3,534 件 ・ 関連度順
概要と使いどころ
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)
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。
GitHub interactome explorer for bmorphism/plurigrid ecosystem. Maps collaborations across AlgebraicJulia, Topos Institute, Anthropic, and MCP servers. Use for discovering cobordisms between research communities.
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。
Python fuzzing with Atheris for discovering vulnerabilities in Python code.
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。
BDD-Driven Mathematical Content Verification Skill Combines Behavior-Driven Development with mathematical formula extraction, verification, and transformation using: - Cucumber/Gherkin for specification - RSpec for implementation verification - mathpix-gem for LaTeX/mathematical content extraction - Pattern matching on syntax trees for formula validation Enables iterative discovery and verification of mathematical properties through executable specifications.
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。
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)
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。
Build and navigate DSP (Data Structure Protocol) — graph-based long-term structural memory of codebases for LLM agents. Stores entities (modules, functions), their dependencies (imports), public API (shared/exports), and reasons for every connection. Use when: (1) project has a .dsp/ directory, (2) user asks to set up DSP or bootstrap project structure, (3) creating/modifying/deleting code files in a DSP-tracked project, (4) navigating project structure, understanding dependencies, or finding modules, (5) user mentions DSP, dsp-cli, .dsp, or structure mapping.
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 asks for a read-only, actionable audit of Python code quality, modernization, maintenance burden, ecosystem opportunities, or Pythonic design across a PR, git diff, staged/unstaged changes, files, or directories.
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 independently assessing how bounded Python code could accomplish the same behavior with less maintained code, more Pythonic design, newer language/stdlib capabilities, or well-maintained ecosystem libraries.
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。
Reviews Python code across type safety, error handling, security, performance, modern patterns, design clarity, typed-boundary compliance, test quality, and documentation. Use when performing code review, PR review, pre-merge quality checks, or assessing Python for security vulnerabilities, bare except clauses, Any usage outside boundaries, or missing input validation at system boundaries.
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 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.
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 creating a new skill or updating an existing skill that extends Claude's capabilities with specialized knowledge, workflows, or tool integrations. Activates on "create a new skill", "add a skill to plugin", or "update existing skill".
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。
Autonomous feature research and gap analysis. Use when starting /add-new-feature or analyzing existing architecture documents. Explores codebase patterns, identifies ambiguities, and produces feature-context-{slug}.md for orchestrator RT-ICA phase. Does NOT make technical decisions.
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。
Bash 5.3 release features and improvements with practical examples. Use when working with Bash 5.3 features, new command substitution, GLOBSORT, loadable builtins, or when user asks about Bash 5.3 changes, new features, or version-specific capabilities.
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。
Bash 5.1 release features and improvements with practical examples. Use when working with Bash 5.1 features, epoch time variables, redirection enhancements, or when user asks about Bash 5.1 changes, new features, or version-specific capabilities.
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。
Bash 5.2 release features and improvements with practical examples. Use when working with Bash 5.2 features, variable handling enhancements, readline improvements, or when user asks about Bash 5.2 changes, new features, or version-specific capabilities.
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。
Design, create, run, monitor, and maintain Vapi Simulations for assistants and squads. Use for turning plain-language requirements into a coverage plan, simulation personalities, scenarios, structured-output success criteria, simulations, suites, extending an existing suite, chat or voice runs, tool mocks, target variables, lifecycle webhooks, regression coverage, launch readiness, CI quality gates, run-result analysis, and simulation API validation errors. Do not use for fixed-turn mock-conversation Evals unless the user is deciding between Evals and Simulations.
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。
Survey the AI coding assistant and agentic tool ecosystem, assess skillsaw's competitive position, identify emerging patterns and missing capabilities, and produce a prioritized strategic report as a GitHub issue. Use when evaluating ecosystem trends and product gaps.
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。
Reviews a whole deliverable (talk, course module, paper, chapter, or referee report) against its manifest without rewriting it. Runs a deterministic gate for build, citations, numbers, facts, and leaks and stops on failure, then runs a finder over each section and a strong-model pass over the whole work, and adjudicates into checks/<date>/findings.json and report.md with anchored findings, severities, and honest NOT CHECKED coverage within a dollar budget. Use when asked to lint or review the whole talk, module, or paper, for a pre-ship review, or for the night shift. Revision stays serial and author-led.
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 an existing repository contains agents, prompts, workflows, playbooks, commands, or Skills and you need to identify which capabilities are worth converting into a ChatGPT/Codex Plugin.
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 a set of candidate Skills or app capabilities is technically valid but still needs to become a clear, useful ChatGPT/Codex Plugin that people can understand, discover, and invoke for real work.
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。
Reads model uncertainty from token log-probabilities. Covers collecting logprobs and aggregating multi-token labels, confidence tiers and margins for triage, calibration assessment with ECE, Brier scores, and reliability diagrams against human labels, responsible downstream use of confidence, and reproducibility archives. Use when the user asks about classifier confidence by item, logprobs, top-k tokens, calibration, ECE, Brier, reliability diagrams, or routing low-confidence cases to human review. Codebook and validation design go to text-classification.
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。
Reads a model's own uncertainty from token log-probabilities: collecting and aggregating label logprobs, confidence tiers and margins for triage, calibration against human labels (ECE, Brier, reliability diagrams), downstream use, and reproducibility. Use for per-item classifier confidence or routing low-confidence cases to human review; codebook and validation design go to text-classification.
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。
Time and timezone utilities for getting current time and converting between timezones. Use when: (1) Getting current time in any timezone, (2) Converting time between different timezones, (3) Working with IANA timezone names, (4) Scheduling across timezones, (5) Time-sensitive operations. Triggers: "what time is it", "current time", "convert time", "timezone", "time in [city]".
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。