--- name: ask-49ff6efa7fe2 description: Ask the wiki a question, retrieve and synthesize relevant pages, optionally crystallize the answer back into the wiki argument-hint: --- # /ask > Ask a question to the wiki knowledge base. The LLM reads context_brief.md for global context, > retrieves relevant pages, synthesizes an answer with citations. Good answers can be > crystallized back into the wiki — written to outputs/, as new concept pages, or appended > to an existing idea/method/output note — so exploration compounds like ingestion does. ## Inputs - `question`: natural-language question (e.g. "What is the core difference between LoRA and Adapter?") - `--crystallize` (optional): if specified, crystallize the answer back into the wiki (default: answer only, no write) - `--format` (optional): output format, default `markdown`, options: `table` / `timeline` / `bullets` ## Outputs - **Always**: terminal output of synthesized answer (with `[[slug]]` citations) - **If crystallize**: - `wiki/outputs/{query-slug}.md` — query result page (default crystallize target) - or `wiki/concepts/{slug}.md` — if the answer reveals a new cross-paper concept - or appended to an existing `wiki/ideas/{slug}.md` / `wiki/methods/{slug}.md` / `wiki/outputs/{slug}.md` — if the answer adds a finding to an existing entity - updated `wiki/graph/edges.jsonl` (relationships produced by crystallize) - updated `wiki/index.md` and `wiki/log.md` ## Wiki Interaction ### Reads - `wiki/graph/context_brief.md` — global compressed context (ideas, gaps, failed ideas, papers, edges) - `wiki/index.md` — page catalog for locating relevant pages - `wiki/graph/open_questions.md` — open questions, helps identify whether the question touches known gaps - `wiki/papers/*.md` — paper pages relevant to the question - `wiki/concepts/*.md` — concept pages relevant to the question - `wiki/methods/*.md` — method pages relevant to the question - `wiki/topics/*.md` — topic pages relevant to the question - `wiki/people/*.md` — if the question involves specific researchers - `wiki/ideas/*.md` — if the question involves research ideas or failed ideas - `wiki/experiments/*.md` — if the question involves experiment results - `wiki/Summary/*.md` — if the question involves domain-wide landscape ### Writes (crystallize mode only) - `wiki/outputs/{query-slug}.md` — CREATE (query result page) - `wiki/concepts/{slug}.md` — CREATE (newly discovered concept) or EDIT (supplement existing concept) - `wiki/ideas/{slug}.md` / `wiki/methods/{slug}.md` / `wiki/outputs/{slug}.md` — EDIT (append finding to existing page) - `wiki/graph/edges.jsonl` — APPEND (relationships produced by crystallize) - `wiki/graph/context_brief.md` — REBUILD (if crystallize created new pages) - `wiki/graph/open_questions.md` — REBUILD (if crystallize created new pages) - `wiki/index.md` — EDIT (if crystallize created new pages) - `wiki/log.md` — APPEND ### Graph edges created (crystallize only) - `output → paper`: `derived_from` (papers cited in the answer) - `output → concept`: `derived_from` (concepts cited in the answer) - `output → idea` / `output → method`: `derived_from` (ideas or methods cited in the answer) - `concept → paper`: `supports` (if a new concept is generalized from papers) ## Workflow **Precondition**: confirm working directory is the wiki project root (containing `wiki/`, `raw/`, `tools/`). Set `WIKI_ROOT=wiki/`. ### Step 1: Load Global Context 1. Read `wiki/graph/context_brief.md` — get compressed snapshot of wiki's current knowledge (ideas, gaps, papers, edges) 2. Read `wiki/graph/open_questions.md` — understand known open questions and knowledge gaps 3. If both are missing, rebuild first: ```bash python3 tools/research_wiki.py rebuild-context-brief wiki/ python3 tools/research_wiki.py rebuild-open-questions wiki/ ``` ### Step 2: Retrieve Relevant Pages 1. Read `wiki/index.md`, match relevant slugs against question keywords 2. Extract ideas, methods, and papers semantically related to the question from context_brief.md 3. Sort by relevance, select top-K pages (K ≤ 15 to avoid exceeding context window) 4. Read full content of selected pages 5. If the question involves relationships (e.g. "difference between X and Y"), additionally read edges connecting X and Y from `wiki/graph/edges.jsonl` ### Step 3: Synthesize Answer 1. Synthesize an answer to the user's question based on collected page content 2. Answer requirements: - **Cited**: every key statement must include a `[[slug]]` wikilink pointing to its source page - **Structured**: organize output according to `--format` parameter (markdown / table / timeline / bullets) - **Acknowledge uncertainty**: clearly flag "insufficient evidence in wiki" for parts with weak support - **Flag knowledge gaps**: if the question touches a known gap in open_questions.md, call it out explicitly - **Cite idea status**: when referencing ideas, note their `status` and `novelty_score` 3. If the question exceeds the wiki's current knowledge, honestly say so and suggest: - which papers to ingest to fill the gap - possible search directions (arXiv keywords, Semantic Scholar queries) ### Step 4: Assess Crystallize Value 1. Judge whether the answer is worth writing back to the wiki (make a recommendation even if `--crystallize` was not specified) 2. Signals that crystallize is worthwhile: - The answer synthesizes information from multiple papers, forming a new cross-paper insight - The answer reveals a concept not yet explicitly recorded in the wiki - The answer adds a finding that strengthens an existing idea, method, or output note - The answer addresses a known gap in open_questions.md 3. Signals that crystallize is not worthwhile: - The answer merely restates the content of a single page - The question is a simple factual lookup (e.g. "What year was LoRA published?") - The answer relies primarily on inference rather than wiki evidence 4. Append a crystallize recommendation at the end of the answer: ``` 💡 Crystallize recommendation: [worthwhile / not needed] — [reason] ``` ### Step 5: Crystallize Back to Wiki (if user confirms or --crystallize was specified) Choose the crystallize target based on answer content: **Case A — Write to outputs/ (default):** 1. Generate slug: `python3 tools/research_wiki.py slug ""` 2. Create `wiki/outputs/{query-slug}.md`: ```yaml --- title: "" slug: "" query: "" # original question source_pages: [] # slugs of all pages cited in the answer date_created: YYYY-MM-DD --- ``` Body is the answer content (preserve wikilinks) 3. Add a graph edge for each cited source page: ```bash python3 tools/research_wiki.py add-edge wiki/ --from outputs/ --to papers/ --type derived_from --evidence "query answer" ``` **Case B — Create new concept:** 1. If the answer reveals a new concept: create `wiki/concepts/{slug}.md` using the CLAUDE.md concept template 2. maturity: emerging 3. key_papers: extracted from answer citations 4. Add graph edges (concept → papers) 5. Append reverse links to relevant paper pages under `## Related` **Case C — Append finding to an existing idea, method, or output note:** 1. If the answer extends a finding tied to an existing entity, append a short paragraph (with `[[slug]]` citations) to the appropriate section: - `wiki/ideas/{slug}.md` → `## Lessons learned` or `## Pilot results` - `wiki/methods/{slug}.md` → `## Limitations` or `## Tradeoff profile` - `wiki/outputs/{slug}.md` → end of the body 2. Add graph edges from the touched page to the cited papers/concepts/methods (`derived_from`) 3. Do not create a new entity; this case only enriches an existing one ### Step 6: Update Navigation and Graph (crystallize only) 1. **index.md**: append new page entries under the appropriate category 2. **log.md**: ```bash python3 tools/research_wiki.py log wiki/ "ask | | crystallized: " ``` If not crystallized: ```bash python3 tools/research_wiki.py log wiki/ "ask | | answer-only" ``` 3. **Rebuild derived graph files** (only if crystallize created new pages): ```bash python3 tools/research_wiki.py rebuild-context-brief wiki/ python3 tools/research_wiki.py rebuild-open-questions wiki/ ``` ### Step 7: Report to User Output a summary including: - Number and list of retrieved pages - Answer (with citations and formatting) - Knowledge gap annotations (if any) - Crystallize recommendation or execution result - Follow-up suggestions (papers recommended for ingestion, related open questions) ## Constraints - **No fabrication**: answers must be grounded in actual wiki content; do not invent from LLM pre-training knowledge - **Citations must exist**: every `[[slug]]` must point to a page that actually exists in the wiki - **raw/ is read-only**: do not modify files under `raw/` - **graph/ only via tools**: do not hand-edit files under `graph/` - **Crystallize requires confirmation**: unless the user explicitly specifies `--crystallize`, only recommend but do not write - **Context limit**: retrieve at most 15 pages to stay within context window - **Cite idea status**: when referencing ideas, always note their `status` and `novelty_score` - **Flag gaps**: if the question touches a known gap in open_questions.md, explicitly call it out - **outputs/ frontmatter must include query and source_pages**: ensures traceability ## Error Handling - **context_brief.md missing**: run `python3 tools/research_wiki.py rebuild-context-brief wiki/` to rebuild, then retry - **wiki is empty**: inform the user to first run `/init` or `/ingest` to build the knowledge base - **no matching pages**: honestly report that no relevant content exists in the wiki, suggest search and ingest directions - **crystallize slug conflict**: append a numeric suffix (e.g. `query-result-2`) - **index.md missing**: run `python3 tools/research_wiki.py init wiki/` to initialize, then retry ## Dependencies ### Tools（via Bash） - `python3 tools/research_wiki.py slug ""` — slug generation - `python3 tools/research_wiki.py add-edge wiki/ --from <id> --to <id> --type <type> --evidence "<text>"` — add graph edge - `python3 tools/research_wiki.py rebuild-context-brief wiki/` — rebuild compressed context - `python3 tools/research_wiki.py rebuild-open-questions wiki/` — rebuild knowledge gap map - `python3 tools/research_wiki.py log wiki/ "<message>"` — append log entry - `python3 tools/research_wiki.py init wiki/` — initialize wiki (fallback) ### Skills（via Skill tool） - `/ingest` — referenced when suggesting the user supplement knowledge ### Shared References - `.claude/skills/shared-references/citation-verification.md` (created in Phase 3) --- Source attribution: skyllwt Original project: https://github.com/skyllwt/OmegaWiki/blob/f676a037d498563b41dc27fdc884eda3a047c030/.claude/skills/ask/SKILL.md License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Dave AI. ## Upstream license notice MIT License Copyright (c) 2026 OmegaWiki Contributors Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Dave AI resource metadata Creator / publisher: skyllwt Original project: https://github.com/skyllwt/OmegaWiki/blob/f676a037d498563b41dc27fdc884eda3a047c030/.claude/skills/ask/SKILL.md Attribution basis: Source repository record Fields of work: Data & Analytics, Education & Research, Marketing Roles: Content Marketer, Data Analyst, Researcher Specialties: Specialty not specified Tools: Wiki Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: e7fa94a128221451050d29bfc1ab21cd03e575b6d5315c25e0f8e547543447fc