Agent skill

skillsmp-skill-recommender

Recommend best-fit SkillsMP skills for a project's stack and delivery goals. Use when building a production skill set for a new project, replacing weak skills with stronger alternatives, auditing current skill coverage, or comparing candidate skills before installation.

Stars 163
Forks 31

Install this agent skill to your Project

npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/other/other/skillsmp-skill-recommender

SKILL.md

SkillsMP Skill Recommender

Select high-fit SkillsMP skills for a specific project and return a practical keep / replace / add plan.

When to Use This Skill

  • Building a production skill set for a new project
  • Replacing low-quality or low-fit skills in an existing stack
  • Comparing candidate skills for the same capability
  • Auditing whether current skills cover required delivery areas
  • Creating a focused install list from a broad project description

Invocation

User invokes this skill by describing the project they want to build.

Examples:

  • "Use $skillsmp-skill-recommender to build a production skill stack for my project."
  • "Use $skillsmp-skill-recommender and suggest skills even if my stack is not finalized yet."

Input Contract (Required)

Extract these fields before ranking:

  • target product type
  • stack and frameworks (or enough constraints to select them)
  • priority outcomes (e.g., speed, security, UX, CI/CD maturity)
  • team level (solo/small team/enterprise-like)
  • existing skill list (optional but preferred)

If enough context is present, continue to search.

If context is insufficient, ask clarifying questions before recommending.

Clarifying Questions (If Needed)

Ask up to 5 short questions, only for missing critical inputs.

Prioritize in this order:

  • what exactly to build
  • required stack/constraints (or whether stack selection is needed first)
  • expected production level
  • must-have areas (security/testing/release/etc.)
  • current skills to replace (if any)

Do not ask questions when the request is already sufficiently specific.

Stack Selection (Required Before Search)

If the user did not provide a finalized stack, you must select one first.

Rules:

  • infer the best-fit implementation stack from the product description, goals, constraints, and team size
  • prefer practical production-ready defaults over exotic combinations
  • state the chosen stack explicitly before skill search
  • only after stack selection, run capability discovery and search queries for that chosen stack
  • do not ask the user to choose stack unless there is a hard technical constraint conflict

Do not skip stack selection when stack is missing or vague.

Capability Discovery (Dynamic)

Build groups from the user intent, not from a fixed preset.

Method:

  • extract nouns (tools/platforms/domains)
  • extract verbs (build/test/deploy/secure/monitor/optimize)
  • merge overlaps into implementation-oriented groups
  • add cross-cutting groups only when needed

Use references/goal-query-map.md only as a seed source. Never force platform- or stack-specific groups unless they are requested or clearly implied.

Search Strategy

Preferred sources:

  1. SkillsMP website UI in MCP Playwright browser
  2. Public SkillsMP docs pages only for orientation, not API querying

Run 2-6 focused queries per discovered group in the site search UI.

Execution method (required):

  • open SkillsMP site with MCP Playwright browser
  • navigate to the skills/catalog search interface
  • if authentication is required, complete it via browser login/session flow
  • type each query manually in the web search input and submit
  • collect candidate skills from rendered UI results
  • do not request or use API tokens for this workflow
  • do not call API endpoints directly from terminal or HTTP client
  • after collecting results, close the Playwright browser session before returning recommendations

Hard restriction:

  • never use curl, fetch, or direct calls to SkillsMP API endpoints such as /api/v1/skills/search or /api/v1/skills/ai-search (token-protected)

Search quality rules:

  • keep queries short and concrete
  • avoid broad mega-queries
  • collect at least top 5 candidates per group
  • deduplicate by id and githubUrl

Filtering and Ranking

Reject candidates when:

  • they are repo-internal maintenance only
  • they are stack-incompatible
  • they are mirrors of a better canonical source
  • description is vague and non-actionable

Use relevance tiers:

  • Tier A: direct fit for high-priority groups
  • Tier B: useful adjacent support
  • Tier C: generic fallback

Rank with this priority:

  1. relevance tier
  2. practical stack fit
  3. stars/forks as quality signal
  4. recency signal if available

Never let stars override relevance.

Decision Rules

For each current skill slot:

  • Replace: clearly better relevance and quality
  • Keep: already strong or no meaningful upgrade
  • Add: missing capability not covered today

When no strong upgrade exists, explicitly keep.

Output Format (Required Order)

Return sections in this exact order:

  1. Discovered Capability Groups
  2. Top Install/Replace List
  3. Replace Matrix
  4. Keep As-Is
  5. Coverage Check
  6. Source Notes

Each recommended skill must include:

  • name
  • source repo/author
  • stars
  • fit rationale
  • priority (P0 / P1 / P2)
  • source note from web UI search context

Quality Bar

  • Prefer canonical upstream sources.
  • Prefer workflow-driven skills over vague guidance.
  • Prefer reusable cross-project skills over one-repo internals.
  • Mark uncertain recommendations explicitly.
  • If context is still insufficient after clarification, say so.

Reference

  • Seed query map (non-exhaustive): references/goal-query-map.md

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