「auditing」の検索結果

Classify, write, and audit documentation per Diátaxis (diataxis.fr) — the four-quadrant method (tutorials · how-to guides · reference · explanation). Encodes the compass as a decision procedure, each quadrant's contract as hard rules, and an executable mixed-mode audit. Project-agnostic; the per-repo doc-location mapping lives in a repo rule of the project's own (kolu: `.apm/instructions/diataxis.instructions.md`). Use when writing any user-facing doc, structuring a docs site, or auditing existing docs for mixed modes.

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Autonomous novel writing CLI agent with web workbench (JiaOS Studio) - use for creative fiction writing, standalone short-fiction packages, cover generation, novel generation, style imitation, chapter continuation/import, EPUB export, AIGC detection, and fan fiction. Native English support with 10 built-in English genre profiles (LitRPG, Progression Fantasy, Isekai, Cultivation, System Apocalypse, Dungeon Core, Romantasy, Sci-Fi, Tower Climber, Cozy Fantasy). Also supports Chinese web novel genres (xuanhuan, xianxia, urban, horror, other). Multi-agent pipeline, two-phase writer (creative + settlement), stronger long-form chapter craft rules, hook-ledger payoff checks, 33-dimension auditing, token usage analytics, creative brief input, structured logging (JSON Lines), multi-model routing, custom OpenAI-compatible provider support, and JiaOS Studio web UI for visual book management, short-fiction runs, cover generation, chapter review, real-time writing progress, market radar, and analytics.

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Quantify cyber risk using FAIR methodology with Monte Carlo simulation, assess control effectiveness against NIST CSF/CIS/ISO 27001 frameworks, evaluate risk appetite alignment, and analyze cyber insurance coverage adequacy. Covers threat landscape mapping, asset valuation, loss event frequency and magnitude estimation, annualized loss expectancy calculation, control gap analysis with ROI ranking, residual risk assessment, and insurance policy gap modeling at 90th/95th/99th percentile loss scenarios. Use when building risk registers, preparing board-level risk dashboards, evaluating security investment priorities, assessing cyber insurance coverage, or auditing any organization's risk quantification maturity per NIST RMF, ISO 27005, or FAIR standards.

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Expert guide for AI Safety, Governance, and Responsible AI in production — Constitutional AI enforcement, runtime guardrails (NeMo Guardrails 2.0, Llama Guard 3), bias auditing, hallucination detection, EU AI Act compliance, model cards, and content provenance / Panduan ahli untuk Keamanan AI, Tata Kelola, dan AI Bertanggung Jawab di produksi.

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Pipeline from a URL or DESIGN.md to tokens, decisions and a UI spec. Use when building UI for an existing brand, auditing a design system or fixing visual inconsistency.

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Nielsen's 10 usability heuristics. Use when auditing an interface, designing interaction flows, writing error messages or reviewing usability.

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Repurpose content into a native TikTok script and caption. Take a LinkedIn post, blog, newsletter, or X thread that worked elsewhere and rebuild it as a spoken script: hook in the first second, every line sayable in one breath, open on the payoff, a trend angle where it fits, plus a caption, published via Publora on approval. Adapts content across platforms. Not for a fresh hook (use tt-hook-scripter), not the caption only (use tt-caption-writer), not auditing a script (use tt-humanizer --mode audit).

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Bias detection and mitigation, fairness metrics, privacy frameworks, consent models, transparency requirements, and accountability structures for data science practice. Covers algorithmic bias sources, disparate impact testing, differential privacy, GDPR principles, model cards, datasheets for datasets, responsible AI frameworks, and the organizational governance needed to make ethics actionable. Use when auditing models for bias, designing privacy-preserving systems, establishing governance processes, or evaluating the social impact of data-driven decisions.

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Test and validate MaaFramework Pipeline JSON, recognition nodes, action nodes, CustomAction/CustomRecognition wiring, resource loading, and end-to-end task behavior. Use when running `run_pipeline`, checking OCR/TemplateMatch/ColorMatch ROI, validating `context.run_task()` results, auditing `action: Custom` or `recognition: Custom` registrations, or choosing the right resource-check command.

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Scans Cosmos SDK blockchains for 9 consensus-critical vulnerabilities including non-determinism, incorrect signers, ABCI panics, and rounding errors. Use when auditing Cosmos chains or CosmWasm contracts. (project, gitignored)

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Systematic code maturity assessment using Trail of Bits' 9-category framework. Analyzes codebase for arithmetic safety, auditing practices, access controls, complexity, decentralization, documentation, MEV risks, low-level code, and testing. Produces professional scorecard with evidence-based ratings and actionable recommendations. (project, gitignored)

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Maps the 18 OCapN/CapTP/E-rights patterns reinvented piecemeal across codex-rs to their canonical Spritely/Goblins equivalents and the hermes-* bridge skills that formalize each correspondence. Use when auditing capability architecture in codex-rs, planning a Hoot/Goblins port, or reasoning about which E-rights primitive a codex-rs module implicitly implements.

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Scans Cairo/StarkNet smart contracts for 6 critical vulnerabilities including felt252 arithmetic overflow, L1-L2 messaging issues, address conversion problems, and signature replay. Use when auditing StarkNet projects. (project, gitignored)

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Scans Algorand smart contracts for 11 common vulnerabilities including rekeying attacks, unchecked transaction fees, missing field validations, and access control issues. Use when auditing Algorand projects (TEAL/PyTeal). (project, gitignored)

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Audits GitHub Actions workflows for security vulnerabilities in AI agent integrations including Claude Code Action, Gemini CLI, OpenAI Codex, and GitHub AI Inference. Detects attack vectors where attacker-controlled input reaches AI agents running in CI/CD pipelines, including env var intermediary patterns, direct expression injection, dangerous sandbox configurations, and wildcard user allowlists. Use when reviewing workflow files that invoke AI coding agents, auditing CI/CD pipeline security for prompt injection risks, or evaluating agentic action configurations.

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Validates and corrects Python shebangs and PEP 723 inline script metadata by applying four shebang-selection rules. Use when auditing or fixing shebangs in Python files — choosing between plain python3 and the uv shebang for standalone scripts with external dependencies, adding or removing PEP 723 metadata blocks to match actual import requirements, checking execute bit presence, or avoiding redundant transitive dependencies when typer is declared.

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Define and develop plugin mission statements — purpose, values, anti-patterns, and trade-offs. Use when creating a new plugin, auditing an existing plugin's alignment, or providing a reference for the alignment check loop to evaluate decisions against. Produces mission.json with [draft] status and writes an interview file for the human to refine it.

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Audit skill lifecycle by tracing call chains, detecting circular dependencies, finding instruction contradictions, identifying duplicated datasets, analyzing bidirectional coherence, discovering scriptable sequences, and learning patterns. Use when checking skill coherence, validating skill workflow, finding semantic gaps in plugin structure, or auditing plugin before marketplace submission. Generates audit reports to .plugin-creator/audits/ with findings by dimension.

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Optimize existing CLAUDE.md, SKILL.md, agent definitions, and other AI-facing files for Claude comprehension and economy. Scope: optimization of existing content only — not upstream sync, not read-only auditing. Measures baseline metrics, delegates to the ai-doc-optimizer agent with file-type-specific context, runs independent verification via second agent, measures post-optimization metrics, and presents comprehensive before/after report. Supports iterative mode for large targets. Use when improving prompt effectiveness, reducing token waste, or rewriting instructions for LLM consumption. Invoke with /optimize-claude-md <file-or-directory>.

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Runs the description-drift experiment — spawns all Claude Code agents simultaneously to collect self-reported capabilities, then compares them against static frontmatter descriptions to reveal how reliable orchestrator routing based on descriptions actually is. Use when measuring description drift across the agent fleet, re-running the capability collection experiment, analyzing a specific agent's self-reported capabilities, or auditing whether frontmatter descriptions accurately reflect agent behavior.

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Evaluate a single skill's quality against its stated purpose. Classifies the skill's purpose type, then scores applicable quality categories — universal dimensions always apply; structural dimensions (scripts, references, assets) are scored only when warranted by the skill's purpose. A focused 20-line behavioral skill with no bundled resources scores fully on all applicable categories. Use when auditing skill quality, checking marketplace readiness, evaluating skill completeness, performing pre-publication evaluation.

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Use when writing delegation instructions to subagents, authoring CLAUDE.md files, rules, skills, or agent definitions, or auditing existing AI-facing content for bloat. Activates on "write a rule for", "add to CLAUDE.md", "create an agent", "update memory", or any request to author AI-facing instruction content. Removes discoverable data, explained-away knowledge, invented constraints, and stale cached facts.

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Audit agent lifecycle — validates agent execution capability against configuration. Accepts plugin path, runs 8 semantic audits (capability vs config alignment, skill loading correctness, inter-agent contracts, prompt contradictions, tool sufficiency, dead agents, scriptable patterns, pattern learning), writes reports to .plugin-creator/audits/. Use when auditing agent lifecycle, checking agent capabilities, verifying tool access, finding dead agents, validating agent contract alignment, or confirming agents can execute workflows.

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Review test effectiveness against authoritative contracts, relevant fault detection, refactor tolerance, boundary fidelity, isolation, and diagnostic value. Use when auditing test quality, reviewing coverage gaps, improving regression protection, or assessing tests after a change.

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。