When you need fresh evidence from the web cross-referenced with the Brain methodology graph,Documentation Index
Fetch the complete documentation index at: https://mintlify.com/jsagir/mindrian-os-plugin/llms.txt
Use this file to discover all available pages before exploring further.
/mos:research runs a dual-source pull — public signal plus calibrated framework. It extracts your room context first, surfaces each finding with a candidate filing location and a decision card, then wires accepted findings as typed graph data other commands can consume. With Brain connected, every finding is semantically cross-referenced against 12,485 methodology vectors. Without Brain (Tier 0), research still runs and files typed evidence — the web-only path works fully.
Usage
| Argument | Description |
|---|---|
topic | A natural-language question or keyword string (e.g. "competitive landscape for B2B SaaS invoicing") |
url | A bare http(s):// URL — routes to the URL ingest pipeline |
--broad | Overrides the computed lens set with a fixed three-lens preset: scholarly, industry, patent — all at equal weight |
If you run
/mos:research with no argument, Larry will ask: “What do you want me to research? Give me a specific question or topic related to your venture.”What Happens
Room context extraction
Larry reads your room — the active section, current JTBD, persona role, and any existing evidence gaps tagged
needs_evidence. This becomes a conversational context summary surfaced before any fetch: “Speaking to your investor lens, you are in the build-thesis workflow… I will research this topic against THIS context.”Lens set computation
From the context summary, Larry computes an ordered, weighted lens set — the sources to query (e.g. scholarly, industry, patent, Brain). The lens names are surfaced so you can see which sources will run. With
--broad, this is overridden by the fixed three-lens preset.Execution via source-lens driver
The driver queries each lens cache-first (30-day TTL). On a miss it fetches live, writes back to cache, and deduplicates against prior research. Results are ranked by evidence tier and relevance. Up to the top 5 findings are returned.
Findings presented in venture context
Each finding is shown with: title, one-line summary, source URL, retrieval timestamp, evidence tier, and a pre-mapped candidate room section with a match score against that section’s existing claim graph. Raw search results are never dumped.
Filing decision per finding (F.1 gate)
For each finding, Larry renders a filing selector with five options: file to the primary section (recommended when confidence clears 0.7), file to a secondary section, split across both, defer to milestone audit, or reject with a captured reason. Nothing files without your explicit verb.
Wiring accepted findings to the graph
Approved findings are written as
EvidenceClaim nodes with review_status: proposed. Typed edges are added: INFORMS to the target section, CONTRADICTS when a finding conflicts with an existing claim, SUPERSEDES when it replaces a weaker-tier source. Rejected findings are wired with a REJECTED_BECAUSE edge — rejection is data, not a dead end.Next-move surface or chain return
If you ran research standalone, Larry surfaces an F.1 next-move selector naming the methodologies that can now consume the wired claims. If research was dispatched by another command (e.g.
/mos:build-thesis), it returns only the accepted EvidenceClaim node IDs so the caller resumes with exactly the evidence it needed.Output / Artifacts
EvidenceClaim nodes
Each approved finding becomes a typed
EvidenceClaim node in your room’s SQLite graph at room/.mindrian/room.db, with provenance: URL, retrieved_at timestamp, and evidence tier.Typed edges
INFORMS, CONTRADICTS, SUPERSEDES, and REJECTED_BECAUSE cascade edges are written to the local graph — never to the Brain. These edges are queryable via /mos:graph.Research artifact
Filed findings land as cited artifacts under
room/**/research/, matching the section the F.1 gate resolved.Memory events
A
research_filed or research_rejected memory event is written per finding, carrying URL, retrieval time, and evidence tier for future deduplication.Brain Behavior
| Mode | What changes |
|---|---|
| With Brain (Tier 1) | The brain lens is included in the lens set, semantically cross-referencing findings against 12,485 methodology vectors. Connections between findings and methodology are surfaced. |
| Without Brain (Tier 0) | The brain lens is skipped with a typed skipped provider status. All other lenses run normally. Research still files typed evidence — nothing is blocked. |
Recovery Modes
When an engine breaks mid-run, the run does not dead-end and does not lie. Theresearch_mode field on every result tells you exactly what kind of run occurred:
research_mode | Meaning |
|---|---|
normal | Every engine worked. Nothing to disclose. |
web_degraded_local_fallback | A live web leg failed; cached or local data covered the gap. The outage stays visible in providers. |
local_only | The run was deliberately offline — your choice, not a failure. |
insufficient_evidence | Zero items anywhere. A cold corpus is a typed verdict, never a silent success with empty arrays. |
llm_engine_recovery | An engine broke and a bounded, fenced LLM recovery did the work; everything it did is disclosed. |
manual_intervention_required | The run needs you — the message names the exact missing thing (a credential, or a Claude spend cap). |
Examples
Related Commands
/mos:graph
Query the typed evidence nodes and edges wired by research using natural language.
/mos:find-connections
Use Brain semantic search to surface cross-domain patterns connected to your filed evidence.
/mos:opportunities
Scan for funding opportunities informed by the room context research builds.
/mos:qualify-opportunity
Qualify a surfaced opportunity at a structured decision card after research surfaces candidates.