Llm wiki research

LLM Wiki Research

Sub-skill of llm-wiki-pm. Handles research sprints, competitive deep dives, and auto-enrichment of stub entities. Delegates all URL fetching to worker-source-fetcher so privacy filtering and raw/ logging happen correctly.

WebFetch note: WebFetch is listed in allowed-tools for quick page previews (e.g., confirming a URL before delegation). For source capture (saving to raw/), always delegate to worker-source-fetcher. Never call WebFetch directly for source saving — privacy filtering and raw/ logging won't happen.

Orient First

Orient per AGENTS.md before any writes:

1. Read $WIKI/SCHEMA.md
2. Read $WIKI/index.md
3. Read last 20-30 lines of $WIKI/log.md
4. Read $WIKI/overview.md

Research sprints may create many pages. Get user confirmation before creating 5+ pages.

Wiki Path Resolution

WIKI=$(cat .wiki-path 2>/dev/null | tr -d '[:space:]')
WIKI=${WIKI:-${CLAUDE_PLUGIN_OPTION_wiki_path:-${WIKI_PATH:-$(pwd)}}}

Worker Delegation

This skill uses worker-source-fetcher for all URL fetching. Never call WebFetch directly for source capture — always invoke the worker:

"Use worker-source-fetcher to fetch [URL]"

This ensures privacy filtering and raw/ logging happen correctly. The worker returns: "OK: saved to raw/<subdir>/<slug>.md" — use that path for synthesis.

---

Operation 1: Research Sprint

Trigger: "research sprint on [topic]", "deep research on [topic]"

① Define scope

Confirm with user:

• What is the topic / question to answer?
• Target depth: surface / standard / deep
• Time budget (number of sources)

② Wiki-first

grep -r "<topic>" $WIKI --include="*.md" -l

Read all relevant pages. Surface: "Wiki has N relevant pages. Here's what we already know: [...]. Gaps: [...]."

③ Research plan

Present 3-5 specific sources to fetch — name each, why it's relevant, what question it answers. Typical: analyst reports, company pages, recent press, whitepapers, industry forums. Get user confirmation before fetching.

④ Delegate fetching

For each confirmed source: "Use worker-source-fetcher to fetch [URL]"

If a fetch fails (404, paywall, timeout), note it and continue. Collect all raw/ paths before writing.

⑤ Synthesize

Read all fetched raw files. Extract entities, claims, data points. Cross-reference against existing wiki pages. Distinguish:

• Confirmed (claim now in 2+ independent sources)
• New (single source, not yet corroborated)
• Contradicts wiki (surface the conflict explicitly)

⑥ File findings

Create/update wiki pages per core ingest discipline: inline provenance, coverage markers, confidence levels, min 2 outbound wikilinks, backlink audit.

Produce a synthesis page at queries/research-<topic>-<YYYY-MM-DD>/README.md:

---
title: "Research Sprint: <Topic>"
created: YYYY-MM-DD
type: query
tags: [<relevant tags>]
sources: [<raw slugs fetched>]
---

## Scope
Question: [...]  Depth: [...]  Sources: N

## What We Knew
[Pre-sprint wiki state summary]

## What We Learned
[Net-new findings with inline citations]

## Confirmations
[Claims now corroborated by 2+ sources]

## Contradictions
[Conflicts with existing wiki pages — cite both sides]

## Still Unknown
[Gaps that fetching didn't resolve]

## Pages Created/Updated
- [[slug]]: [brief note]

⑦ Surface delta

What did we learn that we didn't know? What did it confirm? What's still unknown? Any contradictions with the wiki?

⑧ Log

## [YYYY-MM-DD] research-sprint | topic: <X> | sources: N | pages created/updated: N

---

Operation 2: Competitive Deep Dive

Trigger: "competitive deep dive on [company]", "full competitive analysis of [company]"

① Read existing coverage

Read $WIKI/entities/<company>.md and related comparisons/ pages. If coverage: comprehensive, ask: "Already marked comprehensive — update pass or fresh deep dive?"

② Research plan

Standard source set:

• Official website (product, pricing, about)
• Recent press (last 6-12 months)
• Job postings (signals: what they're building)
• Pricing page
• G2 / Gartner / Capterra reviews
• Analyst mentions (MQ, Wave, IDC)
• Crunchbase / LinkedIn (funding, headcount)

Get user confirmation before fetching.

③ Delegate fetching

"Use worker-source-fetcher to fetch [URL]" for each confirmed source.

④ Update entity page

Apply diff discipline (core §4): snapshot to _archive/<company>-<date>.md first, show diff before writing, update inline provenance on revised facts, sweep stale variants.

⑤ Update comparison pages

Update existing comparisons/ pages. Offer to create a new comparison page if the deep dive reveals strong differentiation.

⑥ Flag confidence by source

Source type	Confidence
Official website, press releases	verified
Analyst reports, G2 reviews	likely
Forums, social media, unofficial blogs	rumor

⑦ Log

## [YYYY-MM-DD] competitive-deep-dive | entity: <company> | sources: N | pages updated: N

---

Operation 3: Auto-Research (Stub Enrichment)

Trigger: "auto-research [entity]", "enrich [entity]", "fill in [entity] page"

For entity pages with coverage: stub. Fast enrichment pass. This is factual/web enrichment. If the user wants relationship/CRM context (tier, touchpoint, feature asks) rather than facts, route to llm-wiki-crm's enrich instead.

① Read the stub — what do we already know? What's in gaps:?

② Search

WebSearch: "[entity name]" recent news
WebSearch: "[entity name]" official website
WebSearch: "[entity name]" [primary domain, e.g. pricing / funding / product]

③ Extract — description, key facts, relationships to other wiki entities, recent events.

④ Update stub page with inline provenance [source: url, date]. Bump coverage: stub → partial if meaningful data found. Update gaps: and sources: frontmatter. Bump updated:.

Do NOT fabricate. If search returns nothing useful, say so. Leave coverage: stub.

⑤ Log

## [YYYY-MM-DD] auto-research | entity: <X> | coverage: stub→partial | sources: N
# or if nothing found:
## [YYYY-MM-DD] auto-research | entity: <X> | no public data found | coverage: stub (unchanged)

---

Operation 4: Gap Research

Trigger: "research our gaps", "fill coverage gaps", "research stubs"

① Scan for stubs

grep -r "coverage: stub" $WIKI --include="*.md" -l
grep -rL "coverage:" $WIKI/entities $WIKI/concepts --include="*.md"

② Present list — ask user which to prioritize.

③ Run Operation 3 on each selected stub in sequence. Surface result before moving to next: "Enriched [[entity]] — stub → partial. Found: [key facts]. Continue?"

④ Report

Gap research complete:
- N pages enriched (stub → partial)
- N pages still stub (insufficient public data)
- N pages skipped

⑤ Log

## [YYYY-MM-DD] gap-research | stubs scanned: N | enriched: N | still-stub: N

---

Pitfalls

• Orient first: before any writes. Research sprints may touch many pages.
• Delegate fetching: always use worker-source-fetcher — never WebFetch directly for source saving.
• Confirm before mass creates: 5+ pages → user confirmation first.
• Inline provenance: every non-obvious claim needs [source: slug, location].
• Coverage markers: bump coverage: after meaningful enrichment.
• Confidence discipline: verified / likely / rumor — label accordingly.
• No fabrication: if search returns nothing, say so.
• Snapshot before destructive updates: _archive/<slug>-<date>.md before deep dive overwrites.
• Dedup: grep before creating. Confirm update vs create.
• Privacy: private by default — leave customer names, deal sizes, and 1:1 content unflagged (never add shareable: true to them).
• Synthesis page: research sprints always produce queries/research-<topic>-<date>/README.md.