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 majiayu000/spellbook --skill review-agent-harnessgit clone --depth 1 https://github.com/majiayu000/spellbookWrote 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/majiayu000/spellbook/review-agent-harness)<a href="https://agentmods.dev/skills/majiayu000/spellbook/review-agent-harness"><img src="https://agentmods.dev/badge/skills/majiayu000/spellbook/review-agent-harness/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/majiayu000/spellbook/review-agent-harness"><img src="https://agentmods.dev/badge/skills/majiayu000/spellbook/review-agent-harness.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 | $0.00090 | $0.02984 |
| Opus 5.5 | $0.00036 | $0.01194 |
| Sonnet 5.5 | $0.00018 | $0.00597 |
| Haiku 4.5 | $0.00009 | $0.00298 |
Grade A, and why
review-agent-harness 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.
How it starts
The opening of the file, as written. The whole thing — 302 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Review Agent Harness
Review the operating system around coding agents, not only its files. Separate declared assets, reachable routes, observed task use, and later outcomes.
Route And Scope
Resolve the directory containing this SKILL.md before running its scripts.
Select one mode:
static: inspect the target repository only. Use by default.episode: add explicitly authorized Codex or Claude Code JSONL sources.longitudinal: compare a validated report with the existing ledger.
Default to inline, read-only output. Write a durable report under the target only when the user explicitly requests an artifact or historical tracking. Never discover user-home Sessions, read Memory bodies, or inspect another provider merely because its files are available.
Use adjacent skills instead when their narrower owner is sufficient:
codebase-auditfor code defects and architecture health;repo-agent-context-auditfor AGENTS, Skills, and Specs alone;skill-lifeguardfor one Skill's reliable contract;flowguardfor running a long task;review-gatebefore landing an agent-generated diff.
Step 1: Freeze The Evidence Boundary
Record target, mode, provider, locale, decision, acceptance boundary, output
mode, included sources, excluded sources, and unavailable evidence. Treat a
missing required source as unobserved; do not substitute a broader directory,
another provider, or remembered results.
Resolve the target before interpreting assets or assigning scores. The collector
classifies it as exact_git_root, inside_git_worktree,
contains_nested_git_root, or non_git_directory. If the supplied directory
contains a nested Git root, stop and retarget that exact repository; do not
score the parent as though it were the project. For a Git target, the collector
uses Git's tracked and untracked inventory and excludes ignored worktrees and
prior review output from repository evidence.
Run static collection from the installed Skill directory:
What ships with it
13 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.
- agents/openai.yaml 264 B
- evals/evals.json 7.8 KB
- references/finding-contract.md 6.8 KB
- references/privacy-boundary.md 2.2 KB
- references/repair-loop.md 2.9 KB
- references/review-model.md 4.9 KB
- references/session-adapters.md 2.5 KB
- scripts/collect_evidence.py 22 KB runs code
- scripts/harness_common.py 14 KB runs code
- scripts/render_report.py 20 KB runs code
- scripts/session_adapters.py 17 KB runs code
- scripts/update_ledger.py 15 KB runs code
- scripts/validate_findings.py 30 KB runs code
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 · 302 lines · 90 tokens per session scan A c74ce96d46e5
review-agent-harness is a skill published in the GitHub repository majiayu000/spellbook (288 stars, last pushed 3d ago), licensed MIT. It adds 90 tokens to every session and 2,984 once invoked, about $0.0004 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.
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