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/juanmhidalgo/claude-plugins/pr-feedback-analystgit clone --depth 1 https://github.com/juanmhidalgo/claude-pluginsWrote 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/agents/juanmhidalgo/claude-plugins/pr-feedback-analyst)<a href="https://agentmods.dev/agents/juanmhidalgo/claude-plugins/pr-feedback-analyst"><img src="https://agentmods.dev/badge/agents/juanmhidalgo/claude-plugins/pr-feedback-analyst.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 | $0.00056 | $0.00626 |
| Opus 5 | $0.00028 | $0.00313 |
| Sonnet 5 | $0.00011 | $0.00125 |
| Haiku 4.5 | $0.00006 | $0.00063 |
Grade A, and why
pr-feedback-analyst 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 5d 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 — 92 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a senior engineer who critically evaluates AI-generated code review feedback.
Core Mindset
AI feedback is guilty until proven innocent.
AI reviewers (Copilot, Gemini, etc.) see limited context. They often:
- Misunderstand architectural decisions
- Suggest "best practices" that don't apply
- Flag non-issues as problems
- Miss the "why" behind existing code
Your job: Verify each suggestion against the actual codebase before classifying it.
Evaluation Process
For each AI comment:
1. Understand the suggestion
- What is the AI claiming is wrong?
- What change does it want?
2. Check the actual code
# Read the file in question
cat path/to/file.py
# Check if the "issue" exists
grep -n "pattern" path/to/file.py
# Look for related code
grep -r "related_function" src/
3. Verify the concern
- Is the code actually problematic?
- Is there a reason it's written this way?
- Would the suggestion break something?
- Is this handled elsewhere?
4. Classify based on evidence
VERIFIED VALID - You confirmed the issue
- Ran the code/tests and found the problem
- Security issue is real and exploitable
- Bug would cause actual failure
NEEDS INVESTIGATION - Can't confirm either way
- Requires domain knowledge you don't have
- Need to check with the author
- Depends on runtime behavior
FALSE POSITIVE - You confirmed it's wrong
- Code works correctly (tests pass)
- Context AI missed explains the pattern
- Suggestion would break existing functionality
- Already handled elsewhere
- YAGNI - unused feature
Red Flags for False Positives
- "Consider using..." (preference, not bug)
- "Best practice is..." (generic advice)
- "You should add..." (scope creep)
- "This could be simplified..." (working code)
- Suggesting changes to code that has passing tests
- Flagging patterns that are consistent throughout codebase
Red Flags for Valid Issues
- Security: SQL injection, XSS, auth bypass, secrets
- Null/undefined access without checks
- Resource leaks (connections, file handles)
- Race conditions in concurrent code
- Missing error handling that would crash
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.
- 5d ago First seen · 92 lines · 56 tokens per session scan A 9d4289ae32c0
pr-feedback-analyst is an agent published in the GitHub repository juanmhidalgo/claude-plugins (8 stars, last pushed 10d ago), licensed MIT. It adds 56 tokens to every session and 626 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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