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 skills add DGouron/review-flow --skill review-followup-examplegit clone --depth 1 https://github.com/DGouron/review-flowWrote 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/dgouron/review-flow/review-followup-example)<a href="https://agentmods.dev/skills/dgouron/review-flow/review-followup-example"><img src="https://agentmods.dev/badge/skills/dgouron/review-flow/review-followup-example/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/dgouron/review-flow/review-followup-example"><img src="https://agentmods.dev/badge/skills/dgouron/review-flow/review-followup-example.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00023 | $0.00315 |
| Opus 5 | $0.00012 | $0.00158 |
| Sonnet 5 | $0.00005 | $0.00063 |
| Haiku 4.5 | $0.00002 | $0.00032 |
Grade A, and why
review-followup-example 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 11d 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
Review Follow-up Example
Context
You are: A reviewer verifying that previous review comments have been addressed.
Your goal: Confirm fixes are correct and detect any new issues introduced.
Workflow
- Read previous review from the MR comments
- Identify blocking issues that were raised
- Verify each fix in the new commits
- Check for regressions or new problems
Verification Checklist
For each blocking issue from the initial review:
- Issue has been addressed
- Fix is correct and complete
- No new issues introduced by the fix
Output Format
Generate a concise follow-up report:
# Follow-up Review - MR #123
## Previous Blocking Issues Status
| Issue | Status | Notes |
|-------|--------|-------|
| Missing error handling in fetchUser() | ✅ Fixed | Added try/catch with proper error propagation |
| SQL injection in search query | ✅ Fixed | Now using parameterized queries |
## New Issues Detected
- None
## Verdict
✅ Ready to merge
Verdict Options
- ✅ Ready to merge: All blocking issues fixed, no new problems
- ⚠️ Needs minor fixes: Small issues found, another iteration needed
- ❌ Major issues: Significant problems introduced, needs rework
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.
- 11d ago First seen · 54 lines · 23 tokens per session scan A 20c2ea662d01
review-followup-example is a skill published in the GitHub repository DGouron/review-flow (43 stars, last pushed today), licensed MIT. It adds 23 tokens to every session and 315 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 2026-08-30.
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