1,284 件 ・ 関連度順
概要と使いどころ
Mine bug patterns from any git repository. Discovers bug-fix commits via git log heuristics, analyzes each in parallel with subagents, writes individual analysis files, and synthesizes a generalized PATTERNS.md with repo-specific details stripped. Invoke explicitly with /bug-archaeology.
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。
Deep file-focused code review for correctness bugs. Unlike shallow-review which runs 6 specialists in parallel across the entire patch, deep-review focuses on user-specified files with full pattern catalogs (500+ patterns), codebase investigation, and source-level context gathering. Use when: the user specifies particular files for focused review, a shallow review flagged areas that need deeper investigation, reviewing critical-path code changes, examining complex serialization/lifecycle/state-machine changes. The user instructs which files to focus on (typically a subset of files in the patch).
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。
Shallow (quick) ensemble bug-finding review using 6 specialist agents in parallel. Each specialist reviews the same patch through a different lens: Logic & Types, Boundaries & I/O, Concurrency & State, Resources & Serialization, Absence Analysis, and API Completeness. Findings are merged and deduplicated. Best for: quick first-pass review of patches, triage of diffs, broad surface-level bug scan. For deeper file-focused review with full pattern catalogs and codebase investigation, use the deep-review skill instead.
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。
Findings-driven targeted code review. Understands the patch via patch-explainer and surrounding code via codebase-analysis, then consults a catalog of bug patterns (~11 categories under references/categories/). Picks only categories whose diff-signals match the patch and items whose "look for" hints match actual code shapes, groups them by review focus (function/file/feature), and spawns parallel subagents — each with a tight scope and selective checklist. Use for medium-to-large patches (50-1000 LOC) as a focused alternative to whole-patch ensemble or whole-file deep review. Triggers on: "review this patch/diff/change", "find bugs in this change", "scoped review", "what could go wrong here", "review using findings", or proactive PR 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。
当面对 2 个以上可以独立进行、无共享状态或顺序依赖的任务时使用
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。
Trigger: a mapping need, parallel units, context backstop, high-risk verify, fresh review, or multi-step debug. Delegate via delegate_task for a named reason.
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。
Static source-code vulnerability scan. Reads a target directory (and THREAT_MODEL.md if present), spawns parallel review subagents per focus area, and writes VULN-FINDINGS.json + .md for /triage to consume. Read-only — no building, running, or network. For execution-verified crashes, use vuln-pipeline instead. Use when asked to "scan for vulns", "review this code for security issues", "find bugs in <dir>", or as the step between /threat-model and /triage.
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。
Sets up and runs AFL++ for multi-core fuzzing of C/C++ projects built with afl-clang-fast or afl-gcc-fast. Covers instrumentation modes, parallel main and secondary campaigns, persistent mode, corpus minimization, and crash triage. Use when scaling fuzzing across cores, fuzzing a mature C/C++ codebase, reading the afl-fuzz status screen, or moving on after libFuzzer has plateaued.
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。
Advanced API exploitation methodology focused on business logic abuse and sophisticated attack patterns that bypass traditional security controls. Covers business logic bypass through API call chaining and workflow manipulation. Addresses GraphQL-specific attacks including batching for credential brute-force, query depth exploitation, and introspection abuse. Includes pagination exploitation for data exfiltration, webhook hijacking for SSRF and data interception, and resource exhaustion through algorithmic complexity attacks. Covers race conditions in API transactions using parallel request techniques. Provides comprehensive JWT manipulation including algorithm confusion, kid injection, jku/x5u abuse, and claim tampering. Details API key leakage detection across source repositories, client-side code, and error messages. Covers undocumented endpoint discovery through predictable naming, debug routes, and source map analysis. Tooling includes Arjun, ParamSpider, jwt_tool, and GraphQL Voyager. Designed for authorized penetration testers targeting business logic layers that automated scanners miss.
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。
Route broad or ambiguous life-sciences research requests to the right skills, normalize core entities, optionally parallelize independent evidence gathering with subagents when available, and synthesize a concise evidence-backed answer. Use when a user asks a general life-sciences question that could span multiple sources or analysis types.
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。
Optimize CircleCI configuration for speed, reliability, and maintainability. Use when users ask to improve `.circleci/config.yml`, reduce CI runtime, tune caching/workspaces/parallelism, remove pipeline waste, or fix flaky pipeline behavior caused by configuration choices.
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 you have a task and must decide WHO does it — yourself, a spawned subagent, or another agent in the topology that already holds the context. The rule: how much would a blank slate have to read in to do this correctly? A lot → route to a context-holding agent; a little → subagent; unsure → a real agent or yourself. NOT the mechanics of fanning out (that's dispatching-parallel-agents / subagent-driven-development) — this is the who/whether decision, and the failure mode that makes teams abandon subagents.
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 multi-agent AI workflows with clear boundaries, handoffs, and monitoring. Use when a complex PM task should run as parallel specialized agents instead of one linear process.
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。
Mandatory iPolloWork code-change gate for modern, minimal, performant, reuse-first implementation and clean repository ownership. Use whenever AI creates, edits, deletes, or refactors application code, server code, packages, scripts, tests, dependencies, schemas, routes, UI, or generated-file workflows. Reuse existing code before creating files, keep one source of truth, prevent parallel implementations and junk directories, justify every new file or dependency, and audit the current change before completion.
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Build a burning-paper ring loader whose GLSL flame is drawn by a Worker on an OffscreenCanvas started from the head, so a main thread blocked by the page build cannot freeze it, with a progress count, an even-paced run into the hand-off, and a clean fade to the page. Covers the ember thread and ember gradient, porous char, worker start-up, parallel shader compile with a waited 1-px warm-up draw, hand-off without a stall, reduced motion and hidden-tab behaviour. Use for a loading screen, preloader, page loader, splash, "kindling", burn loader, ember ring, burning paper hole, fire loader, or a loader that must keep moving while heavy JavaScript, three.js or shader builds hold the main thread.
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。
Real-time search engine supporting web search, vertical domain search, parallel batch search, and URL content extraction.
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。
比较数据、张量、序列、上下文、流水和专家并行的容量与通信代价。用于决定多卡切分和超节点规模。
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。
Execute a validated plan: worktree isolation, TDD scaffolding, level-based parallel agents, quality gate with smoke test, PR creation and merge. Handles everything through to merged PR.
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。
Add descriptive prose to bare sections in whitepapers (FR + EN) using 9 parallel agents
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。
Archive multiple completed changes at once. Use when archiving several parallel changes.
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 the user asks to "remove AI writing patterns", "humanize this text", "make this sound more natural", "remove AI-generated traces", "fix robotic writing", or needs to eliminate AI writing patterns from prose. Supports both English and Chinese text. Based on Wikipedia's "Signs of AI writing" guide, detects and fixes inflated symbolism, promotional language, superficial -ing analyses, vague attributions, AI vocabulary, negative parallelisms, and excessive conjunctive phrases.
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 QwenPaw-backed Worker needs to decide whether to do work directly, use native subagents for internal parallelism, or create a temporary QwenPaw agent with a custom AGENTS.md and 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。
MUST USE for C#/.NET deterministic tests that require the smallest production seam for DateTime/Task.Delay/File/Environment/Guid/Random, static API preservation, nested/parallel overrides, or no real I/O. USE ONLY when the target workspace contains C# source plus a .csproj or .sln. DO NOT USE for audits, bulk migration, code that already has an injectable seam, or an explicit migration to a user-named existing abstraction (migrate-static-to-wrapper). Use instead of general test generation when the requested test is impossible without a production edit and seam selection is still open.
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。
Convert .NET tests from xUnit.net v2/v3 to MSTest v4 while preserving VSTest or MTP. Use for replacing xunit packages, Fact/Theory/InlineData/MemberData, assertions, IClassFixture/ICollectionFixture, ITestOutputHelper, TestContext cancellation, traits/Owner, skips, timeouts, and xUnit parallelization. Also use when a "convert xUnit to MSTest" request may already be migrated: inspect and report the no-op. Do not use for xUnit v2-to-v3, MSTest upgrades, NUnit/TUnit conversion, or runner-only VSTest-to-MTP migration.
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。