Capability menu for the research engine. Lists the 10 freely-composable research packages, what each does, when to reach for it, and a pointer to its full skill table. Read this after north-star crystallization to decide which packages to use — no fixed order. Also serves as the skill-index (capability map).
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name
research-catalog
description
Capability menu for the research engine. Lists the 10 freely-composable research packages, what each does, when to reach for it, and a pointer to its full skill table. Read this after north-star crystallization to decide which packages to use — no fixed order. Also serves as the skill-index (capability map).
execution
reference
Research Catalog
These 10 packages are freely-composable capability domains. There is no prescribed order and no pipeline. After your research direction is crystallized, decide which packages to invoke, in what sequence, and whether to loop back — based on the current research task. Each package is a self-contained research engine with its own campaigns, strategies, tactics, and SOPs.
To use a package, read its reference table under references/<package>.md for the full skill list (sorted by layer, then name). Pick the package whose purpose matches your current need; open its table; select the skills you need.
The 10 packages are listed alphabetically below — the order carries no sequencing meaning.
ara-from-context
Research-to-artifact compilation engine: compiles a completed context/ research record into an ARA (Agent-Native Research Artifact) — a 4-layer machine-executable knowledge package (PAPER.md + logic/ + src/ + trace/ + evidence/), not a LaTeX narrative paper — then runs a Level-2 epistemic rigor review. One campaign — ara-from-context.
Reach for it when: a research arc (typically after experiment-execution) has produced results worth packaging for agents to reproduce/extend → compile context into an ARA + epistemic review. Requires the external compiler / rigor-reviewer skills ().
npx @ara-commons/ara-skills
Skills: see references/ara-from-context.md
convergence
Universal convergence engine: turns an unstructured candidate set into ranked selections, balanced portfolios, and validated decisions. Six campaigns — multi-criteria-scoring, pairwise-ranking, structured-consensus, feasibility-assessment, portfolio-optimization, steel-manning.
Reach for it when: score/rank candidates against multiple criteria; produce a global ranking via pairwise comparisons; multiple perspectives disagree and need convergence; assess feasibility/readiness; select a balanced portfolio; verify rejected candidates or stress-test winners.
Skills: see references/convergence.md
creative-ideation
Creative generation engine: transforms hypotheses and research questions into diverse solution spaces. Ten parallel creativity campaigns spanning structural, analogical, destructive, and combinatorial methods.
Reach for it when: SCAMPER / TRIZ / component surgery / structural transformation / function trimming → structural-deconstruction; cross-domain / analogical transfer / bisociation / random stimulus → cross-domain-discovery; assumption negation / reverse brainstorming / worst method → assumption-destruction; biomimicry / biological analogy / BioTRIZ → biomimicry; analogy / metaphor / excursion method → synectics; morphological analysis / Zwicky box / design space → morphological-exploration; PO / lateral thinking / concept fan → lateral-thinking; concept blending / blending / emergence → combinatorial-creativity; perspective switching / six hats / role-play → perspective-forcing; enumeration / coverage analysis / method matrix → systematic-enumeration.
Skills: see references/creative-ideation.md
deep-insight
Deep insight engine: from surface phenomena to root causes, boundaries, assumptions, and the problem itself. Five campaigns — gap-analysis, insight, boundary-analysis, sensitivity-analysis, problem-reformulation.
Experiment execution engine: from validated hypotheses/approaches through experiment design, constraint analysis, scenario planning, implementation planning, to actual execution and result collection. Four campaigns — experiment-design, constraint-analysis, scenario-planning, implementation-planning.
Goal-driven hypothesis & research-question formation engine: turns upstream gaps and insights into testable hypotheses and precise research questions. Three campaigns — gap-prioritization, hypothesis-formulation, research-question.
Reach for it when: gap ranking / prioritization / which is worth doing / multi-dimensional scoring / portfolio → gap-prioritization; hypothesis generation / theoretical derivation / falsifiability / If-then / variables / mechanisms / competing hypothesis → hypothesis-formulation; research question / PICO / SPIDER / FINER / scope / sub-question decomposition / success criteria → research-question.
Skills: see references/hypothesis-formation.md
knowledge-acquisition
Systematic research-knowledge acquisition engine: academic literature, patent landscapes, benchmark evaluations, cross-study statistical synthesis, and SOTA baselines. Five campaigns — literature-survey, patent-mining, benchmark-archaeology, meta-analysis, baseline-establishment.
Reach for it when: literature review / survey / paper search / PRISMA / snowball → literature-survey; patent analysis / prior art / white space / claims / IPC → patent-mining; benchmark analysis / evaluation methods / metric flaws / leaderboards / saturation → benchmark-archaeology; cross-study statistical synthesis / effect size / heterogeneity / publication bias / GRADE → meta-analysis; SOTA compilation / performance comparison / baseline reproduction / progress curves → baseline-establishment.
Skills: see references/knowledge-acquisition.md
knowledge-structuring
Knowledge-structuring engine: compiles findings into structured artifacts in the wiki vault. Four campaigns — ontology-building (concept hierarchies), causal-modeling (causal graphs), dimensional-analysis (design-space maps), argument-mapping (argument graphs).
Reach for it when: building a domain ontology — concept extraction, relation typing, taxonomy construction → ontology-building; identifying variables and mapping mechanisms into a causal graph → causal-modeling; discovering the dimensions/axes of a design space and enumerating combinations to find gaps → dimensional-analysis; extracting claims, linking evidence, assessing argument strength → argument-mapping.
Skills: see references/knowledge-structuring.md
north-star-crystallization
Research intent crystallization engine: transforms vague research interests into precise, actionable North Star statements through structured dialogue, producing a North Star plus a structured ResearchBrief. One campaign with three strategies — cold-start, warm-start, hot-start — chosen by the user's existing clarity.
Reach for it when: the research direction needs to be (re)crystallized — no direction at all → cold-start; a general direction but not specific → warm-start; a specific topic/problem needing structure → hot-start. Reach for it whenever the current direction feels fuzzy and you want to re-anchor before composing other packages.
Skills: see references/north-star-crystallization.md
stress-test
Research-artifact stress-testing engine: adversarially validates any artifact — hypotheses, claims, experiment designs, approaches, research questions, gaps, or ideas — until every claim survives or is annotated. Five campaigns — multiagent-debate, red-teaming, failure-anticipation, counterfactual-probing, adversarial-stress-testing.
