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 allemaar/open-skills --skill cold-reviewgit clone --depth 1 https://github.com/allemaar/open-skillsWrote 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/allemaar/open-skills/cold-review)<a href="https://agentmods.dev/skills/allemaar/open-skills/cold-review"><img src="https://agentmods.dev/badge/skills/allemaar/open-skills/cold-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/allemaar/open-skills/cold-review"><img src="https://agentmods.dev/badge/skills/allemaar/open-skills/cold-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00107 | $0.02212 |
| Opus 5 | $0.00053 | $0.01106 |
| Sonnet 5 | $0.00021 | $0.00442 |
| Haiku 4.5 | $0.00011 | $0.00221 |
Grade A, and why
cold-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 11d 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 — 155 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/cold-review
Run an independent review of actual work artifacts using fresh context. The goal is to surface issues the current agent misses because it has too much context, owns the work, or is biased toward its own implementation. This is a workflow, not a persistent mode.
Structured execution spec:
protocol.yon. Read it for the canonical rules and step sequence; this file is explanation. The two must stay in sync — if you edit one, update the other and refresh the@STAMPdate.
Caller Options. Before executing, run the Caller Options protocol (
caller-options/SKILL.md): triage this invocation for material optionality across the venues and modes declared in front-matter; if one path clearly dominates, proceed silently; otherwise surface the options to the caller. cold-review's modes are the reviewer-count tiers (narrow/medium/broad→ 1/2/3 reviewers); its venue isinlineonly (cold-review spawns its own reviewers — COP never wraps a self-orchestrating skill).
Boundary
Use cold-review for completed or in-progress work artifacts: diffs, files, code, tests, screenshots, UI states, plans, specs, docs, command outputs, worker reports. Do not use it for raw ideas. Use insight-adversarial for multi-POV critique of plans/ideas/strategy, verify for a formal self-gate, and double-check when the current agent should re-read and challenge a specific target itself.
Step 1 — Establish target and objectives
Identify: the work assessed; the objectives (infer and label as inferred if not given); the constraints (non-goals, compatibility, style, acceptance criteria, preferences); and the available evidence (tests, commands, screenshots, docs, diffs, source). If no concrete artifact exists, stop and ask for the target — do not review from vague memory.
Step 2 — Classify the work
Classify the primary target type and choose lenses accordingly:
| Target type | Required review lenses |
|---|---|
| Backend / code | Correctness, architecture, security, tests, maintainability, performance |
| Frontend / UI | User flow, visual layout, responsiveness, accessibility, interaction states |
| Plan / spec | Ambiguity, scope, sequencing, assumptions, executability |
| Agent skill / workflow | Trigger clarity, runtime portability, step ordering, failure modes |
| Mixed work | Pick the top 2 target types and cover both |
What ships with it
1 file 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.
- 11d ago First seen · 155 lines · 107 tokens per session scan A 5a51f3ee21f3
cold-review is a skill published in the GitHub repository allemaar/open-skills (14 stars, last pushed yesterday), licensed Apache-2.0. It adds 107 tokens to every session and 2,212 once invoked, about $0.0005 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 2026-08-30.
Other skills, from other repositories
verify-implementation
A workflow that runs a project’s verification skills to produce a report on coding patterns, architecture rules, and project conventions. It is intended for work after implementation, before a pull request, or during code review.
remove-ai-slops
Removes AI-generated code smells from branch changes or an explicit file list behind regression tests. Use when the user asks to clean up, deslop, or remove AI-slop patterns from recent changes.
semgrep-rule-variant-creator
Creates language variants of existing Semgrep rules. Use when porting a Semgrep rule to specified target languages. Takes an existing rule and target languages as input, produces independent rule+test directories for each language.
ln-21-documentation-auditor
Audits documentation and comments for trustworthy claims, coverage, and discoverability. Not for code, test, or architecture audits.
ln-23-test-suite-auditor
Audits existing tests for meaningful coverage, trustworthy oracles, and maintenance value. Not for test implementation or a single delivery review.
ln-41-test-strategy-planner
Plans a risk-based test portfolio and prioritized scenarios without editing tests. Not for test execution or implementation.