The Research Operations surface is how the enterprise plans, funds, scopes, and synthesizes research across four workstreams: clinical R&D, R&D finance, market research, and product research. This orchestrator forks its context, routes your inquiry to one of four sub-skills, then returns a digest. Heavy intake (protocol drafts, program ledgers, survey exports, interview transcripts) stays in the forked context.
This is the enterprise counterpart to the academic research/ domain. If your question is about finding literature, grants, or patents, use research/. If it is about planning, funding, scoping, or synthesizing research as an operational discipline, you are in the right place.
| Symptom | Sub-skill |
|---|---|
| "We're designing a Phase 2 trial — what's the endpoint and sample size?" | clinical-research |
| "What's our R&D program burn, and is this cost CapEx or OpEx?" | research-finance |
| "What's the TAM for this product, and how do we survey the segment?" | market-research |
| "How many users do we interview, and how do we synthesize the findings?" | product-research |
Same two-signal threshold pattern as commercial-skills. Single-signal → clarifying question. Mixed signals → highest-confidence first, chain second in a follow-up turn. Never silently chain.
| Signal class | Keywords | Sub-skill |
|---|---|---|
| CLINICAL | clinical trial, study design, protocol, endpoint, sample size, power, phase 1/2/3, biostatistics, eligibility, feasibility, estimand | clinical-research |
| RD_FINANCE | R&D budget, program budget, burn, runway, F&A, indirect rate, overhead, capitalize vs expense, R&D capex, portfolio ROI, rNPV | research-finance |
| MARKET | TAM, SAM, SOM, market sizing, survey design, sampling, margin of error, segmentation, competitive intelligence, market research | market-research |
| PRODUCT | user interview, JTBD, usability test, concept test, prototype test, discovery research, research repository, insight synthesis, saturation | product-research |
Derived from Matt Pocock's grill-with-docs pattern: explore-then-ask, one question per turn with a recommended answer, walk the decision tree depth-first, track dependencies, anchor every challenge in the research canon (references/ of each sub-skill).
Check the user's working directory first:
clinical-research, no question needed)?protocol.json → clinical; program-budget.json → finance; tam-model.json → market; interview-guide.md → product)?If the workspace resolves the lane, route silently.
Matt's rule: never bundle. Always recommend.
Pattern:
Q1/1: [precise question naming the two candidate lanes]
Recommended: [Lane X, because <signal-table rationale>]
(Confirm, or override?)
If the inquiry legitimately crosses two lanes (e.g., "design this trial AND budget it" = CLINICAL + RD_FINANCE), walk depth-first:
Never silently chain.
Forward original prompt + structured inputs (protocol JSON, program ledger CSV, market model, observation export).
≤ 200 words: analyzed, top 3 findings (anchored to a canon citation), top 3 next actions (named human owner where applicable), artifact path, and one grill challenge for the user. Examples:
Grill the user on lane-defining decisions before invoking the sub-skill. One per turn, recommended answer, canon citation:
Never run a sub-skill until the lane-defining decision is locked.
Before invoking a sub-skill for the first time in a workspace, point the user at that skill's onboarding questionnaire so the tools run pre-configured to their context:
python3 skills/<sub-skill>/scripts/onboard.py # interactive Q&A
python3 skills/<sub-skill>/scripts/onboard.py --show # questions + current config
Each sub-skill has its own question set (clinical: area/alpha/power/dropout/owners · finance: area/F&A/runway/standard/owner · market: profile/confidence/MoE/method · product: profile/insight-threshold/method/stakes). Answers persist to ~/.config/research-ops/<sub-skill>.json (or ./.research-ops/<sub-skill>.json with --scope project) and are consumed automatically by every tool in that skill. Customization is mandatory discipline here, not decoration — surface the onboarding step when a user starts a fresh research workstream.
Each sub-skill ships its own scripts/ar_evaluator.py — an isolated bridge to engineering/autoresearch-agent. Invoke autoresearch only when the user explicitly asks to "optimize", "improve", or "run a loop". The handoff is per-skill (no shared coupling): the loop edits the skill's input file and the evaluator scores it (clinical → feasibility_composite higher; finance → runway_months higher; market → tam_divergence lower; product → validated_insights higher). Never auto-start a loop; never let the loop edit the evaluator.
research/ (academic) — that domain finds literature, grants, and patents. This domain plans, funds, scopes, and synthesizes research.ra-qm-team — that's regulatory/QM submission (ISO 13485/14971, MDR, FDA 510(k)/PMA/QSR). clinical-research designs the study; it routes submission out to ra-qm-team.finance/financial-analysis — that's corporate close + valuation. research-finance manages R&D program/portfolio spend.research/grants — that's funding discovery. research-finance manages money already won.product-team — that's persona/journey artifacts, discovery sprints, and live A/B experiments. product-research is the method + repository discipline.marketing-skill — that's campaign analytics and demand-gen. market-research is upstream methodology.| Sub-skill | Artifact |
|---|---|
| clinical-research | protocol_synopsis.md + sample_size.json |
| research-finance | rd_program_budget.md + capex_opex_routing.json |
| market-research | market_sizing.md + sample_plan.json |
| product-research | research_plan.md + insight_synthesis.json |
documentation/implementation/research-ops-expansion-plan.md