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 commands/elct9620/ai-coding-skills/refactorgit clone --depth 1 https://github.com/elct9620/ai-coding-skillsWrote this? Show the measurements
A badge for your README with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them.
[](https://agentmods.dev/commands/elct9620/ai-coding-skills/refactor)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 | $0.00019 | $0.02989 |
| Opus 5 | $0.00010 | $0.01494 |
| Sonnet 5 | $0.00004 | $0.00598 |
| Haiku 4.5 | $0.00002 | $0.00299 |
Grade A, and why
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 3d 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 — 163 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Rule
The <execute name="main">ARGUMENTS</execute> is entry point for this command.
Skills Rubric
To select skills for cleaning up legacy code, consider the following rubric:
| Skill | When to use |
|---|---|
coding:refactoring |
Core skill for all refactoring operations, always required. |
coding:architecture |
Cross-class refactoring (Move Method, Extract Class), layer violations, module boundary issues, or structural reorganization. |
coding:principles |
SOLID/DRY violations, coupling issues, or responsibility problems. |
coding:design-patterns |
Replace complex conditionals or duplicated logic with appropriate patterns. |
coding:testing |
Missing test coverage that blocks safe refactoring. |
coding:schema |
Restructuring database schemas, API contracts, or data access patterns. |
coding:security |
Refactoring reveals or addresses security issues (hardcoded secrets, missing input validation). |
The language-specific skills not listed, check all available skills before deciding skills to use.
Definition
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.
- 3d ago First seen · 163 lines · 19 tokens per session scan A 8f9abbdb47ab
refactor is a command published in the GitHub repository elct9620/ai-coding-skills (5 stars, last pushed 7d ago), licensed Apache-2.0. It adds 19 tokens to every session and 2,989 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 2026-08-31.
Other commands, from other repositories
git
Git operations with intelligent commit messages and workflow optimization.
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.