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概要と使いどころ
Curate, review, or repair ai-gene-review ModuleReview YAML documents under modules/, including pathway/module boundary setting, parts and variant modeling, annoton-level molecular functions, representative UniProt/PTN grounding, module deep-research provenance, validation, rendering, and project batch updates.
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Review, either de-novo, or augment the review of an existing one. A list of deep research providers may be specified.
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 this agent when you need to review UniProt annotation rules (ARBA, UniRule) for quality, biological accuracy, and GO annotation appropriateness. This agent performs comprehensive analysis of rule condition sets, evaluates literature support, assesses taxonomic scope, and recommends curation actions.
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Fierce autonomous internal co-founder. Bounded web research for evidence-backed improvements. Structured idea formulation with self-critique filter that rejects ideas not serving the goal. Auto-implements high-confidence aligned improvements, proposes medium-confidence ideas for approval. Evaluates viral coefficient, AI search visibility (GEO), and solo SaaS economics. Considers higher pursuits: employing disabled people, spiritual tech investigation, 99% wealth donation ethos.
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说明如何读取和写入 IELTS Buddy 学习计划、精确课程与练习内容、任务提醒、学习路径、资源和学习事件；不定义诊断、推荐或计划内容。
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在已绑定且具备 IELTS Buddy 后台题库权限的账号下，发现并调用只读题库数据接口；不定义题库研究或分析方法。
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。
ERPNext-aware reasoning for accounting, selling, buying, stock, manufacturing, projects, HR, support, education, CRM, and other ERPNext module work. Use when a task affects ERPNext module behavior, configuration, reports, dashboards, workflows, or customizations.
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。
Frappe LMS customization guidance for courses, batches, lessons, quizzes, assessments, enrollment, progress, certificates, portals, and learning workflows. Use when work touches Frappe LMS or education/training flows.
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Investigation flow (Glob -> Grep -> Read), evidence-based research with file:line references, structured output format for AI consumption. Use for pattern discovery, implementation research, and codebase investigation.
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Research what people have discussed in the last 30 days across high-signal public platforms. Use when the user asks for `/last30days`, recent trend research, competitive intelligence, market pulse briefs, or engagement-ranked synthesis from Reddit, Hacker News, GitHub, X, YouTube, Polymarket, and similar 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。
Broad, pre-curation evidence reconnaissance for a declared Gaia skill, intake batch, upstream repository, or proposed generic capability. Run before choosing a discovery strategy or mapping topology, to give the curator independent research leads and counterevidence. Context only: never scores, grades, ratifies, ingests, or mutates the registry. Later ev-discovery and ev-pipeline phases validate/promote any selected leads.
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Research and populate tracked stablecoin compliance sidecars for the U.S. GENIUS Act, EU MiCA, or both. Use when adding or auditing authorization, applicability, pathway, or disclosure evidence.
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Coordinate a Pharos stablecoin addition or pre-launch promotion by enforcing pre-edit runtime admission, routing specialist research and authorized edits, and owning the integrated generation/check handoff.
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Research stablecoin reserve composition, review provenance, custody evidence, dependency edges, and backing-pillar slices; populate the owning reserves sidecar only when explicitly requested.
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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.
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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。
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.
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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。
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.
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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。