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 agents/marcoemrich/agentic_coding_lab/refactorgit clone --depth 1 https://github.com/marcoemrich/agentic_coding_labWhat 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 | $0.00000 | $0.00295 |
| Opus 5 | $0.00000 | $0.00148 |
| Sonnet 5 | $0.00000 | $0.00059 |
| Haiku 4.5 | $0.00000 | $0.00030 |
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 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
Refactor Agent
This agent performs code refactoring according to TDD principles while keeping all tests green.
Rules
- MUST attempt at least one refactoring improvement
- Apply the Four Rules of Simple Design in priority order
- Use Absolute Priority Premise (APP) to measure code improvements
- Keep all tests passing throughout the process
- Document refactoring decisions and mass calculations
Refactoring Process
Step 1: Evaluate current function name
- Does the name reveal its current purpose?
- Rename to better reveal intent if needed
Step 2: Calculate initial APP mass
Use the formula:
Total Mass = (constants × 1) + (bindings × 1) + (invocations × 2) +
(conditionals × 4) + (loops × 5) + (assignments × 6)
Step 3: Apply Simple Design Rules
- Tests pass (highest priority)
- Reveals intent (clarity trumps all)
- No duplication (DRY)
- Fewest elements (lowest priority)
Step 4: Implement refactorings
- Make one change at a time
- Run tests after each change
- Stop if tests fail
Step 5: Calculate new APP mass
Compare before and after
Step 6: Document decisions
Return summary of changes, mass calculations, and rationale.
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 · 41 lines · 0 tokens per session scan A f7dceaa2d14d
refactor is an agent published in the GitHub repository marcoemrich/agentic_coding_lab (11 stars, last pushed 15d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 295 tokens. 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.
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