Status: Stable
Version: 1.0.0
Author: Rifteo
Tags: workflow
Summary
Read anySKILL.md, score it across five weighted quality dimensions (100 pts total), run a static compatibility analysis against 50+ AI agent profiles, and get a ranked fix list before publishing.
- Scores Trigger quality (25 pts), Instruction clarity (25 pts), Agent-agnostic design (20 pts), Self-containment (15 pts), and Output definition (15 pts). A score of 70+ means publishable; below 50 means silent failures on most agents
- Maps the skill to 7 agent profile types (covering 54 named agents) and assigns Compatible / Partial / Likely broken for each
- Returns a ranked fix list with one specific, actionable improvement per dimension
- Offers to auto-fix the top issue and re-score immediately, or export the report as
skill-benchmark-report.md
SKILL.md file
Discover skill details
Discover skill details
Skill Benchmark
Score a SKILL.md file across quality and compatibility dimensions, then return a ranked fix list for improvement before publishing.When to Use This Skill
Use this skill when the user:- Asks to benchmark, score, or audit a skill (“is my skill any good?”)
- Wants to check cross-agent compatibility (“will this work on Cursor / Windsurf / Gemini?”)
- Needs a ranked fix list before publishing a SKILL.md to the community repo
- Is comparing two skills or evaluating quality before a pull request submission
- Wants to audit a SKILL.md for compatibility before opening a PR
What Does It Check?
The skill performs purely static analysis, no agents are installed or called. It reads the target SKILL.md, applies the scoring rubric fromreferences/rubric.md, checks the skill against agent compatibility signals from references/agent-profiles.md, and produces the report using references/report-template.md.In scope:- Any SKILL.md in the local project or at a specified path
- All 5 scoring dimensions (trigger quality, instruction clarity, agent-agnostic design, self-containment, output definition)
- Compatibility with 54 named AI agents across 7 profile types
- Auto-fix of the top-ranked issue on request
- Runtime testing of the skill against a live agent this is static analysis only
- Scoring SKILL.md files for frameworks other than the Rifteo skills format
How It Works
Step 0: Locate the Target SkillSearch the current directory, then.claude/skills/, .agents/skills/, .cline/skills/, and global skill paths. If multiple files are found, list them and ask the user to confirm.Step 1: IntakeExtract the skill name, description, body line count, presence of bundled resources (scripts/, references/), and any explicit agent target mentions.Step 2: Score Each DimensionApply the rubric from references/rubric.md. For each of the 5 dimensions, record the score, a one-sentence rationale, and the single most impactful fix available.Step 3: Run Compatibility MatrixCheck the skill against each of the 7 agent profiles for hard-fail and soft-fail signals. Assign the worst result across all applicable profiles per named agent (FAIL overrides WARN overrides PASS).Step 4: Generate the ReportFill in the full report template: total score, per-dimension breakdown, compatibility matrix (54 agents grouped into Compatible / Partial / Broken), and ranked fix list.Step 5: Offer Next StepsAfter presenting the report, offer three options: auto-fix the top-ranked issue and re-score; deep-dive into any dimension with full evidence; or export the report as skill-benchmark-report.md.Output
| Score | Meaning |
|---|---|
| 70–100 | Publishable, skill is ready for the community repo |
| 50–69 | Needs work some agents will degrade or skip steps |
| < 50 | Not publishable will silently fail on most agents |
Known Limitations
- Scoring is strict: 70 is the publishable threshold, not 60 or 65
- Compatibility results reflect static signals; a skill rated Partial may still work on some agents depending on their current version
- The auto-fix option applies only the single top-ranked fix per run re-run to address the next issue
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