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Plugins
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Discovery-to-watchlist research primitives. idea-search is a read-only scan over the agentm recall engine surfacing existing vault/codebase entries relevant to a question. learn-forward leans by name on agentm's approved-source forward-learning pipeline to mine operator-configured sources onto the watchlist. codebase-improvement applies a research insight's stale pattern against the operator's own repo, surfacing exactly one watchlist finding per match -- never auto-editing. All three are strictly discovery-surfacing: findings land in the watchlist for operator review, nothing is ever auto-adopted.
Project-scoped knowledge base for Claude Code with progressive disclosure (INDEX.md dispatcher, sed-extracted summaries) and contrarian-pass investigation via background Opus subagents. Conflict-handling history prevents re-trying refuted approaches across sessions.
PhD-level multi-perspective research inspired by Stanford's STORM. The 'storm research' skill orchestrates five expert persona agents (practitioner, skeptic, economist, historian, academic), maps their contradictions, synthesizes a cited briefing, and red-teams the result. Say 'storm research <topic>' to start.
Deep-research harness: plan conversationally, execute autonomously into a cited report with resumable file state and pluggable backends.
4-skill knowledge aggregation plugin: /research (gather & integrate), /analyse (status, gaps & argumentation health), /assess (claim confidence & dependency propagation), /synthesis (combinations, argument chains & decision trees). Includes @researcher agent for skill-aware parallel research and @synthesizer agent for Cross-Impact and Morphological analysis.
team-ready Claude Code skill adapted from Anthropic internal-comms patterns for repeatable research and enablement briefings.
A persistent research thinking partner for academic/scientific projects (English). Maintains layered memory across sessions and collaboratively explores research directions.
Discover and deeply research B2B companies to sell to. Uses Serper.dev for Google-grade search and browser-use (Chromium under the hood) for page extraction — including JS-rendered SPAs. Outputs a scored research report and CSV. Use when the user wants to find prospects, build a target list, or score companies against an ICP.
Make research data collection trustworthy without gatekeeping: ODCS data contracts with CI enforcement, dataset QoS/SLOs, data-product reviews (DAUTNIVS), right-sized governance with steward roles and certification, agent-consumable dataset catalogs, and lakehouse storage architecture for training data.
Autonomous research → plan → implement → test → decide loop. Codex critiques the plan and reviews the implementation; each stage is mirrored into an Obsidian wiki for cross-cycle memory; commits after each successful stage. Stops only when tests pass and no open doubts remain.
Enterprise / cross-functional Research Operations domain — the managed counterpart to the academic research/ domain. v2.9.0 ships 5 skills: orchestrator (context: fork) + clinical-research (study design: protocol synopsis + endpoint selection + sample-size/power for means/proportions/survival + phase-gate feasibility) + research-finance (R&D program budgeting with F&A split + burn/runway + capitalize-vs-expense routing + portfolio ROI) + market-research (TAM/SAM/SOM computed both top-down and bottoms-up + survey sampling with FPC and per-segment minima + Kotler segmentation scoring) + product-research (goal-matched study design + method-based saturation with confidence + insight synthesis that flags single-source anecdotes). Hard rules: clinical outputs are estimates with a named clinical owner (never fact), finance outputs surface assumptions and route capex-vs-opex to a named finance owner (never auto-decide), market sizes show method + assumptions (never a single number), product insights require recurrence across independent participants. Each sub-skill ships per-skill onboarding questions (onboard.py), a customization config consumed by every tool, and an isolated opt-in autoresearch evaluator (ar_evaluator.py) bridging to engineering/autoresearch-agent. 24 stdlib Python tools (12 analysis + 12 onboarding/customization/autoresearch), 12 reference docs. Distinct from ra-qm-team (regulatory/QM submission), finance (corporate close/valuation), research/grants (funding discovery), product-team (persona/journey/live experiments), marketing-skill (campaign analytics).
Research orchestrator (hybrid router + fallback). Deterministic SIGNALS classification routes to 6 specialists (pulse/litreview/grants/dossier/patent/syllabus) at >=2 signals, else runs own 8-step plan-decompose-search-synthesize-cite fallback. Routing transparency mandatory. Path-B from megaprompt 13.
Research orchestrator (hybrid router + fallback). Deterministic SIGNALS classification routes to 6 specialists (pulse/litreview/grants/dossier/patent/syllabus) at >=2 signals, else runs own 8-step plan-decompose-search-synthesize-cite fallback. Routing transparency mandatory. Path-B from megaprompt 13.
Research helpers for reading public web pages that normal browsing, scraping, snippets, or direct fetch tools cannot read cleanly.
Academic paper writing guidance for ML/CV/NLP papers
End-to-end academic research pipeline: literature discovery, citation verification, thematic synthesis, and perspective-driven content generation. AutoResearchClaw-inspired.
Plan-based AI research workflows: master plan with tracker, versioned execution plans, decision log, plan-quality rubric, and a browser board for review and annotation.
Structured research summarization — summarize academic papers, market research, user interviews, and competitive analysis into actionable insights.
Structured research summarization — summarize academic papers, market research, user interviews, and competitive analysis into actionable insights.
1 working methods for research thinking: research-synthesis, and more
Online research skills. Ships deep-research (cited HTML report), ux-research / ui-research (live-rendered UI variation mockups), and price-estimate (competitive pricing analysis with pessimistic bias). All reports open in the browser
Skills and agents for the ML research pipeline: literature review, research ideation, math verification, experiment design, honest results reporting, and paper writing.
Key-free research skills driving the host agent's own LLM + web tools (ships deep-deep-research).
End-to-end research lifecycle: explore, foundation, design, eval, experiment, write, review, rebuttal — with adversarial pre-mortem gates at every phase transition.