「codebase」の検索結果

Hunts for the other instances of a bug already found — the variants of one root cause across a codebase. Use immediately after a vulnerability, logic bug, or bad pattern turns up in a specific file and the question becomes where else it occurs, including the bare conversational form ("are there others like this?", "is this the same bug?"). Also for generalizing one known instance into a CodeQL or Semgrep query for its whole pattern family, and for triaging a set of look-alike candidates against a known root cause. Not for initial discovery with no bug in hand.

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。

Runs a Trailmark summary analysis on a codebase. Returns auto-detected languages, entry point count, and dependency list. Use when vivisect or galvanize needs a quick structural overview. Triggers: trailmark summary, code summary, structural overview.

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。

Understand a codebase before looking for bugs in it - what each function assumes, what it guarantees, and what it depends on elsewhere. Use when starting an audit, threat model, or architecture review on unfamiliar code, and before any vulnerability-hunting pass.

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。

Performs security-focused differential review of code changes. Adapts analysis depth to codebase size, uses git blame for context, calculates blast radius by counting callers, checks test coverage of modified code, and generates a markdown report. Use when reviewing a PR, commit, or diff for security vulnerabilities, checking whether a change re-introduces a previously fixed bug, asking what else a change could break, or finding which modified code has no test covering it.

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。

Analyzes smart contract codebases to identify state-changing entry points for security auditing. Detects externally callable functions that modify state, categorizes them by access level (public, admin, role-restricted, contract-only), and generates structured audit reports. Excludes view/pure/read-only functions. Use when auditing smart contracts (Solidity, Vyper, Solana/Rust, Move, TON, CosmWasm) or when asked to find entry points, audit flows, external functions, access control patterns, or privileged operations.

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 a codebase for security vulnerabilities using CodeQL's interprocedural data flow and taint tracking analysis. Triggers on "run codeql", "codeql scan", "build codeql database", "SAST scan", "taint analysis", "dataflow analysis", or "find vulnerabilities in this repo". Covers Python, JavaScript/TypeScript, Go, Java/Kotlin, C/C++, C#, Ruby, and Swift. Supports "run all" (security-and-quality + security-experimental) and "important only" (high-precision) scan modes, and creates data extension models for project-specific sources and sinks. For fast single-file pattern matching, or when no build is available for a compiled language, use the semgrep skill; to parse SARIF that already exists rather than produce it, use the sarif-parsing 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。

Runs a Semgrep security scan over a codebase: detects languages, selects rulesets, presents the plan for explicit approval, then runs every approved ruleset through scripts/run-scans.sh, which batches the semgrep processes and writes scans.json, and merges the output to SARIF. Supports two scan modes, "run all" for full ruleset coverage and "important only" for security findings at medium-to-high confidence and impact. Uses Semgrep Pro for cross-file taint analysis when it is available. Use when asked to scan code for vulnerabilities, run a security audit with Semgrep, find bugs, or perform static analysis. For the same scan without the approval gate, use the /static-analysis:semgrep-scan workflow.

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。

Smart contract development advisor based on Trail of Bits' best practices. Analyzes codebase to generate documentation/specifications, review architecture, check upgradeability patterns, assess implementation quality, identify pitfalls, review dependencies, and evaluate testing. Use when asking whether a smart contract project follows development best practices, reviewing on-chain/off-chain split, upgradeability, or delegatecall proxy patterns against guidelines, or seeking recommendations on contract design, inheritance, events, documentation, dependencies, or test strategy.

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。

Load or manage Product Design's saved user context. Use when the user asks to set up Product Design, get started, onboard, save product or design sources, see what Product Design remembers, update saved context, or remember Product Design preferences. Examples include product URLs, Figma files, screenshots, reference images, codebase paths, Storybook, tokens, design systems, brand assets, and general product/design notes.

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。

Inspect a ChatGPT Apps MCP server codebase and generate chatgpt-app-submission.json with app info suggestions, tool hint justifications, test cases, and negative test cases, then report review-check findings and outputSchema warnings for submission review.

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 or update a professional-grade design system in Figma from a codebase. Use when the user wants to create variables/tokens, build component libraries, create individual components with proper variant sets and variable bindings, set up theming (light/dark modes), document foundations, or reconcile gaps between code and Figma. Also use when the user asks to create or generate any component in Figma — even a single one — since components require proper variable foundations, variant states, and design token bindings to be production-quality. This skill teaches WHAT to build and in WHAT ORDER — it complements the `figma-use` skill which teaches HOW to call the Plugin API. Both skills should be loaded together.

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。

Generative Engine Optimization (GEO) specialist — the technical, on-page publishing work that makes a given page or site discoverable, understandable, trustworthy, quotable, and fresh for AI answer engines (Google AI Overviews, ChatGPT Search, Bing Copilot, Perplexity). Use when asked to 'optimize this page/site for GEO', 'optimize for AI search / answer engines', 'get my page cited by ChatGPT/Perplexity', 'improve AI visibility/citability', 'write an llms.txt', 'add citation-ready structure or schema for AI answers', 'otimizar para busca com IA', or to audit/create/improve a codebase for generative search. Do NOT use for AI-driven SEO content strategy or programmatic pages at scale (use ai-seo), classic keyword/SERP ranking (use seo), accessibility (use web-accessibility), or multi-area site audits (use web-quality-audit).

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。

Repository-grounded threat modeling that enumerates trust boundaries, assets, attacker capabilities, abuse paths, and mitigations, and writes a concise Markdown threat model. Use when the user asks to threat model a codebase or path, enumerate threats or abuse paths, or perform AppSec threat modeling. Do NOT use for general architecture summaries, code review, security best practices (use security-best-practices), or non-security design 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。

Groups existing components into logical business domains to plan service-based architecture. Use when asking "which components belong together?", "group these into services", "organize by domain", "component-to-domain mapping", or planning service extraction from an existing codebase. Do NOT use for identifying new domains from scratch (use domain-analysis) or analyzing coupling (use coupling-analysis).

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 planning legacy system migrations, codebase modernization, monolith decomposition, microservices consolidation, cross-language rewrites, or framework upgrades. Invoke for strangler fig pattern, incremental migration strategy, or refactoring roadmaps. Do NOT use for domain analysis (use domain-analysis), component sizing (use component-identification-sizing), or step-by-step decomposition plans (use decomposition-planning-roadmap).

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。

Your pathfinder for navigating unknown codebases. Investigates with precision, implements surgically, and never assumes — if it doesn't know, it says so. Maintains a .notebook/ knowledge base that grows across sessions, turning every discovery into lasting intelligence. Summons available skills, MCPs, and docs when the mission demands. Use when fixing bugs, implementing features, refactoring, investigating flows, or any development task in unfamiliar territory. Triggers on "fix this", "implement this", "how does this work", "investigate this flow", "help me with this code". Do NOT use for greenfield scaffolding, CI/CD, or infrastructure provisioning.

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 senior-operator mode for AI agents that resolve tasks end to end without babysitting and never create new problems. The agent verifies every claim against real evidence (web search dated to the current month and year, the codebase, and available tools, MCPs, and CLIs); it never guesses, never fakes confidence, and never claims something is done without proof. It stays silent and keeps working, interrupting the user only on three stops, namely a destructive or irreversible action, a dead-end with no evidence after exhausting sources, or genuine ambiguity that changes the outcome. Output is short, literal, and human. Use when the user says "not-your-babysitter", "nanny mode", "work autonomously", "stop babysitting", or "no hand-holding", or wants an agent that solves problems on its own, especially hands-on engineering and operational tasks. Do not use when the user explicitly wants a tutorial, a verbose walkthrough, or open-ended brainstorming.

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。

Deploy applications to Render by analyzing codebases, generating render.yaml Blueprints, and providing Dashboard deeplinks. Use when the user wants to deploy, host, publish, or set up their application on Render's cloud platform. Do NOT use for deploying to Vercel, Netlify, or Cloudflare (use their respective skills).

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 or update a professional-grade design system in Figma from a codebase. Use when the user wants to create variables/tokens, build component libraries, create individual components with proper variant sets and variable bindings, set up theming (light/dark modes), document foundations, or reconcile gaps between code and Figma. Also use when the user asks to create or generate any component in Figma — even a single one — since components require proper variable foundations, variant states, and design token bindings to be production-quality. This skill teaches WHAT to build and in WHAT ORDER — it complements the `figma-use` skill which teaches HOW to call the Plugin API. Both skills should be loaded together.

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 when implementing a Figma design as code (design → code) — the read-FROM-Figma direction. Triggers: 'implement this Figma design', 'build this screen from Figma', 'turn this Figma into code', 'code this up from Figma', 'design to code', or a figma.com design URL provided alongside a codebase. Encodes the workflow for reading a node out of Figma with get_design_context and adapting its reference output to the target project — reusing existing components, tokens, and conventions, and honoring Code Connect mappings, component docs, design annotations, and design tokens by priority. Complements figma-code-connect (component mapping) and figma-generate-design / figma-use (the reverse, code → design direction).

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 user asks to "build a Solana dapp", "write an Anchor program", "create a token", "debug Solana errors", "set up wallet connection", "test my Solana program", "deploy to devnet", or "explain Solana concepts" (rent, accounts, PDAs, CPIs, etc.). Also use for quick on-chain lookups via public RPC + curl — "what's the balance of <wallet>", "look up transaction <sig>", "token balance for <account>", "check this address on mainnet/devnet". End-to-end Solana development playbook covering wallet connection, Anchor/Pinocchio programs, Codama client generation, Surfpool/LiteSVM/Mollusk testing, security checklists, and JSON-RPC curl lookups against public clusters. Prefers @solana/kit v7 plugin clients (createClient + .use()), wallet connection via @solana/kit-plugin-wallet + @solana/react, web3.js v3 (Kit internals, RC) as the migration target for legacy codebases, and Surfpool for local networks and integration testing.

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。

Analyzes your codebase and asks 3 targeted questions to recommend the right AI-assisted development methodology stack

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。

Detect, suggest, and evaluate GoF design patterns in TypeScript/JavaScript codebases. Use when refactoring code, applying singleton/factory/observer/strategy patterns, reviewing pattern quality, or finding stack-native alternatives for React, Angular, NestJS, and Vue.

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。

Analyzes your codebase and asks 3 targeted questions to recommend the right AI-assisted development methodology stack

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。