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tessleng/skill-insights

Scan a directory or workspace for SKILL.md files across all agents and repos, capture supporting files (references, scripts, linked docs), dedupe vendored copies, enrich each Tessl tile with registry signals, and emit a canonical JSON inventory validated by JSON Schema. Then run four analytical phases in parallel against the inventory — staleness + git provenance (history, broken refs, contributors), quality (Tessl `skill review`), duplicates (similarity + LLM judgement), registry-search (per-standalone-skill registry suggestions, HTTP only) — and render a self-contained interactive HTML report with a top-of-report health overview, top-issues panel, recently-changed list, and per-tessl.json manifests view.

84

1.44x
Quality

90%

Does it follow best practices?

Impact

97%

1.44x

Average score across 2 eval scenarios

SecuritybySnyk

Advisory

Suggest reviewing before use

Overview
Quality
Evals
Security
Files

Evaluation results

100%

24%

Audit Two Specific Repositories — One Consolidated Report

Criteria
Without context
With context

Single consolidated discovery.json

100%

100%

Single consolidated report.html

100%

100%

Discovery includes repo-a

100%

100%

Discovery includes repo-b

100%

100%

Discovery EXCLUDES repo-c

100%

100%

Discovery script invoked once

0%

100%

Discovery command captured in summary

50%

100%

Both --repo flags present

0%

100%

stats.total_repos is 2

50%

100%

All four phase outputs at the consolidated path

100%

100%

Summary confirms repo-c excluded

100%

100%

94%

35%

Audit the Skills in a Repository

Criteria
Without context
With context

discovery.json exists

100%

100%

discovery.json schema 1.3

0%

0%

discovery counts three skills

50%

100%

staleness.json produced

50%

100%

duplicates.json produced

100%

100%

quality.json produced or gracefully failed

100%

100%

report.html rendered

100%

100%

Bundled discovery script invoked

0%

100%

Phases dispatched in parallel

0%

100%

Render runs after all phases

100%

100%

Bundled render script used

0%

100%

Summary mentions correct counts

100%

100%

Graceful degradation surfaced

100%

100%

Evaluated
Agent
Claude
Model
Claude Sonnet 4.6

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