调研与学习技能

Use when `paper-review` needs a canonical manuscript text artifact before claim extraction.

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Map paper notes + taxonomy into a signal table of tensions, missing pieces, and promising academic axes for brainstorm discussion. Writes `output/trace/IDEA_SIGNAL_TABLE.md`.

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Generate a compact pool of discussion-worthy research directions from the signal table, writing `output/trace/IDEA_DIRECTION_POOL.md`.

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Review and record one pending human checkpoint in a research Workspace; use when a HUMAN Unit is blocked on an `Approve C*` decision, and never treat silence or artifact existence as approval.

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Write the survey's front matter files (Abstract, Introduction, Related Work, Discussion, Conclusion) in paper voice, with high citation density and a single evidence-policy paragraph.

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Use when a tutorial module plan exists but each module still needs a verifiable teaching loop.

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Create per-subsection evidence packs (NO PROSE): claim candidates, concrete comparisons, evaluation protocol, limitations, plus citation-backed evidence snippets with provenance.

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Build per-chapter (H2) writing briefs (NO PROSE) so the final survey reads like a paper (chapter leads + cross-H3 coherence) without inflating the ToC.

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PRODUCES a citation-budget plan/report (`output/CITATION_BUDGET_REPORT.md`); does NOT edit the draft. Allocates an in-scope per-H3 budget (NO NEW FACTS) so drafts stop failing the global unique-citation gate; citation-injector applies it.

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Curate reader-facing survey tables for the Appendix (clean layout + high information density), using only in-scope evidence and existing citation keys.

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Conduct multi-round deep research on any GitHub Repo. Use when users request comprehensive analysis, timeline reconstruction, competitive analysis, or in-depth investigation of GitHub. Produces structured markdown reports with executive summaries, chronological timelines, metrics analysis, and Mermaid diagrams. Triggers on Github repository URL or open source projects.

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Use this skill instead of WebSearch for ANY question requiring web research. Trigger on queries like "what is X", "explain X", "compare X and Y", "research X", or before content generation tasks. Provides systematic multi-angle research methodology instead of single superficial searches. Use this proactively when the user's question needs online information.

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Transfer learning, metrics optimization, and continuous improvement for AI-powered QE agents.

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AI-powered defect prediction, pattern learning, and root cause analysis for proactive quality management.

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Conduct evidence-first technical research for Agentic QE, Ruflo, related ruvnet projects, or external tools. Use when Codex must investigate a repository, compare current upstream changes, trace dependencies and history, distinguish verified facts from inference, or synthesize findings into actionable engineering recommendations. Do not use for a simple known-answer lookup or an implementation-only task.

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Quantum-resistant, self-learning version control for AI agents with ReasoningBank intelligence and multi-agent coordination

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Implement ReasoningBank adaptive learning with AgentDB's 150x faster vector database. Includes trajectory tracking, verdict judgment, memory distillation, and pattern recognition. Use when building self-learning agents, optimizing decision-making, or implementing experience replay systems.

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Implement adaptive learning with ReasoningBank for pattern recognition, strategy optimization, and continuous improvement. Use when building self-learning agents, optimizing workflows, or implementing meta-cognitive systems.

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Optimizes QE agent performance through transfer learning, hyperparameter tuning, and pattern distillation across test domains. Use when improving agent accuracy, applying learned patterns to new projects, tuning quality thresholds, or implementing continuous improvement loops for AI-powered testing.

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Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.

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Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.

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对照高可信度一手来源调查问题，并将发现以 Markdown 文件落库。当用户希望研究某个主题、收集文档或 API 事实、或把阅读跑腿交给后台 agent 时使用。

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Run a fast 2-agent pre-submission check for an economics paper — focuses on contribution, identification, and causal overclaiming. Completes in ~1 minute.

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Review research code for reproducibility and quality, extract the paper's main empirical claims, compare paper to code, and write a constructive markdown report. Designed for social science / economics projects with LaTeX papers and Stata, R, or Python code.

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