MultiPhysicsVault/.agents/skills/wiki-query/SKILL.md

name, description

name

description

wiki-query

Answer questions by searching the compiled Obsidian wiki. Use this skill when the user asks a question about their knowledge base, wants to find information across their wiki, asks "what do I know about X", "find everything related to Y", or wants synthesized answers with citations from their wiki pages. Also use when the user wants to explore connections between topics in their wiki. Works from any project. Includes an index-only fast mode triggered by "quick answer", "just scan", "don't read the pages", "fast lookup" — returns answers from page summaries and frontmatter without reading page bodies.

Wiki Query — Knowledge Retrieval

You are answering questions against a compiled Obsidian wiki, not raw source documents. The wiki contains pre-synthesized, cross-referenced knowledge.

Before You Start

1. Resolve config — follow the Config Resolution Protocol in llm-wiki/SKILL.md (walk up CWD for .env → ~/.obsidian-wiki/config → prompt setup). Prefer ~/.obsidian-wiki/config for cross-project queries when present, even if it is a symlink to the vault .env. This gives OBSIDIAN_VAULT_PATH and any QMD variables. Works from any project directory.
2. Load QMD settings from the resolved config before deciding retrieval strategy. If QMD_WIKI_COLLECTION is set, treat QMD as available subject only to transport/tool checks below. If it is empty or unset, say briefly why QMD is being skipped before using grep/page reads.
3. If $OBSIDIAN_VAULT_PATH/hot.md exists, read it first — it gives you instant context on recent activity. If the user's question is about something ingested recently, hot.md may answer it before you even open index.md.
4. Read $OBSIDIAN_VAULT_PATH/index.md to understand the wiki's scope and structure

Visibility Filter (optional)

By default, all pages are returned regardless of visibility tags. This preserves existing behavior — nothing changes unless the user asks for it.

If the user's query includes phrases like "public only", "user-facing", "no internal content", "as a user would see it", or "exclude internal", activate filtered mode:

• Build a blocked tag set: {visibility/internal, visibility/pii}
• In the Index Pass (Step 2), skip any candidate whose frontmatter tags contain a blocked tag
• In Section/Full Read passes (Steps 3–4), do not read or cite any blocked page
• Synthesize the answer only from allowed pages — do not mention that excluded pages exist

Pages with no visibility/ tag, or tagged visibility/public, are always included.

In filtered mode, note the filter in the Step 6 log entry: mode=filtered.

Retrieval Protocol

Follow the Retrieval Primitives table in llm-wiki/SKILL.md. Reading is the dominant cost of this skill — use the cheapest primitive that answers the question and escalate only when it can't. Never jump straight to full-page reads.

Step 1: Understand the Question

Classify the query type:

• Factual lookup — "What is X?" → Find the relevant page(s)
• Relationship query — "How does X relate to Y?" / "What contradicts X?" → Find both pages, their cross-references, and their relationships: frontmatter blocks for typed edges
• Synthesis query — "What's the current thinking on X?" → Find all pages that touch X, synthesize
• Gap query — "What don't I know about X?" → Find what's missing, check open questions sections

Also decide the mode:

• Index-only mode — triggered by "quick answer", "just scan", "don't read the pages", "fast lookup". Stops at Step 3. Answers from frontmatter + index.md only.
• Normal mode — the full tiered pipeline below.

Step 2: Index Pass (cheap)

Build a candidate set without opening any page bodies:

• You've already read index.md above — use it as the first filter. It lists every page with a one-line description and tags.
• Use Grep to scan page frontmatter only for title, tag, alias, and summary matches. A pattern like ^(title|tags|aliases|summary): scoped to vault .md files is far cheaper than content grep.
• Collect the top 5–10 candidate page paths ranked by:
  1. Exact title or alias match
  2. Tag match
  3. Summary field contains the query term
  4. index.md entry contains the query term
• Apply tier ordering within each rank bucket: when two candidates score equally, prefer tier: core over tier: supporting over tier: peripheral. Read the tier: frontmatter field with the same cheap grep as other fields. Pages without a tier: field are treated as supporting.

If you're in index-only mode, stop here. Answer from summary: fields, titles, and index.md descriptions only. Label the answer clearly: "(index-only answer — page bodies not read; facts below are from page summaries and may miss nuance)". Then skip to Step 5.

Step 2b: QMD Semantic Pass (optional — requires QMD_WIKI_COLLECTION in resolved config)

GUARD: If $QMD_WIKI_COLLECTION is empty or unset after config resolution, skip this entire step and proceed to Step 3. Mention the missing variable in your working update.

No QMD? Skip to Step 3 and use Grep directly on the vault. QMD is faster and concept-aware but the grep path is fully functional. See .env.example for setup.

If QMD_WIKI_COLLECTION is set, run QMD before reaching for Grep unless the question is already fully answered by hot.md or index.md metadata. QMD is especially preferred when the question is semantic, project-specific, asks for related context, or uses terms that may not appear verbatim in titles/frontmatter.

Choose the QMD transport from $QMD_TRANSPORT:

• mcp (default): use the QMD MCP tool configured in the agent.
• cli: run the local qmd CLI. Use $QMD_CLI if set; otherwise use qmd.

For detailed CLI command selection, maintenance, and VM caveats, use the local $qmd-cli skill when it is installed.

If the selected transport is unavailable (no MCP tool, qmd not on PATH, or the command errors), skip QMD and continue with Step 3.

For MCP transport:

mcp__qmd__query:
  collection: <QMD_WIKI_COLLECTION>   # e.g. "knowledge-base-wiki"
  intent: <the user's question>
  searches:
    - type: lex    # keyword match — good for exact names, file paths, error messages
      query: <key terms>
    - type: vec    # semantic match — good for concepts, patterns, "what is X like"
      query: <question rephrased as a description>

For CLI transport, pick the command from $QMD_CLI_SEARCH_MODE:

Keep operator-like or punctuation-heavy tokens such as no-sudo, ansible_become=false, and ~/.local/bin in the lex: line. Rewrite the vec: line as plain natural language without hyphenated -term words; QMD treats -term as negation, and negation is not supported in vec/hyde queries.

• quality (default): best relevance; slower on CPU.

  ${QMD_CLI:-qmd} query $'lex: <key terms>\nvec: <question rephrased as a description>' -c "$QMD_WIKI_COLLECTION" -n 8 --files
  

• balanced: hybrid search without LLM reranking; use when quality is too slow.

  ${QMD_CLI:-qmd} query $'lex: <key terms>\nvec: <question rephrased as a description>' -c "$QMD_WIKI_COLLECTION" -n 8 --no-rerank --files
  

• fast: semantic-only recall, or search instead when exact names, file paths, or error messages matter.

  ${QMD_CLI:-qmd} vsearch "<question rephrased as a description>" -c "$QMD_WIKI_COLLECTION" -n 8 --files
  

Use ${QMD_CLI:-qmd} get "#docid" to retrieve a ranked document by docid when CLI output provides one.

The returned snippets or ranked files act as pre-read section summaries. If they answer the question fully, skip Step 3 and go straight to Step 4 (reading only the pages QMD ranked highest). If not, use the ranked file list to guide which files to grep or read in Step 3.

Also search papers when the question may have source material in _raw/:

If QMD_PAPERS_COLLECTION is set and the user is asking about a topic likely covered by ingested papers (research, theory, background), run a parallel search against the papers collection. Cite raw sources separately from compiled wiki pages in your answer.

Step 3: Section Pass (medium cost — only if Steps 2/2b are inconclusive)

For each of the top candidates, pull the relevant section without reading the whole page:

• Use Grep -A 10 -B 2 "<query-term>" <candidate-file> to get just the lines around the match.
• This usually returns 15–30 lines per hit instead of 100–500.
• If the section grep gives a clear answer, go straight to Step 5.

Step 4: Full Read (expensive — last resort)

Only when Steps 2 and 3 don't answer the question:

• Read the top 3 candidates in full. When choosing which 3 to read, apply tier ordering: read core pages before supporting, and skip peripheral pages unless they are the only match.
• Follow at most one hop of [[wikilinks]] from those pages if the answer requires cross-references.
• For relationship queries ("How does X relate to Y?" / "What contradicts X?"): also read the relationships: frontmatter block of the candidate pages. Each entry gives a typed, directional edge (extends, implements, contradicts, derived_from, uses, replaces, related_to). Surface these explicitly in your answer — "Page A contradicts Page B (typed edge)" is more useful than "Page A links to Page B".
• Check "Open Questions" sections for known gaps.
• If you're still short, then fall back to a broad content grep across the vault. Tell the user you escalated — this is the expensive path and they should know.

Step 5: Synthesize an Answer

Compose your answer from wiki content:

• Cite specific wiki pages using [[page-name]] notation
• Note which step the answer came from ("found in summary" vs "grepped section" vs "full page read") — helps the user understand confidence
• If the wiki has contradictions, present both sides
• If the wiki doesn't cover something, say so explicitly
• Suggest which sources might fill the gap

Page trust annotations: For every page cited in your answer, check its lifecycle frontmatter and compute is_stale = (today − updated) > 90 days. Annotate risky pages inline so the user knows which citations to verify:

Condition	Annotation
lifecycle: archived	(ARCHIVED: superseded by [[target]]) — use the successor instead
lifecycle: disputed	(DISPUTED, marked <lifecycle_changed>: <lifecycle_reason or "reason unspecified">)
is_stale + lifecycle: verified	(VERIFIED but stale: last updated <updated>) — reader should re-verify before relying
is_stale (other lifecycle)	(stale: last updated <updated>)

Examples in a synthesized answer:

[[concept-page]] (stale: last updated 2026-01-15) — Original claim was X.
[[verified-page]] (VERIFIED but stale: last updated 2025-09-10) — Reader should reverify before relying.
[[disputed-page]] (DISPUTED, marked 2026-04-30: contradicted by [[new-source]]) — Earlier said Y, now uncertain.
[[old-page]] (ARCHIVED: superseded by [[new-page]]) — Use the successor.

Pages with no lifecycle field (legacy pages predating the schema) are treated the same as draft — annotate if stale, skip otherwise. Never fabricate a lifecycle_reason; if the field is absent, omit the reason from the annotation.

Step 6: Log the Query

Append to log.md:

- [TIMESTAMP] QUERY query="the user's question" result_pages=N mode=normal|index_only|filtered escalated=true|false

Answer Format

Structure answers like this:

Based on the wiki:

[Your synthesized answer with wikilinks to source pages]

Pages consulted: page-a, page-b, page-c

Gaps: [What the wiki doesn't cover that might be relevant]