Research & Learning skills

Deep, iterative internet research for current facts, source triangulation, contradiction checks, due diligence, competitive analysis, claim verification, "latest" lookups, and cited reports. Use when the user asks to browse, search, research the web, verify claims, compare sources, gather primary sources, investigate companies, people, products, technical topics, news, events, markets, or create AKBP-ready durable research evidence.

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Diagnose active BIRD daemon incidents across config, runtime, environment, sockets, logs, source, and tooling. Use when BIRD fails to start or reload, crashes, loses sessions/routes, behaves differently after lint passes, or when birdcc and bird -p disagree. Collect read-only evidence first, protect routing secrets, and route isolated config edits to bird-agent or pure implementation research to bird-source-explorer.

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Use when the user wants to see what learned instincts exist, their confidence scores and how current they are — reads the local instincts file under ~/.aos/instincts/ and reports it. Read-only.

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Use when the user wants to share or back up learned instincts — validates the local instincts file, allowlists the six known fields, strips secrets and home-directory paths, and writes a sanitized JSON artifact the user names. Never modifies the instincts file itself.

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Create a new Hindsight-powered subagent with long-term memory. Use when the user wants a specialized agent that learns and remembers across sessions.

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get_blocks ツールの引数、出力形式、BlockData/EntityData の読み方。レッドストーン回路を調査・検証する前に必ず読む。

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CLI版 mc-cli get-blocks コマンドの引数、出力形式、BlockData/EntityData の読み方。レッドストーン回路を調査・検証する前に必ず読む。

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Search US court opinions, dockets, and judges. 3 tools for federal court records research.

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Development skill from everything-claude-code

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Scientific research and analysis skills

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Analyze market conditions, geographic opportunities, pricing, consumer behavior, and product validation across Google Maps, Facebook, Instagram, Booking.com, and TripAdvisor.

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Complete App Store Optimization (ASO) toolkit for researching, optimizing, and tracking mobile app performance on Apple App Store and Google Play Store

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Skill for discovering and researching autonomous AI agents, tools, and ecosystems using the AgentFolio directory.

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Derive slices from specs, order by dependencies, write acceptance criteria with citations, and finalize phase/progress artifacts

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Cross-session progress tracking, pattern extraction, and session resumption. Gives agents memory across sessions using markdown files in `.memory/pipeline/progress/`. Subsumes self-improving-agent — handles both implementation tracking AND experiential learning in one skill. Workflows invoke specific protocols by name.

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Multi-source research synthesizer. Takes a question, runs 3-5 parallel web searches with varied phrasings, deduplicates, and returns a cited, concise answer. For Hebrew questions, searches in both Hebrew and English. Output is always under ~400 words.

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Continuous self-improvement through systematic logging, pattern detection, and behavioral updates. Use when: the owner corrects you, a task fails, you discover a better approach, or you notice a recurring pattern. Store raw learnings in .learnings/, update the specific skill or workflow that caused the issue when appropriate, and avoid vague promises to do better.

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Use when working with Allma — allma incident management, collaboration workflows, post-incident learning, and channel orchestration. Covers incident creation via Slack, timeline tracking, stakeholder communication, automated workflows, and retrospective analysis. Use when managing active incidents, reviewing post-incident learnings, or analyzing incident patterns in Allma.

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Train and fine-tune transformer language models using TRL (Transformers Reinforcement Learning). Supports SFT, DPO, GRPO, KTO, RLOO and Reward Model training via CLI commands.

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Orchestrator for the full academic research pipeline: research -> write -> integrity check -> review -> revise -> re-review -> re-revise -> final integrity check -> finalize. Coordinates deep-research, academic-paper, and academic-paper-reviewer into a seamless 10-stage workflow with mandatory, coverage-bounded integrity checks, two-stage peer review, and auditable quality-assurance artifacts. Triggers on: academic pipeline, research to paper, full paper workflow, paper pipeline, end-to-end paper, research-to-publication, complete paper workflow, 연구부터 논문까지, 연구 주제 설정부터 논문 완성까지, 논문 전체 워크플로.

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Multi-perspective academic paper review with dynamic reviewer personas. Runs a 5-seat, role-separated review panel (Journal-Fit Reviewer + 3 peer-review roles + Devil's Advocate) with field-specific expertise; role separation is not a claim of independent error processes. Supports full review, re-review (verification), quick assessment, methodology focus, Socratic guided, and calibration modes. Triggers on: review paper, peer review, manuscript review, referee report, review my paper, critique paper, simulate review, editorial review, calibrate reviewer, reviewer calibration, measure reviewer accuracy, 審查論文, 論文審查, 模擬審查, 同儕審查, 幫我審這篇, 以審查人角度評估, 審查者校準, 논문 심사, 동료 심사, 모의 심사, 심사자 관점에서 평가, 심사자 보정.

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Scholarly writing and research compliance. Use for CRediT, preregistration, Plan S, Nelson Memo, preprints, ORCID, LLM disclosure.

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Run programmatic Tavily search and extraction while filtering raw results outside the main agent context. Use for multi-step or high-volume research where titles, snippets, and selected passages should be curated before synthesis.

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Improve the clarity and voice of AI-assisted academic writing (papers, theses, rebuttals) and

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