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 yeaight7/agent-powerups --skill receiving-code-reviewgit clone --depth 1 https://github.com/yeaight7/agent-powerupsWrote 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/yeaight7/agent-powerups/receiving-code-review)<a href="https://agentmods.dev/skills/yeaight7/agent-powerups/receiving-code-review"><img src="https://agentmods.dev/badge/skills/yeaight7/agent-powerups/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/yeaight7/agent-powerups/receiving-code-review"><img src="https://agentmods.dev/badge/skills/yeaight7/agent-powerups/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 | $0.00033 | $0.00813 |
| Opus 5 | $0.00016 | $0.00407 |
| Sonnet 5 | $0.00007 | $0.00163 |
| Haiku 4.5 | $0.00003 | $0.00081 |
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 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 — 118 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Purpose
Evaluate code review feedback with technical rigor before implementing any changes. Prevents wasted work from blindly applying incorrect suggestions and maintains technical integrity.
When to Use
- Receiving code review comments from any reviewer (human or automated).
- Feedback seems unclear or technically questionable.
- Multiple review items arrive at once and need to be prioritized.
Inputs
- Code review feedback (comments, suggestions, required changes).
- Access to the codebase to verify claims.
Workflow
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
Handling Unclear Feedback
If any item is unclear: stop, ask for clarification on all unclear items before implementing anything. Items may be related — partial understanding leads to wrong implementation.
Feedback from the User (Project Owner)
- Trusted — implement after understanding.
- Still ask if scope is unclear.
- Skip to action or brief technical acknowledgment. No performative agreement.
Feedback from External Reviewers
Before implementing:
- Is it technically correct for this codebase?
- Does it break existing functionality?
- Is there a reason the current implementation exists?
- Does it work on all target platforms/versions?
- Does the reviewer understand the full context?
If suggestion is wrong: push back with technical reasoning. If you cannot easily verify: say so and ask for direction. If it conflicts with prior architectural decisions: stop and discuss with the project owner before proceeding.
YAGNI Check
If a reviewer suggests "implementing properly" a feature or endpoint:
grep -r "feature_name" .
If unused: propose removal (YAGNI). If used: implement.
Implementation Order
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 First seen · 118 lines · 33 tokens per session scan A 4054cc5c58a6
receiving-code-review is a skill published in the GitHub repository yeaight7/agent-powerups (6 stars, last pushed 3d ago), licensed Apache-2.0. It adds 33 tokens to every session and 813 once invoked, about $0.0002 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 2026-09-14.
Other skills, from other repositories
github-code-review
Review PRs: diffs, inline comments via gh or REST.
simplify-code
Sequential 3-lens cleanup of recent code changes.
requesting-code-review
Pre-commit review: security scan, quality gates, auto-fix.
kodama-constraints
Enforce non-negotiable safety, scope, security, and quality constraints for implementation and review work.
hqe
Comprehensive codebase health auditing, remediation, and verification skill based on the canonical HQE Protocol v5.0.0.
adk-review
Reviews the uncommitted changes in an adk-python working tree and reports correctness, design, public-API stability, test, sample and documentation gaps as a prioritized findings report, fixing them only when asked. Use when the user asks to review local changes, wants a self-review before opening a pull request, asks…