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 damianvtran/local-operator --skill responding-to-reviewgit clone --depth 1 https://github.com/damianvtran/local-operatorWrote 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/damianvtran/local-operator/responding-to-review)<a href="https://agentmods.dev/skills/damianvtran/local-operator/responding-to-review"><img src="https://agentmods.dev/badge/skills/damianvtran/local-operator/responding-to-review/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/damianvtran/local-operator/responding-to-review"><img src="https://agentmods.dev/badge/skills/damianvtran/local-operator/responding-to-review.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 | $0.00046 | $0.00606 |
| Opus 5.5 | $0.00018 | $0.00242 |
| Sonnet 5.5 | $0.00009 | $0.00121 |
| Haiku 4.5 | $0.00005 | $0.00061 |
Grade A, and why
responding-to-review 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 3d 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 — 52 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Responding to review
Review feedback is input, not instruction. Verify before implementing; answer per item; ship one batched round.
Per finding, in order
- Verify the claim. Reproduce it, or read the code path it names. A finding can be wrong, and implementing a wrong fix adds a bug. If the finding is about evidence (a run, a frame, a number), the fix is to regenerate the evidence, not to edit the sentence.
- Decide: fix, reject, or defer.
- Answer with the decision and its support. No performative agreement ("Great catch!"). Say what happens to the finding; agreement without verification commits you to work you have not assessed.
Clarify all first
- Read the whole round before editing. If an item is genuinely unclear (you cannot state the change it asks for), ask once, for all unclear items together, before the remediation edits. One batch of questions beats three round trips.
One batched round
- Collect all accepted fixes into ONE remediation pass. Do not commit-and-reply per finding.
- Group by stream where the project does (code, tests, docs); keep it one round.
- Where several reviewers ran, wait for all initial reports before ONE remediation
commit and answer their items together. Proven provenance-only metadata fixes
are addenda, not suite resets; unchanged evidence needs compatibility proof.
Rebased content still needs convergence, and real majors still need fixes.
See
guide://deliveryfor the shared bounds.
The reply format
For each item, in the thread where the round was posted:
fixed: <what changed> (<commit SHA>)rejected: <why, with the check that shows it>deferred: <where it is recorded and why it cannot be now>
Never mark fixed without the commit; never leave an item unanswered. "Not addressed" with no reason is a rejection nobody can audit.
When you disagree
- Say so with the evidence: the test output, the code path, the spec line. Silence is the worst option; fix nothing, say nothing, and the item stays open forever.
- Once the round resolves, let it go. Re-litigating a settled item in the next round burns a round for everyone.
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.
- 3d ago Changed · +4 lines b8db87ab9651
- 5d ago First seen · 48 lines · 46 tokens per session scan A e8172b75d233
responding-to-review is a skill published in the GitHub repository damianvtran/local-operator (218 stars, last pushed today), licensed MIT. It adds 46 tokens to every session and 606 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-10-11.
Other skills, from other repositories
building-agents
Use when building or restructuring an LLM agent — provider adapter, tool calling, structured output, RAG, agent loop, eval gate, cost routing, tracing, MCP server — model-agnostic across OpenAI/Anthropic/Gemini/OSS so a model swap is a config change. NOT vector-store SQL alone (that is postgresdb) or service…
agent-eval
Use when measuring whether an LLM or agent system actually got better and gating merges on it: golden sets, fixing an inflated LLM-as-judge, scoring RAG (faithfulness, contextual recall) or agent trajectories (tool correctness, completion), or picking an eval framework. NOT building the agent loop, tools or RAG…
coding-standards
Applies language-neutral engineering standards when writing or reviewing code, especially around boundaries, failures, domain models, dependencies, tests, and type safety.
hqe
Comprehensive codebase health auditing, remediation, and verification skill based on the canonical HQE Protocol v5.0.0.
code-reviewer
A code-review workflow written in Chinese that checks software changes for correctness, security, performance, and maintainability. It also defines a report format for critical issues and suggestions.
requesting-code-review
Use when completing tasks, implementing major features, or before merging. Validates work meets requirements through systematic review process.