Borrowing it
Nothing to install: this file belongs to ivfarias/ceo. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/ivfarias/ceo/main/.ai/skills/receiving-code-review/SKILL.mdgit clone --depth 1 https://github.com/ivfarias/ceoWrote 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/ivfarias/ceo/receiving-code-review)<a href="https://agentmods.dev/skills/ivfarias/ceo/receiving-code-review"><img src="https://agentmods.dev/badge/skills/ivfarias/ceo/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/ivfarias/ceo/receiving-code-review"><img src="https://agentmods.dev/badge/skills/ivfarias/ceo/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 | $0.01489 |
| Opus 5.5 | $0.00015 | $0.00596 |
| Sonnet 5.5 | $0.00008 | $0.00298 |
| Haiku 4.5 | $0.00004 | $0.00149 |
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 1mo 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.
The source is not reproduced here
No licence file
A repository with no LICENSE is all rights reserved by default, so the body is not copied here. The metadata, the measurements and the link are.
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.
- 1mo ago First seen · 214 lines · 38 tokens per session scan A c9382e92b8f3
receiving-code-review is a skill published in the GitHub repository ivfarias/ceo (25 stars, last pushed 7mo ago), with no licence file. It adds 38 tokens to every session and 1,489 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 2026-08-30.
Other skills, from other repositories
arch-check
An architecture and implementation review tool that examines how a codebase is modeled, divided into responsibilities, organized into modules, and connected by dependencies. It also checks for issues such as circular dependencies, over-design, and violations of common design principles.
cost-estimation
Use when the user wants to estimate or predict the cost, token usage, or time of a task BEFORE it runs — "how much will this feature cost to build", "estimate the tokens for this spec", "what will this slice cost", "predict the agent-compute time before I commit" — produces a token + time estimate as a range with…
advocatus-diaboli
Use when acting as the adversarial spec reviewer — raises steel-manned objections across six categories before plan approval, requires evidence per objection, and discloses what was not challenged.
ai-literacy-assessment
This skill should be used when the user asks to "assess AI literacy", "run an assessment", "check literacy level", "evaluate our AI collaboration", "where are we on the framework", or wants to determine their team's AI literacy level using the ALCI instrument.
choice-cartographer
Use when acting as the decision-archaeology agent — surfaces decisions a spec has made (including the silent ones), emits each material choice as a Henney-style pattern story for human disposition, and pays down intent debt before plan approval.
sentinel-design
Use when designing, reviewing, or classifying an agent that guards the human — defines the sentinel category, the three-part signature (S1 read-only, S2 advisory-to-human, S3 explicit honesty rule), the near-miss gallery that explains why read-only-plus-advisory is not sufficient, the…