# Research Catalog

> 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).

- Skill: `yogsoth-ai/research-catalog` (Agent Skill, multi-file: 11 files)
- Install (CLI): `npx skillmds@latest add yogsoth-ai/research-catalog`
- Raw SKILL.md: https://api.skillmd.com/api/skills/yogsoth-ai/research-catalog/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Research & Search
- Author: yogsoth-ai (https://skillmd.com/u/yogsoth-ai)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/yogsoth-ai/research-catalog

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# 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.

**Reach for it when:** gap identification / white-space classification / evidence map / prioritization → gap-analysis; root-cause analysis / stakeholders / tensions / HMW / 5 Whys → insight; validity boundaries / method failure / robustness / distribution shift → boundary-analysis; assumption ranking / sensitivity / variance decomposition / critical path → sensitivity-analysis; redefining the problem / dominant ideas / multiple perspectives / wicked problems → problem-reformulation.

**Skills:** see `references/deep-insight.md`

## experiment-execution

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.

**Reach for it when:** experiment design / factors / variables / ablation / baseline comparison / statistical methods → experiment-design; bottleneck / constraint / insufficient resources / dependencies / conflicts → constraint-analysis; scenarios / future / robustness / worst case / competitors / timeline → scenario-planning; planning / execution / implementation / running experiments / result analysis / reproducibility → implementation-planning.

**Skills:** see `references/experiment-execution.md`

## hypothesis-formation

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.

**Reach for it when:** adversarial debate / multi-perspective review → multiagent-debate; systematic attack / assumption challenge → red-teaming; failure-mode prediction / risk assessment → failure-anticipation; critical-dependency probing / causal necessity → counterfactual-probing; logical falsification / boundary testing → adversarial-stress-testing.

**Skills:** see `references/stress-test.md`