# Research Knowledge Orchestrator

> Route a research or knowledge task to the right skill among 12 specialists — current-web research, neural discovery, multi-source cited synthesis, systematic literature review, scholarly evaluation, biomedical/patent/genomic databases, codebase onboarding, guided code tours, live docs lookup, and persistent project memory. USE WHEN a user wants to research, investigate, review the literature, look something up, or understand a codebase but hasn't named the specific tool.

- Skill: `sheshiyer/research-knowledge-orchestrator` (Agent Skill)
- Install (CLI): `npx skillmds@latest add sheshiyer/research-knowledge-orchestrator`
- Raw SKILL.md: https://api.skillmd.com/api/skills/sheshiyer/research-knowledge-orchestrator/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Product & Planning
- Author: Sheshiyer (https://skillmd.com/u/sheshiyer)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/sheshiyer/research-knowledge-orchestrator

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# Research & Knowledge Orchestrator

The single entry skill for research and knowledge work. It locates the task on the
**question type × evidence depth** map and delegates to one of 12 specialist spokes. The
cross-cutting discipline every spoke shares — pick the *lightest* evidence lane that answers
the question, label every claim by provenance, and escalate only when synthesis demands it —
lives in `research-knowledge-core`; read it before promising coverage or mixing sources.

## Routing map (intent → spoke)

**Operate a research pass (start here when the lane is unclear)**
- "Research this", "compare", "what's the latest", recurring lookup → `research-ops` *(operator wrapper; chooses the lane below)*

**Current-web research**
- Fast discovery / web · code · company · people lookup → `exa-search`
- Thorough, cited, multi-source report ("deep dive", "current state of") → `deep-research`

**Academic & scientific literature**
- Find · screen · synthesize · cite a body of literature → `scientific-thinking-literature-review`
- Judge a paper / proposal / methods section / evidence quality → `scientific-thinking-scholar-evaluation`
- Biomedical literature, MeSH, PMID, E-utilities → `scientific-db-pubmed-database`
- Patents & trademarks, official IP records → `scientific-db-uspto-database`
- Genomic database queries, sequence lookup, enrichment → `scientific-pkg-gget`

**Understand a codebase**
- Map an unfamiliar repo → onboarding guide + starter CLAUDE.md → `codebase-onboarding`
- Author a step-by-step `.tour` walkthrough (onboarding / PR / RCA) → `code-tour`

**Reference & memory**
- Up-to-date library/framework docs (named framework, API, setup) → `documentation-lookup`
- Persist project context across sessions; resume where you left off → `ck`

## Folded spokes (content extraction, scraping, transcripts, monitoring)

Routable spokes folded into this cluster. They cover the *acquisition and
distillation* lanes — getting raw content out of the web/feeds/recordings and
turning it into structured, summarized evidence — feeding the research lanes above.

**General research & content distillation**
- Three-mode research (quick/standard/extensive) + content extraction; 240+ Fabric patterns → `research`
- Current open-web search with source extraction and evidence gathering → `web-search`
- Apply a named Fabric pattern (extract wisdom, summarize, threat model, etc.) to content → `fabric`
- Summarize/transcribe a URL, podcast, or local file (text + transcript fallback) → `summarize`

**Parse & extract structured content**
- Parse URLs, files, videos, PDFs, articles to structured JSON (entities, transcripts, batch) → `parser`
- Fetch + summarize a YouTube video's transcript (proxy-backed for cloud IP blocks) → `youtube-transcript`

**Scrape the web at scale**
- Social-media / e-commerce / business-data scraping via Apify actors (Twitter, IG, LinkedIn, TikTok, Maps, Amazon) → `apify`
- Progressive, tiered URL scraping via Bright Data → `brightdata`

**Meetings & feeds monitoring**
- Analyze meeting transcripts/recordings for communication patterns and actionable feedback → `meeting-insights-analyzer`
- Interact with Fireflies meeting data and the Fireflies API (via Membrane) → `fireflies`
- Monitor blogs and RSS/Atom feeds for updates (blogwatcher CLI) → `blogwatcher`

## Standard Operating Flow

1. Classify the ask: which **question type** (current fact · comparison · literature · IP · genomic · codebase · API reference · resume context) and which **evidence depth**.
2. Take the **lightest useful lane first** — local/docs/memory before web, `exa-search` before `deep-research`, scoping review before systematic. The model and escalation ladder are in `research-knowledge-core`.
3. Delegate to the spoke(s). Multi-step asks fan out in evidence order (e.g. "review the literature and rate the key paper" → `scientific-thinking-literature-review` → `scientific-thinking-scholar-evaluation`).
4. Return: chosen spoke(s), the evidence lane used, claims labeled by provenance (sourced fact / supplied context / inference / recommendation), dates on freshness-sensitive answers, and the next action.

## Guardrails

See `research-knowledge-core`. In short: **evidence-tier discipline** — never answer a current
question from stale memory when a fresh search is cheap; never mix inference into sourced facts
without labeling it; never spin up a heavyweight research pass when local code, docs, or `ck`
memory already hold the answer; always date freshness-sensitive claims and name your sources.
The cluster's value is *trustworthy, traceable* answers — don't trade that for speed.

## Boundaries

- **"Research this" — which spoke?** Default a bare, lane-unclear "research this" to
  `research-ops`, which picks the rung. Skip it and go direct when the lane is already obvious:
  `deep-research` for a thorough multi-source cited report on a *general/web* topic; the
  `scientific-*` spokes (`scientific-thinking-literature-review`, `-scholar-evaluation`,
  `scientific-db-pubmed-database`, `-uspto-database`, `scientific-pkg-gget`) when the subject is
  *academic / biomedical / IP / genomic* and needs scholarly rigor or a citable database.
- **Codebase, not the web.** `codebase-onboarding` and `code-tour` operate on *this* repo's own
  source — mapping it and authoring `.tour` walkthroughs. They are not web research; for external
  topics use `deep-research` / `exa-search` instead.

## Picked-up spokes (knowledge-base authoring & scientific computation)

Vetted additions from the antigravity-awesome-skills library (MIT). They extend two
lanes the cluster was thin on: **personal knowledge-base authoring** (Obsidian vaults)
and **exact scientific computation**, plus one more content-extraction tool.

**Knowledge base authoring (Obsidian)**
- Write/edit Obsidian Flavored Markdown — wikilinks, embeds, callouts, properties → `obsidian-markdown`
- Build `.base` database-views (filters, formulas, table/card/list) over notes → `obsidian-bases`
- Author/edit `.canvas` visual maps, mind maps, knowledge graphs (JSON Canvas 1.0) → `json-canvas`
- Shell-driven vault ops + plugin/theme dev against a running Obsidian → `obsidian-cli`

**Scientific computation (turn sourced data into exact results)**
- Symbolic math: solve equations, calculus, simplification, closed-form derivations → `sympy`
- Astronomy: coordinates, units, FITS I/O, WCS, cosmology, precise time → `astropy`

**Content extraction**
- Token-lean clean-markdown extraction from a webpage via the Defuddle CLI → `defuddle`
  (use over a raw page fetch when reading docs/articles/blog posts; complements `parser`/`web-search`)

## Loading spokes on demand

To keep CLI startup context lean, this cluster's spokes are **not** separately registered as skills — only this orchestrator and its `*-core` are enumerated. When you route to a spoke named above, **load it on demand** by reading its file:

`~/.agents/skill-clusters/skills/<spoke-name>/SKILL.md`  (or `skills/<spoke-name>/SKILL.md` inside the skill-clusters repo).