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 guanyang/open-agent-hub --skill receiving-code-reviewgit clone --depth 1 https://github.com/guanyang/open-agent-hubWrote 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/guanyang/open-agent-hub/receiving-code-review)<a href="https://agentmods.dev/skills/guanyang/open-agent-hub/receiving-code-review"><img src="https://agentmods.dev/badge/skills/guanyang/open-agent-hub/receiving-code-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/guanyang/open-agent-hub/receiving-code-review"><img src="https://agentmods.dev/badge/skills/guanyang/open-agent-hub/receiving-code-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 | $0.00038 | $0.01459 |
| Opus 5 | $0.00019 | $0.00730 |
| Sonnet 5 | $0.00008 | $0.00292 |
| Haiku 4.5 | $0.00004 | $0.00146 |
Grade A, and why
receiving-code-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 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.
This is a copy
100% identical to receiving-code-review — 0 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 — 206 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Code Review Reception
Overview
Code review requires technical evaluation, not emotional performance.
Core principle: Verify before implementing. Ask before assuming. Technical correctness over social comfort.
The Response Pattern
WHEN receiving code review feedback:
1. READ: Complete feedback without reacting
2. UNDERSTAND: Restate requirement in own words (or ask)
3. VERIFY: Check against codebase reality
4. EVALUATE: Technically sound for THIS codebase?
5. RESPOND: Technical acknowledgment or reasoned pushback
6. IMPLEMENT: One item at a time, test each
Forbidden Responses
NEVER:
- "You're absolutely right!" (explicit instruction-file violation)
- "Great point!" / "Excellent feedback!" (performative)
- "Let me implement that now" (before verification)
INSTEAD:
- Restate the technical requirement
- Ask clarifying questions
- Push back with technical reasoning if wrong
- Just start working (actions > words)
Handling Unclear Feedback
IF any item is unclear:
STOP - do not implement anything yet
ASK for clarification on unclear items
WHY: Items may be related. Partial understanding = wrong implementation.
Example:
your human partner: "Fix 1-6"
You understand 1,2,3,6. Unclear on 4,5.
❌ WRONG: Implement 1,2,3,6 now, ask about 4,5 later
✅ RIGHT: "I understand items 1,2,3,6. Need clarification on 4 and 5 before proceeding."
Source-Specific Handling
From your human partner
- Trusted - implement after understanding
- Still ask if scope unclear
- No performative agreement
- Skip to action or technical acknowledgment
From External Reviewers
BEFORE implementing:
1. Check: Technically correct for THIS codebase?
2. Check: Breaks existing functionality?
3. Check: Reason for current implementation?
4. Check: Works on all platforms/versions?
5. Check: Does reviewer understand full context?
IF suggestion seems wrong:
Push back with technical reasoning
IF can't easily verify:
Say so: "I can't verify this without [X]. Should I [investigate/ask/proceed]?"
IF conflicts with your human partner's prior decisions:
Stop and discuss with your human partner first
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 · 206 lines · 38 tokens per session scan A 091df1629510
receiving-code-review is a skill published in the GitHub repository guanyang/open-agent-hub (961 stars, last pushed today), licensed MIT. It adds 38 tokens to every session and 1,459 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to receiving-code-review, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
agent-design-best-practices
Best practices for designing Claude Code agent files (.claude/agents/.md). This skill should be used when writing or reviewing agent markdown files to ensure proper design with focused domains, correct tool access, reusable definitions, and separation of capabilities from lifecycle. Combines Anthropic's official…
agent-cross-review
Structured cross-review protocol between specialized agents. Ensures scope alignment, priority calibration, and domain-aware feedback. Use when one agent reviews another's work, during handoffs, or when validating cross-cutting concerns.
neo-code-review
Use this skill when the user asks to review or audit source code, a PR, diff, commit, or recent changes for bugs, security, performance, tests, compatibility, or maintainability, duplicated code or logic across files, or hard-coded values. Also use it after an AI agent finishes modifying code to inspect the current…
neo-pr
Use this skill when the user asks to create, draft, review, format, or generate a Pull Request (PR) title and description, specify target or source branches for a repository, or convert git branch diffs into concise, high-impact, non-AI-slop PR content.
neo-rust
Use this skill when writing, refactoring, debugging, or auditing Rust code. Trigger for .rs files, Cargo projects, ownership/borrowing/lifetime issues, Result/Option error handling, unnecessary clone/performance work, unsafe code review, or modern Rust architecture.
capturing-golden-rules
Use when the same mistake or bad pattern recurs, when a code-review comment is really a convention worth enforcing, or when you catch AI-generated drift/slop and want it to never happen again. Encodes the lesson as a durable rule in golden-rules.md — the feedback-flywheel / harness-layer-learning ratchet — instead of…