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/claritune/mutantx/mx-reportnpx skills add Claritune/mutantx --skill mx-reportgit clone --depth 1 https://github.com/Claritune/mutantxWhat 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 | $0.00023 | $0.00541 |
| Opus 5 | $0.00012 | $0.00270 |
| Sonnet 5 | $0.00005 | $0.00108 |
| Haiku 4.5 | $0.00002 | $0.00054 |
Grade A, and why
mx-report 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 — 61 lines — stays where its author put it; the contents beside it link to each section on GitHub.
MutantX — Phase 4: Reporting
This phase has two layers:
- Deterministic (Python script) — calculates scores, generates summary tables, includes patch diffs for survived mutants
- AI analysis (this skill) — reads the report and adds "why this matters" explanations, suggested tests, and prioritized recommendations
Step 1: Run the Deterministic Report Script
Locate and execute mx-report.py:
python3 <path-to-mx-report.py> --mutants-dir .mutants
Find mx-report.py by checking these locations in order:
./src/scripts/mx-report.py(if inside the MutantX repo)~/.claude/scripts/mx-report.py(global install)
This generates .mutants/report.md with summary statistics, survived mutant patches, killed mutant table, and updates the manifest summary. Display the script's output to the user.
Step 2: AI Analysis of Survived Mutants
After the script runs, read .mutants/report.md. For each survived mutant section (marked with <!-- AI analysis --> comments), replace the comment with:
For each survived mutant:
- Read the patch diff shown in the report.
- Read the relevant source file to understand the surrounding code context.
- Add these two sections after the diff block:
Why this matters: A brief explanation of what kind of bug this mutation represents and what could go wrong in production if this bug existed. Be specific to the actual code, not generic.
Suggested test: A concrete, copy-pasteable test case (in the project's language and test framework) that would catch this mutant. Use realistic test names and assertions.
After all survived mutants:
Add a ## Recommendations section at the end of the report with 2-5 bullet points summarizing the most important test gaps found. Be specific — reference file names and functionality, not generic advice.
Step 3: Present Results
Display a concise summary to the user:
Mutation Testing Complete!
Score: 84% (16 killed / 19 testable)
Survived: 3 mutants — see .mutants/report.md for details
Top gaps:
- src/billing.ts: price rounding logic has no edge case tests
- src/auth.ts: token expiry check can be bypassed
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 · 61 lines · 23 tokens per session scan A 013614913ea1
mx-report is a skill published in the GitHub repository Claritune/mutantx (2 stars, last pushed 1mo ago), licensed MIT. It adds 23 tokens to every session and 541 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 skills, from other repositories
commit
Atomic git commit with conventional message. Use when the user says "commit", "save my changes", "commit this", or wants to create a git commit. Stages specific files, writes a conventional commit message with body explaining non-obvious decisions. Never uses git add -A.
systematic-debugging
4-phase root cause debugging: understand bugs before fixing.
python-run
Run and debug Python scripts in the project. Use when the user says "run python", "execute this script", "debug this py file", or wants to run/modify a .py file. Handles dependency checks, linting, execution, and error analysis.
github-code-review
Review PRs: diffs, inline comments via gh or REST.
plan
Plan mode: write markdown plan, no execution.
simplify-code
Sequential 3-lens cleanup of recent code changes.