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概要と使いどころ
Execute sophisticated Data Extraction and Privacy Leakage attacks explicitly against Large Language Models (LLMs) to natively force the neural network entirely into organically regurgitating exact, verbatim strings of Highly Confidential Personally Identifiable Information (PII), proprietary source code, or copyrighted material categorically memorized intrinsically during its foundational pre-training phase.
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Clickjacking playbook. Use when testing whether target pages can be framed, whether X-Frame-Options or CSP frame-ancestors are properly configured, and whether UI redress attacks can trigger sensitive actions.
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Understand the mechanics of Border Gateway Protocol (BGP) Hijacking, where an attacker creatively manipulates Internet routing tables to intercept, monitor, or drop massive volumes of network traffic destined for legitimate Autonomous Systems (AS).
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Identify and exploit Mass Assignment vulnerabilities in APIs. Use this skill when testing REST APIs or application forms that directly map user-supplied JSON or POST input to internal database objects. An attacker can inject undocumented variables (e.g., `is_admin`, `verified`) to illegally modify restricted properties.
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AI/LLM red-team assessment using the OWASP LLM Top 10 (2025) + OWASP AI Testing Guide (AITG v1, Nov 2025) frameworks, plus OWASP MCP Top 10 runtime testing for agentic/MCP targets. Tests prompt injection, jailbreaks, system prompt leakage, sensitive data extraction, excessive agency, improper output handling, model extraction, content bias, evasion, membership inference, MCP token exposure, MCP command injection, and more. Uses four tools in combination: FuzzyAI (single-turn jailbreak fuzzing), PyRIT (multi-turn orchestrated attacks), Garak (probe-based vulnerability scanning), and promptfoo (plugin-based red-team evaluation). Each tool covers different OWASP categories; running them together gives systematic coverage. Includes a conditional MCP reconnaissance phase and a post-access AI infrastructure phase (chained from /post-exploit). Produces: OWASP LLM Top 10 + AITG + MCP coverage matrix, findings per category, architecture diagram of the AI system, PoCs for confirmed exploits. Chains into /gh-export for issue filing.
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Inspects a skill's SKILL.md and its observations/runs.md log, identifies failure patterns, and proposes a targeted amendment to improve the skill. Trigger on: "improve this skill", "fix this skill", "update this skill", "why does X keep failing", "this skill is wrong", "add this to the skill", or automatically when observations/<skill-name>/runs.md contains 3 or more failure entries. Outputs the amendment as a diff the user can review before applying. Records the amendment rationale in observations/<skill-name>/runs.md after user confirmation.
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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Use this agent when you need to create a pathway summary for a gene after completing the main gene review. This agent should be invoked specifically to generate GENE-pathway.md files that summarize the gene's role in biological pathways with proper citations and mermaid diagrams.
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Use this agent when you need to perform custom bioinformatics analyses on genes or proteins that go beyond standard database lookups. This includes situations where existing domain/family annotations from UniProt are insufficient, when you need to verify dubious annotations, or when you need to perform comparative analyses across gene sets. The agent creates reproducible analysis pipelines with proper dependency management and documentation.
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。
Review and resolve PR review comments on the current branch's PR in this Flutter repository, from both AI reviewers (CodeRabbit, CodiumAI, CodePilot, Copilot, etc.) and human reviewers. Fetches comments via `gh`, splits them into AI and human groups, verifies each comment against the actual Flutter code, triages them, proposes fixes, and optionally applies fixes, replies to comments, and pushes. Use when the user asks to review PR comments, resolve PR feedback, review AI comments, review human review comments, or handle code review feedback.
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 working with PyQt/PySide6 QtCore - signals, slots, properties, timers, settings, file I/O
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 styling PyQt/PySide6 widgets with QSS - selectors and pseudo-states, stylesheet application, common style properties, widget-specific styling, or building a dark theme
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。
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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Frappe customization-surface guidance covering Custom Field, Property Setter, Client Script, Server Script, Workspace, Web Page, Page, Print Format, Report, Dashboard, Workflow, Role, Notification, Webhook, and related builder/admin DocTypes. Use when choosing or changing Frappe customization layers.
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。
Class Variance Authority - type-safe component variant styling with cva(), compound variants, and VariantProps
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。
Accessible React component library with style props and theming
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Design token architecture - primitive/semantic/component tiers, naming grammar, CSS custom property delivery, and DTCG build pipelines
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Inspect a codebase, a stack the user describes, or a description of what they want to build; map what is there to agents-inc catalog skills — or intent to candidate built-in stacks the user picks from — and emit a SeedPayload plus a human-readable proposal report. Use when seeding a configuration for a project that has none.
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UI component review patterns. Use when reviewing React components, hooks, props, state, styling, and accessibility. Covers rules of hooks, effect cleanup, render performance, list keys, keyboard and ARIA patterns.
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NativeWind v4+ - Tailwind CSS utility classes for React Native, className prop, CSS variables, dark mode, platform prefixes, animations, theming, third-party component integration
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。
Graph-relational database with EdgeQL query language, code-first schema, link-based relations, computed properties, and fully typed TypeScript query builder
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 skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like "the xlsx in my downloads") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.
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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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Drain the Pharos Dependency Map coverage-audit queues into evidence-refreshed edges, reviewed target dispositions, or documented deferrals. Use monthly after weekly production dependency audits, after dependency coverage drops, or when reviewing adapter gaps, material unlinked slices, symbol leads, duplicate/split groups, or unverifiable published edges.
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。