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 Snowflake-Labs/cocoplus --skill cocoreviewgit clone --depth 1 https://github.com/Snowflake-Labs/cocoplusWrote 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/snowflake-labs/cocoplus/cocoreview)<a href="https://agentmods.dev/skills/snowflake-labs/cocoplus/cocoreview"><img src="https://agentmods.dev/badge/skills/snowflake-labs/cocoplus/cocoreview/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/snowflake-labs/cocoplus/cocoreview"><img src="https://agentmods.dev/badge/skills/snowflake-labs/cocoplus/cocoreview.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 | $0.00057 | $0.04935 |
| Opus 5.5 | $0.00023 | $0.01974 |
| Sonnet 5.5 | $0.00011 | $0.00987 |
| Haiku 4.5 | $0.00006 | $0.00494 |
Grade A, and why
cocoreview 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 20d 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 — 366 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Objective
You are executing CocoReview — a structured, evidence-grounded code review. Your task is to produce an actionable prioritized finding report using the six-severity vocabulary and the four-phase review process.
Before proceeding, verify that .cocoplus/ exists. If not, output: "CocoPlus is not initialized. Run $pod init first." Then stop.
Routing Table
| Invocation | Loads |
|---|---|
$review <file> |
universal-quality.md + language guide inferred from file extension |
$review --security |
universal-quality.md + language guide + security-review.md |
$review --architecture |
universal-quality.md + architecture-review.md |
$review --complexity |
Run cocoreview/pr-complexity only — no LLM review |
$review --language <lang> |
universal-quality.md + explicit language guide |
| `$review export markdown | html |
Step 1 — Parse Invocation and Run Complexity Analyzer
Parse the $review command for file path and flags. If no file path provided for non-complexity commands, ask: "Which file or directory should I review?"
Always run cocoreview/pr-complexity first (before any LLM work):
invoke cocoreview/pr-complexity --file <path>
Read the output JSON. If size_bucket is XL (≥800 lines of change), surface a split recommendation:
⚠ CocoReview: This artifact is in the XL complexity bucket (<N> lines changed, complexity score <score>).
XL changes are significantly harder to review effectively. Consider splitting into:
- Schema/model changes (separate PR)
- Logic/transformation changes (separate PR)
- Test additions (can merge with logic changes)
Proceed with full review anyway? [YES/SPLIT]
If --complexity flag: display complexity output and stop (no LLM review).
Step 2 — Load Reference Guides
Based on routing table above, load the appropriate guides from .cortex/skills/cocoreview/. Always load universal-quality.md. Load others based on flags and risk_flags from complexity analyzer output (e.g., if security-sensitive in risk_flags, load security-review.md).
What ships with it
8 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 20d ago Changed d712ddb358fc
- 1mo ago First seen · 366 lines · 57 tokens per session scan A b48e15950443
cocoreview is a skill published in the GitHub repository Snowflake-Labs/cocoplus (726 stars, last pushed 20d ago), licensed MIT. It adds 57 tokens to every session and 4,935 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 2026-09-03.
Other skills, from other repositories
resolve-review-pr
Review an existing fix PR using the audited repro and full diff, preserving branch and approval rules.
code-review
Review code for quality, bugs, and best practices. Use when asked to review or audit code.
audit
(forwward) Aggressively audits and cleans a codebase — removes slop, dead code, and AI-generated noise; fixes DRY/SOLID violations; restructures toward the simplest correct implementation. One commit, no behavior changes. Use when the user says "audit the code", "clean up tech debt", "remove dead code", "deslop"…
resolve-investigate
Verify the reported path, cause, and historical intent before choosing a fix.
otto-impact-assessment
Check the final diff's consumers, focused tests, evidence, and gaps.
feature-publish
Commit the feature, write the PR body, publish in the selected mode, and drive the PR.