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Ask

  • 3 installs
  • 134 repo stars
  • Updated June 29, 2026
  • kv0906/pm-kit

Helps with ai & agent building tasks.

About

ask is a Claude Code skill for ai & agent building. It helps solo builders move faster with AI-assisted development.

  • ask
  • AI & Agent Building
  • AI-coding skill

Ask by the numbers

  • 3 all-time installs (skills.sh)
  • Ranked #13,677 of 16,546 AI & Agent Building skills by installs in the Skillselion catalog
  • Data as of Aug 2, 2026 (Skillselion catalog sync)
npx skills add https://github.com/kv0906/pm-kit --skill ask

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Listed on Skillselion
Installs3
repo stars134
Last updatedJune 29, 2026
Repositorykv0906/pm-kit

What it does

Helps with ai & agent building tasks.

Files

SKILL.mdMarkdownGitHub ↗

/ask — Question Answering

Fast answers from your vault. Uses QMD hybrid search (BM25 + vector + rerank) when available, with automatic fallback to vault grep.

Context

Config: @_core/config.yaml

Input

User input: $ARGUMENTS

Processing Steps

Step 1: Detect Search Backend

Check if QMD MCP tools are available in the current session:

1. Try `qmd_status` (MCP tool) to check QMD availability and index state. 2. Evaluate result:

  • QMD available AND has embedded docs → use QMD mode
  • QMD available but 0 embedded docs → use Fallback mode + hint
  • QMD not available (tool missing/error) → use Fallback mode

Step 2: Parse Question

  • Detect project if mentioned
  • Identify question type:
  • "what did we decide about X" → decisions
  • "who's blocked" → blockers
  • "find doc for X" → docs
  • "status of X" → index

Step 3A: QMD Mode (preferred)

When QMD is available and indexed:

1. Deep search: Call qmd_deep_search with the user's question.

  • Use collection filter from config: _core/config.yaml → qmd.collection_name (default: pm-kit)
  • Cap results: use qmd.max_results from config (default: 8)

2. Retrieve top docs: Call qmd_get or qmd_multi_get for the top-scored results.

  • Apply minimum score threshold from config: qmd.min_score (default: 0.35)

3. Synthesize answer: Read the retrieved content and produce a grounded answer with source citations.

Step 3B: Fallback Mode (vault grep)

When QMD is not available or not indexed:

1. Search Strategy

Question TypeSearch Path
Decisionsdecisions/{project}/*.md
Blockersblockers/{project}/*.md
Docsdocs/{project}/*.md, docs/general/*.md
Status01-index/{project}.md
GeneralAll folders

2. Search Methods

  • Filename match first (fastest — naming-as-API)
  • Frontmatter field match
  • Content grep (slower)

Step 4: Return Answer

Output Format

With results

## Answer

{Direct answer — 1-3 sentences, grounded in source content}

### Sources
- `{file-path}` — {brief context why this source is relevant}
- `{file-path}` — {brief context}

### Search Mode
{QMD (deep) | Fallback (vault grep)}

No results

## No Results Found

Searched: {folders or QMD collection}
Query: "{query}"

**Suggestions**:
- Try broader terms
- Check spelling
- Run `qmd embed` if QMD is installed but not indexed

### Search Mode
{QMD (deep) | Fallback (vault grep)}

Fallback mode hint

When running in Fallback mode, append after the answer:

Tip: Install QMD for smarter search with semantic understanding. See handbook/QMD_INTEGRATION.md for setup.

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