Borrowing it
Nothing to install: this file belongs to redhat-community-ai-tools/harness-eval. 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/redhat-community-ai-tools/harness-eval/main/.cursor/commands/skill-review.mdgit clone --depth 1 https://github.com/redhat-community-ai-tools/harness-evalWrote 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/commands/redhat-community-ai-tools/harness-eval/skill-review)<a href="https://agentmods.dev/commands/redhat-community-ai-tools/harness-eval/skill-review"><img src="https://agentmods.dev/badge/commands/redhat-community-ai-tools/harness-eval/skill-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/commands/redhat-community-ai-tools/harness-eval/skill-review"><img src="https://agentmods.dev/badge/commands/redhat-community-ai-tools/harness-eval/skill-review.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 | $0.00000 | $0.00596 |
| Opus 5 | $0.00000 | $0.00298 |
| Sonnet 5 | $0.00000 | $0.00119 |
| Haiku 4.5 | $0.00000 | $0.00060 |
Grade A, and why
skill-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 10d 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 — 70 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Eval Skill
Deep-evaluate a single skill with static analysis and qualitative review, both individually and in context of the full setup.
Arguments
$ARGUMENTS should be the skill name or path to the skill directory.
Hard Rules
- Never give a verdict without running the checks and reading the actual content.
- Every rubric category must be checked.
- Don't manufacture problems. If the skill is good, say so.
Step 1: Ask Output Preference
Ask the user: print the report in conversation, or write to a file?
Step 2: Select the Skill
Determine the skill path from $ARGUMENTS. If the user says a skill name, find it under skills/<name>/SKILL.md or .cursor/skills/<name>/SKILL.md.
Step 3: Run Lint
uvx --from harness-eval harness-eval skill-review <skill-path> --context .
If uvx is not available, fall back to pip install harness-eval and use harness-eval directly.
Read the output for diagnostics, token count, and contextual findings.
Step 4: Read Actual Files
Read:
- The skill's SKILL.md file
- All files in the skill's subdirectories (reference files)
- All OTHER skill SKILL.md files in the workspace (for context)
- System instructions (.cursor/rules/*.mdc, .cursorrules, CLAUDE.md)
Step 5: Qualitative Review
Evaluate the skill against these categories:
- Specificity: vague platitudes vs actionable patterns
- Redundancy: duplicates the AI's default behavior?
- Trigger quality: description missing/broad/narrow?
- Token efficiency: over 500 lines? Should be split?
- Instruction clarity: contradictions, vague language, hedging?
- Content quality: no structure, no examples, missing edge cases?
For each issue found, cite specific evidence from the content.
Step 6: Contextual Analysis
Check redundancy against three sources:
- The AI's default behavior (generic advice = redundant)
- Other skills in the workspace (overlap = partially redundant)
- System instructions content (duplication = wasted tokens)
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.
- 10d ago First seen · 70 lines · 0 tokens per session scan A 871caa440e3f
skill-review is a command published in the GitHub repository redhat-community-ai-tools/harness-eval (27 stars, last pushed 2d ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 596 tokens. 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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handoff
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lians-recall
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t800-onboard
A command for newcomers that explains what is installed in Cursor globally and in the current project, plus what the T-800 team can do.