Skill health lens

Skill Health Lens

Audits Agent Skills against the same checks ModelBound runs in its Skill Development Pipeline — Trust, Token Budget, Duplicates, and Tool Surface — without running the full pipeline.

When to use

• Reviewing or editing any SKILL.md file
• Before publishing a skill to a team marketplace or Cursor Marketplace
• When an agent's skill library has grown past ~10 skills
• When a skill keeps misfiring or being ignored by the agent

What to check

For each SKILL.md in scope:

1. Trust score (target: ≥ 70)

Score the skill 0–100 using ModelBound's @modelbound/skill-trust heuristics:

• Frontmatter completeness — name, description, optional allowed_tools present and well-formed
• Description quality — has a clear "when to use" signal, is between 30 and 400 chars, names concrete triggers
• Instruction shape — uses numbered steps or bullets, no walls of prose, no contradictory rules
• Determinism — avoids vague language ("maybe", "consider", "could") in favor of imperatives
• Provenance — links to source docs or examples where claims are made

Report trust tier:

• Green ≥ 80
• Amber 60–79
• Red < 60

2. Token budget

Estimate tokens (chars / 4) for the skill body and compare against ModelBound's thresholds:

• Green ≤ 2,000 tokens
• Amber 2,000–5,000 tokens
• Red > 5,000 tokens

Flag the top 3 most token-heavy sections and suggest where to split into sub-skills or move into a referenced doc.

3. Duplicate content

For each skill, compare with every other SKILL.md in the workspace using a Jaccard similarity over normalized tokens. Flag pairs with similarity ≥ 0.4 as likely duplicates and ≥ 0.6 as near-clones. Suggest a merge or rename.

4. Tool surface audit

Parse the allowed_tools frontmatter (and any explicit tool mentions in the body). Flag:

• Skills with no allowed_tools field (implicit unrestricted access)
• Skills granting access to destructive tools (run_terminal, delete_file, write_file on broad globs)
• Skills mixing read-only research with mutating actions (split recommendation)

How to run

1. Glob **/SKILL.md from the workspace root (or operate on the currently open file).
2. For each file, compute the four sections above.
3. Produce a single Markdown report grouped by skill, then a workspace-level summary table.
4. End with 3 prioritized fixes the user can apply now (concrete, copy-paste-ready edits).

ModelBound integration

When the user is signed in to ModelBound (via the modelbound MCP server defined in this plugin), also:

• Call skills.list (optionally filtered by category or group_id) and skills.listGroups to pull the user's Skills — including a whole group at once when they say things like "lens my Onboarding group" or "audit every Backend skill".
• Use skills.get / skills.getFile for the full bundle, and platform.exportSkillForIde with group_id to bundle every skill in a group into the IDE in one shot.
• Offer to request an AI review for any skill scoring Red on Trust.
• Surface team-level policies (banned tools, required sections) so they're checked alongside the four core checks.

Note: ModelBound no longer uses the legacy "pack" concept — everything is a Skill, optionally organized into a Skill group. Older packs.* / export_pack_for_ide tool names still work as deprecated aliases but new code should use skills.* and platform.exportSkillForIde.

If the MCP server is unreachable or the user is not signed in, the four core checks still run locally.

Output format

# Skill Health Lens — <N> skills in <workspace>

## Summary
| Skill | Trust | Tokens | Duplicates | Tools |
| --- | --- | --- | --- | --- |
| code-reviewer | 🟢 86 | 🟢 1.2k | — | ✅ scoped |
| pr-helper    | 🟡 71 | 🔴 6.8k | clones code-reviewer (0.62) | ⚠️ unrestricted |

## Top 3 fixes
1. **pr-helper**: split into `pr-summarize` and `pr-review` (current body is 6.8k tokens).
2. **pr-helper**: add `allowed_tools: [read_file, grep]` to drop the unrestricted warning.
3. **pr-helper** ↔ **code-reviewer**: 62% overlap — consider merging or extracting a shared rule.

## Details
...

Out of scope

• Editing skills automatically — the agent surfaces fixes, the user applies them
• Replacing the ModelBound Skill Development Pipeline — this is the local "Test & Optimize" lens only
• Running scoring inside the agent loop on every keystroke — invoke on demand