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概要と使いどころ
Adversarial spec-compliance PR review — cross-references diffs against approved specs, verifies runtime claims against source, detects competing PRs, audits scope/convention compliance. Use before merging.
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。
Produce animated source control visualizations using Gource. This skill handles installation of Gource and ffmpeg, detection of repository metrics, preset-based configuration, single and multi-repo log generation, ffmpeg video encoding pipeline, headless rendering for server environments, caption generation from git tags, GitHub avatar resolution, and GSD output delivery. Use this skill whenever the user wants to visualize repository history, create a code evolution video, see project timeline animations, generate Gource videos, combine multiple repos into one visualization, or produce any kind of source control visualization. Also trigger when the user mentions "Gource", "repo visualization", "code history video", "project evolution animation", or asks to "show me what we built".
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。
Analyze code changes for security vulnerabilities using LLM reasoning and threat model patterns. Use for PR reviews, pre-commit checks, or branch comparisons.
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 CodeQL static analysis for security vulnerability detection, taint tracking, and data flow analysis. Use when asked to analyze code with CodeQL, create CodeQL databases, write custom QL queries, perform security audits, or set up CodeQL in CI/CD pipelines.
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。
The **Coherence Composer** is an epistemological skill that validates structural consistency and explores counterfactual worlds while respecting constraints from both **Commitment Tracker** (ontology) and **Opacity Detector** (epistemology).
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。
The **Commitment Tracker** skill extracts and makes explicit the **ontological commitments** that agents make implicitly through their code and behavior. It enables detection and resolution of silent incompatibilities between systems that use different underlying assumptions about what exists and ho
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。
Detects timing side-channel vulnerabilities in cryptographic code. Use when implementing or reviewing crypto code, encountering division on secrets, secret-dependent branches, or constant-time programming questions in C, C++, Go, Rust, Swift, Java, Kotlin, C#, PHP, JavaScript, TypeScript, Python, or Ruby.
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。
Digital forensics techniques for detecting device compromise, analyzing malware indicators, and investigating targeted surveillance. Based on methodologies from Citizen Lab's Pegasus Project and Predator investigations.
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。
Google Calendar management via CalendarACSet. Transforms scheduling operations into GF(3)-typed Interactions, routes to triadic queues, detects saturation for balanced-calendar-as-condensed-state.
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。
Hopf bifurcation detection for dynamical system state transitions with GF(3) phase portraits
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。
Lee Cronin's Assembly Theory for molecular complexity measurement and life detection via assembly index computation.
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。
Interact with Aptos blockchain - check balances, transfer APT, swap tokens, stake, and execute Move view functions. Features game-theoretic decision analysis with Nash equilibrium detection. All transactions require explicit approval.
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。
Implement affective valence as directional derivative of interoceptive energy landscapes for AI alignment. Use when building alignment-aware RL agents, validating GF(3) conservation in reward signals, training Langevin-based policies, or analyzing fold-change detection signals in POMDP environments.
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。
Interruption pattern detection and retrieval from Amp thread history. Use for analyzing tool rejection patterns and improving agent 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。
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.
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 AddressSanitizer to detect memory safety bugs in C/C++ programs. Identifies use-after-free, buffer overflow, memory leaks, and other memory errors.
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。
This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.
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。
This skill should be used when agents generate placeholder tokens like "pseudo-", "mock-", "temporary", "TODO", "demo-", or similar incompleteness markers. Detects substitution patterns in agent OUTPUT and triggers mandatory user interview instead of accepting incomplete work. Activates automatically on any output containing forbidden tokens.
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。
Auto-selects and enforces the strongest valid Python typing lane for the detected Python version and dependencies — no user input required. Use when adding or tightening type annotations, eliminating Any usage in internal code, designing boundary validators or parsers, choosing between stdlib typing (TypedDict, Protocol, dataclasses), Pydantic models, or Hypothesis property tests, addressing ty or mypy failures, or applying version-specific features (TypeIs, ReadOnly, PEP 695 generics, PEP 649 deferred evaluation). Enforces boundary isolation — raw payloads validated immediately at ingress and returned as typed internal objects.
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。
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.
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 existing tests against the software and contracts they actually exercise. Use to assess whether a test has justified unique protection, detects consequential faults, tolerates valid refactors, earns its lifecycle cost, or deserves strengthening, replacement, consolidation, or removal consideration. Reviews are read-only and evidence-backed, including TDD and regression tests.
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 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。
Use for architecture review, dependency graphs, coupling analysis, or circular dependency detection. Establishes project module namespaces and traces imports plus Claude plugin hook, skill, script, PEP 723, and MCP boundaries. Emits partitioned Mermaid graphs with cycle, coupling, and ownership signals; verifies contract and consequence before assigning severity. Registers diagrams as codebase-analysis artifacts.
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 PLAN artifact from SAM Stage 2 needs contextualization against actual codebase state — grounds the design plan in reality by performing scope analysis (NEW/MODIFY/COMPLETE classification), conflict detection between plan assumptions and codebase patterns, and resource mapping to concrete file paths and integration points. Produces an updated ARTIFACT:PLAN registered via MCP with a Contextualization section appended.
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。