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/review-implementing/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/review-implementing)<a href="https://agentmods.dev/skills/leei1337/phantom-neural-cortex/review-implementing"><img src="https://agentmods.dev/badge/skills/leei1337/phantom-neural-cortex/review-implementing.svg" alt="Measured on agentmods" 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 | $0.00068 | $0.00933 |
| Opus 5 | $0.00034 | $0.00466 |
| Sonnet 5 | $0.00014 | $0.00187 |
| Haiku 4.5 | $0.00007 | $0.00093 |
Grade A, and why
review-implementing 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 6d 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 — 156 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Review Feedback Implementation
Systematically process and implement changes based on code review feedback.
When to Use
Automatically activate when the user:
- Provides reviewer comments or feedback
- Pastes PR review notes
- Mentions implementing review suggestions
- Says "address these comments" or "implement feedback"
- Shares a list of changes requested by reviewers
Systematic Workflow
1. Parse Reviewer Notes
Identify individual feedback items:
- Split numbered lists (1., 2., etc.)
- Handle bullet points or unnumbered feedback
- Extract distinct change requests
- Clarify any ambiguous items before starting
2. Create Todo List
Use TodoWrite tool to create actionable tasks:
- Each feedback item becomes one or more todos
- Break down complex feedback into smaller tasks
- Make tasks specific and measurable
- Mark first task as
in_progressbefore starting
Example:
- Add type hints to extract function
- Fix duplicate tag detection logic
- Update docstring in chain.py
- Add unit test for edge case
3. Implement Changes Systematically
For each todo item:
Locate relevant code:
- Use Grep to search for functions/classes
- Use Glob to find files by pattern
- Read current implementation
Make changes:
- Use Edit tool for modifications
- Follow project conventions (CLAUDE.md)
- Preserve existing functionality unless changing behavior
Verify changes:
- Check syntax correctness
- Run relevant tests if applicable
- Ensure changes address reviewer's intent
Update status:
- Mark todo as
completedimmediately after finishing - Move to next todo (only one
in_progressat a time)
4. Handle Different Feedback Types
Code changes:
- Use Edit tool for existing code
- Follow type hint conventions (PEP 604/585)
- Maintain consistent style
New features:
- Create new files with Write tool if needed
- Add corresponding tests
- Update documentation
Documentation:
- Update docstrings following project style
- Modify markdown files as needed
- Keep explanations concise
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.
- 6d ago First seen · 156 lines · 68 tokens per session scan A c6fb3bc5272a
review-implementing is a skill published in the GitHub repository LEEI1337/phantom-neural-cortex (5 stars, last pushed 6mo ago), licensed MIT. It adds 68 tokens to every session and 933 once invoked, about $0.0003 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.
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