Borrowing it
Nothing to install: this file belongs to dominik1001/caldav-mcp. 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/dominik1001/caldav-mcp/main/.agents/skills/review-agents-md/SKILL.mdgit clone --depth 1 https://github.com/dominik1001/caldav-mcpWrote 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/dominik1001/caldav-mcp/review-agents-md)<a href="https://agentmods.dev/skills/dominik1001/caldav-mcp/review-agents-md"><img src="https://agentmods.dev/badge/skills/dominik1001/caldav-mcp/review-agents-md/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/dominik1001/caldav-mcp/review-agents-md"><img src="https://agentmods.dev/badge/skills/dominik1001/caldav-mcp/review-agents-md.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 | $0.00059 | $0.01163 |
| Opus 5 | $0.00030 | $0.00581 |
| Sonnet 5 | $0.00012 | $0.00233 |
| Haiku 4.5 | $0.00006 | $0.00116 |
Grade A, and why
review-agents-md 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 12d 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 — 79 lines — stays where its author put it; the contents beside it link to each section on GitHub.
The meta-principle: a context file earns its tokens. Every line must tell the agent something it can't infer from the code, the formatter, or the linter. If a line could be deleted without the agent getting worse, delete it.
Workflow
- Read the target file (default:
AGENTS.md, thenCLAUDE.mdat repo root). If both exist, review both. - Evaluate against each rubric below. For every issue, quote the offending lines and propose a concrete rewrite — not just a critique.
- Report findings grouped by severity: Cut (delete), Rewrite (fix in place), Add (missing required content).
- End with a one-line verdict: total lines now vs. proposed, and whether the tech stack is mentioned.
Do not auto-edit the file unless the user asks. Surface the diff first.
Rubric
Length & signal density
Good: Short and load-bearing. Every line is non-obvious and would change agent behavior if removed. Bad: Long preambles, restated obvious facts ("we use TypeScript"), or generic engineering advice. Flag any file over ~200 lines as suspect and identify the lowest-signal sections to cut.
Non-obvious content only
Good: Conventions, architecture choices, tooling quirks ("we use bun, not node"; "migrations run via make db-migrate, not the ORM CLI").
Bad: Anything derivable from package.json, Cargo.toml, file extensions, or a five-second skim of the repo. Cut it.
Tech stack is mentioned
Good: The language/runtime/framework choices the agent would otherwise guess wrong are mentioned somewhere in the file (e.g., "Bun 1.x runtime, not Node"; "Postgres 16 + Drizzle, not Prisma"). A dedicated ## Tech Stack section is fine but not required — a one-liner near the top works too.
Bad: No mention at all of the non-obvious stack choices, so the agent has to infer them from manifests. If missing, propose a short addition drafted from the repo's manifests.
Auto-generated content
Good: Hand-written, curated.
Bad: Looks /init-generated — boilerplate headings, file-tree dumps, restatement of package.json scripts. Auto-generated context files measurably reduce success rates. Recommend deletion and replacement with a hand-written file.
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.
- 12d ago First seen · 79 lines · 59 tokens per session scan A 4d4f2663e2b5
review-agents-md is a skill published in the GitHub repository dominik1001/caldav-mcp (100 stars, last pushed 5d ago), licensed MIT. It adds 59 tokens to every session and 1,163 once invoked, about $0.0003 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.
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Detects code smells, anti-patterns, and readability issues. Use when implementing features, reviewing code, or refactoring.