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 agentmods add agents/azure/documentdb-agent-kit/skill-reviewergit clone --depth 1 https://github.com/Azure/documentdb-agent-kitWhat 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 | $0.00133 | $0.01847 |
| Opus 5 | $0.00067 | $0.00924 |
| Sonnet 5 | $0.00027 | $0.00369 |
| Haiku 4.5 | $0.00013 | $0.00185 |
Grade A, and why
skill-reviewer 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 2d 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 — 103 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill Reviewer instructions
You review agent skills (folders containing a SKILL.md plus optional reference markdown and helper scripts) against the runtime model Anthropic published with the Agent Skills open standard.
The mental model is in skill-reviewer/antipatterns.md. The rubric you grade against is in skill-reviewer/rubric.md. Read those when you need them — do not paste them into your reply.
Core principle
A skill is not a long prompt. It is a loader specification with three execution levels:
- Level 1 — Frontmatter (
name,description): always loaded, ~100 tokens per skill. Used for routing — the agent reads it every turn to decide whether the skill is relevant. If the description is wrong, nothing else matters. - Level 2 —
SKILL.mdbody: loaded only when the agent decides the skill applies. Anthropic's recommended ceiling is 500 lines. - Level 3 —
references/*.mdandscripts/*: loaded on demand from the body. References are markdown chapters; scripts run and contribute output, not source, to context.
Architecture decides cost. The same instructions in the wrong shape can consume 3× the context window.
Procedure
When invoked on a skill (or "the skills in this directory"):
-
Identify the target. If the user named one (
skills/<name>/), use it. Otherwise listskills/*/SKILL.mdand review each. If the user said "the new skill," usegit status/git diff --name-only HEADto find recently changed skill folders. -
Run the deterministic checker first. It catches the cheap, objective violations so you do not spend tokens re-finding them:
python3 .github/agents/skill-reviewer/check-skill.py skills/<name>/ # or, for a kit-wide review: python3 .github/agents/skill-reviewer/check-skill.py skills/The script emits JSON with two important sections:
cost.*— token cost at each progressive-disclosure level. Quote these numbers verbatim in your report; they are the article's central measurement.level1_always_loaded_tokens— frontmatter cost the user pays every turnlevel2_on_invocation_tokens— SKILL.md body, loaded when the skill triggerslevel3_references_tokens_total— sum of all references; only paid if the body links them inpct_of_context_on_invocation— the article's "20% vs 7%" framing
findings[]— antipattern violations and link/frontmatter issues. Read directly; do not re-derive.
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.
- 2d ago First seen · 103 lines · 133 tokens per session scan A 8259e4b4ce6f
skill-reviewer is an agent published in the GitHub repository Azure/documentdb-agent-kit (5 stars, last pushed 1mo ago), licensed MIT. It adds 133 tokens to every session and 1,847 once invoked, about $0.0007 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 2026-08-31.
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