Borrowing it
Nothing to install: this file belongs to LEEI1337/phantom-neural-cortex. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/LEEI1337/phantom-neural-cortex/master/.claude/skills/test-fixing/SKILL.mdgit clone --depth 1 https://github.com/LEEI1337/phantom-neural-cortexWrote 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/skills/leei1337/phantom-neural-cortex/test-fixing)<a href="https://agentmods.dev/skills/leei1337/phantom-neural-cortex/test-fixing"><img src="https://agentmods.dev/badge/skills/leei1337/phantom-neural-cortex/test-fixing/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/skills/leei1337/phantom-neural-cortex/test-fixing"><img src="https://agentmods.dev/badge/skills/leei1337/phantom-neural-cortex/test-fixing.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 | $0.00079 | $0.00720 |
| Opus 5.5 | $0.00032 | $0.00288 |
| Sonnet 5.5 | $0.00016 | $0.00144 |
| Haiku 4.5 | $0.00008 | $0.00072 |
Grade A, and why
test-fixing 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 1mo 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.
This is a copy
91% identical to test-fixing — 5 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 111 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Test Fixing Workflow
Systematically identify and fix all failing tests using smart grouping strategies.
When to Use
Automatically activate when the user:
- Explicitly asks to fix tests ("fix these tests", "make tests pass")
- Reports test failures ("tests are failing", "test suite is broken")
- Completes implementation and wants tests passing
- Mentions CI/CD failures due to tests
Systematic Approach
1. Initial Test Run
Run make test to identify all failing tests.
Analyze output for:
- Total number of failures
- Error types and patterns
- Affected modules/files
2. Smart Error Grouping
Group similar failures by:
- Error type: ImportError, AttributeError, AssertionError, etc.
- Module/file: Same file causing multiple test failures
- Root cause: Missing dependencies, API changes, refactoring impacts
Prioritize groups by:
- Number of affected tests (highest impact first)
- Dependency order (fix infrastructure before functionality)
3. Systematic Fixing Process
For each group (starting with highest impact):
-
Identify root cause
- Read relevant code
- Check recent changes with
git diff - Understand the error pattern
-
Implement fix
- Use Edit tool for code changes
- Follow project conventions (see CLAUDE.md)
- Make minimal, focused changes
-
Verify fix
- Run subset of tests for this group
- Use pytest markers or file patterns:
uv run pytest tests/path/to/test_file.py -v uv run pytest -k "pattern" -v - Ensure group passes before moving on
-
Move to next group
4. Fix Order Strategy
Infrastructure first:
- Import errors
- Missing dependencies
- Configuration issues
Then API changes:
- Function signature changes
- Module reorganization
- Renamed variables/functions
Finally, logic issues:
- Assertion failures
- Business logic bugs
- Edge case handling
5. Final Verification
After all groups fixed:
- Run complete test suite:
make test - Verify no regressions
- Check test coverage remains intact
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.
- 1mo ago First seen · 111 lines · 79 tokens per session scan A e53b47fdb389
test-fixing is a skill published in the GitHub repository LEEI1337/phantom-neural-cortex (5 stars, last pushed 7mo ago), licensed MIT. It adds 79 tokens to every session and 720 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 91% identical to test-fixing, differing in 5 lines, and is treated as a copy.
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