step may leave the codebase broken. Do not change tests and implementation in the same step. If the code needs redesign rather than restructuring, say so instead of planning a refactor that cannot get there.
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 · 36 lines · 19 tokens per session scan A 18328606ab6c
plan-refactor is a command published in the GitHub repository Amey-Thakur/AI-SKILLS (7 stars, last pushed 3d ago), licensed MIT. It adds 19 tokens to every session and 250 once invoked, about $0.0001 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 2026-09-06.
Other commands, from other repositories
api-aqa-flow
Workflow for backend API test automation: TMS / Issue Tracker test cases → automated API tests, HITL-gated.
spec-impl
Execute spec tasks using TDD methodology.
plan-feature
Production-grade feature planning with dual-AI validation (Claude + Antigravity/Gemini 3 via agy).
arch-boundaries
Install mechanically-enforced architecture boundaries that fail CI on spaghetti imports.
implement-approved-slice
Implement only the approved slice with minimal, explicit, review-friendly changes, then persist execution evidence in slice notes and TASKSTATE.md. The single official execution path of the workflow. Supports an opt-in test-first (TDD) mode, enabled per slice or via --tdd, that writes the failing test before the code…
tdd
Guide a Test-Driven Development workflow for the current task.