調査、論文、教育、資格学習（4,109 件）
概要と使いどころ
Research stablecoin reserve composition, review provenance, custody evidence, dependency edges, and backing-pillar slices; populate the owning reserves sidecar only when explicitly requested.
日本語の概要は準備中です。原文の説明を表示しています。
Chart a too-big effort as a shared map of decision tickets on the Gaia headquarters issue tracker, then resolve them one at a time until the way to the destination is clear. Use when someone says: "this is too big for one session", "chart a map", "wayfinder map", "plan this effort", "what do we need to decide first", "open a wayfinder", "work the map", "what's on the frontier", or /wayfinder. Covers any gaia-research org repo — gaia-skill-tree, gaia-research, gaia-skill-heaven and beyond — but every map and every ticket lives on ONE tracker: gaia-research/gaia-skill-tree. This is cartography (planning an effort as decisions), NOT triage of an existing backlog — for that use /gaia-triage.
日本語の概要は準備中です。原文の説明を表示しています。
Triage the Gaia issue backlog and skill-batch intake queue. Use this skill when someone asks to: "triage issues", "clean up the issue tracker", "review the backlog", "close stale issues", "process the intake queue", "review skill-batches", "evaluate draft skill proposals", "approve or reject pending skills", "is this issue still valid?", "what's clogging the backlog?", "run a triage pass", or /gaia-triage. Covers two workstreams: (1) GitHub issue lifecycle — identifying resolved, stale, or need-more-info issues and acting on them via gh CLI; (2) skill-batch intake — evaluating draft proposals in registry-for-review/skill-batches/ and routing them toward promotion or rejection. This is the gatekeeping step before new skills enter the canonical registry; and (3) a cross-repo triage sweep — sizing, P0-P4 prioritization, umbrella/epic synthesis, and assignment across every gaia-research org repo, on the one headquarters tracker.
日本語の概要は準備中です。原文の説明を表示しています。
Fuse all of a contributor's named skills into a single fusion node (suite capstone) in the registry. Use when the user says "fuse <contributor>'s skills", "create a suite for <contributor>", "build a capstone for <user>", "fuse all of <user>'s named skills", "create a suite fusion", "consolidate <contributor> into one skill", or explicitly types /gaia-fuse-full-suite. Also triggers when a contributor has accumulated 3+ named skills and the user asks what to do next with them, or when someone asks "can we make a suite from these?". This skill: collects component IDs from registry/named/<contributor>/, verifies nodes exist, researches evidence and appraises Trust Magnitude, runs `gaia dev fuse` to write the fusion node and suite manifest, validates, and opens a PR on a review/meta/ branch.
日本語の概要は準備中です。原文の説明を表示しています。
End-to-end plan → implement → review pipeline that takes one triaged Gaia issue to a merged-ready PR, with Kill Criteria on the issue and zero follow-up debt. Use when someone says: "resolve this issue", "take #N end-to-end", "work the P0 queue", "fix and ship this issue", "run the resolver", "close out this issue properly", or /gaia-issue-resolver. Works any gaia-research org repo — gaia-skill-tree, gaia-research, gaia-skill-heaven — while the issue itself lives on the headquarters tracker, gaia-research/gaia-skill-tree. Assumes the issue is already triaged and carries a P0–P4 label; if it does not, run /gaia-triage first.
日本語の概要は準備中です。原文の説明を表示しています。
Skippable Phase 0 of the Gaia evidence verification pipeline. Use only on declared need for higher-quality Stage-2 evidence: a promotion candidate, or a skill that gaia-meta-sweep flagged as under-evidenced. Given a skill id, source repo/context, and needed evidence types (benchmark-result, arxiv, peer-review, social-signal), it runs Firecrawl searches, scrapes top hits to markdown, and appends discovered sources into Phase 1 inputs. Missing benchmark evidence is recorded as benchmark-source candidates first, not direct scoring rows. Trigger phrases: "discover evidence", "find new evidence", "Phase 0", "run ev-discovery", "search for benchmarks/papers", "under-evidenced", "promotion candidate needs stronger proof". This is the ONLY ev-* skill that searches the web for NEW evidence; other ev-* skills aggregate or verify known sources. Requires Firecrawl to be usable (`firecrawl --status` authenticated OR FIRECRAWL_API_KEY set); skips gracefully when neither holds.
日本語の概要は準備中です。原文の説明を表示しています。
Walks a user through connecting the judge models this plugin can use — Codex, OpenCode, OpenRouter (or any compatible endpoint: 9router, z.ai, Moonshot), Gemini — and through picking models, including "research current models for me". Use for "multi setup", "set up multi", "configure judges/reviewers/ backends", "connect 9router", "change/pick models", "onboarding", or whenever a probe shows missing backends and the user needs to know what to do about it.
日本語の概要は準備中です。原文の説明を表示しています。
Scaffold a PhysicsIntern research workspace in the current folder. Run only when the user explicitly invokes it to set up a new physics/maths research workspace.
日本語の概要は準備中です。原文の説明を表示しています。
Scaffold a PhysicsIntern research workspace in the current folder.
日本語の概要は準備中です。原文の説明を表示しています。
Use when the user wants to be taught an Anthropic product or feature - "teach me Claude Code", "learn Cowork", "academy lesson", "train me on MCP or the Claude API", "quiz me", "next lesson" - or asks for structured learning rather than a one-off answer. Covers Claude Code, Claude.ai, Cowork, Tag, Platform/API, and AI Fluency. Do NOT use for auditing how the user already works (use academy-review) or for mid-task help where the user just wants their task done.
日本語の概要は準備中です。原文の説明を表示しています。
Reverse engineers binaries using radare2/rizin interactively, covering analysis initialization, navigation, disassembly and graph views, patching, and the visual mode workflow. Activates for requests to reverse a binary with radare2 or rizin, learn r2 commands, or navigate and disassemble a sample in r2.
日本語の概要は準備中です。原文の説明を表示しています。
Analyzes how a ransomware sample encrypts files: identifying the crypto scheme (symmetric/asymmetric/hybrid), key handling, file targeting and extension/marker changes, shadow-copy deletion, and ransom-note artifacts. Activates for requests to analyze ransomware encryption, assess decryptability, or study ransomware behavior.
日本語の概要は準備中です。原文の説明を表示しています。
Captures and analyzes malware network traffic from a detonation: extracting C2 endpoints, HTTP/DNS patterns, TLS metadata (JA3/SNI), and beaconing from a PCAP to build network IOCs and detection. Activates for requests to analyze malware PCAP, extract C2 from traffic, or study network behavior.
日本語の概要は準備中です。原文の説明を表示しています。
Research a topic and save findings to vault
日本語の概要は準備中です。原文の説明を表示しています。
Research topic by searching vault and synthesizing findings
日本語の概要は準備中です。原文の説明を表示しています。
Teach models new tasks from a handful of examples — prompting strategies, example selection, and in-context learning mechanics.
日本語の概要は準備中です。原文の説明を表示しています。
Uncensored open-model inference on Featherless AI — raw model behavior for research and evaluation.
日本語の概要は準備中です。原文の説明を表示しています。
Research libraries and APIs automatically by pulling live documentation into the agent's context before answering. Use when writing code against unfamiliar or fast-moving libraries so examples match current versions.
日本語の概要は準備中です。原文の説明を表示しています。
Manage citations and references — collecting sources, organizing libraries, formatting bibliographies, and keeping reference integrity. Use when writing anything that cites sources.
日本語の概要は準備中です。原文の説明を表示しています。
Run a monthly money check-in with an AI assistant: categorize spending, spot leaks, set one savings target, and view it through the 50/30/20 lens — judgment-free. Use once a month to stay aware of where your money goes. General educational information, not financial advice.
日本語の概要は準備中です。原文の説明を表示しています。
Learn effectively from courses and academies: choosing programs, studying actively, and converting courses into skills. Use when investing time/money in structured learning.
日本語の概要は準備中です。原文の説明を表示しています。
Build with BAAI's Aquila models — open bilingual models from Beijing's academy with research transparency.
日本語の概要は準備中です。原文の説明を表示しています。
Optimize content for AI answer engines (AEO/GEO): citation-friendly structure, factual precision, and measuring AI visibility. Use when AI search drives discovery.
日本語の概要は準備中です。原文の説明を表示しています。
Label smarter, not more — pick the most informative examples for annotation to maximize model gains per label.
日本語の概要は準備中です。原文の説明を表示しています。