200 个 · 按相关度排序
Reviews code for common security weaknesses, authentication problems, exposed credentials, and unsafe configuration. It adjusts review depth to the changes and offers an adversarial workflow, where defensive and attacking perspectives guide fixes and subsequent verification.
中文简介正在准备中。当前显示英语简介。
Estimate remaining workflow costs from the average spend of completed steps. Show actual spending and projected totals in the status line, warn about configured budget overruns, and suggest a budget profile when estimated costs are high.
中文简介正在准备中。当前显示英语简介。
Research complex topics across multiple sources using available external AI providers, then combine the findings. Choose the depth, focus, and output format to receive a detailed report, concise summary, comparison table, or recommendations.
中文简介正在准备中。当前显示英语简介。
Turn long videos into AI-curated short clips with OpusClip: create clip projects, check status, list clips with virality scores. Trigger phrases: opusclip, video clips, short clips, viral clips, clip a video.
中文简介正在准备中。当前显示原文说明。
Run fable-mode execution discipline on Claude Fable 5.1 — the top of the escalation ladder and the strongest staged run available. Routes the task to the @fable-orchestrator agent with its model overridden to Fable (`model: "fable"`), which stages the work, delegates artifact production to @fable-worker-sonnet / @fable-worker-haiku, and cold-checks deliverables with @fable-verifier. Trigger when the user explicitly asks for thorough/systematic/"deep work" handling on the strongest model ("fable on fable", "stage this on fable", "deep work mode, fable", "run this on 5.1"), or when a fable-opus run reports the task exceeds Opus's ceiling. Benchmarked 2026-09-02: zero correctness delta on Fable 5.1 at ~1.1× tokens, ~2× wall — a provenance runner, not an accuracy runner. Do NOT use for ordinary single-pass tasks — prefer fable-opus, fable-sonnet, or fable-haiku when the task doesn't need peak reasoning. Fable is plan-included; bulk work still routes down-tier.
中文简介正在准备中。当前显示原文说明。
Choose an agent and model tier using the task description and learned patterns. The skill can explain its recommendation, use compiler-based edits for supported simple changes, and record task outcomes to inform future routing decisions.
中文简介正在准备中。当前显示英语简介。
Run a prepared set of test cases to check agent-booster success rates and routing decisions. Optionally compare results with Gemini or Claude models, then save detailed measurements and summaries as JSON records for later verification.
中文简介正在准备中。当前显示英语简介。
Organize Happy's GitHub issues, find duplicates, and check whether reported bugs are fixed on main, the primary development branch. Prepare replies and proposed actions for maintainer review, with explicit approval required before changes affecting contributors.
中文简介正在准备中。当前显示英语简介。
Interpret the saved results of a completed AI council review. Count verdicts, check whether enough participants responded and whether models are independent, surface substantive dissent, and explain what action the outcome supports.
中文简介正在准备中。当前显示英语简介。
Research technical, market, or academic topics through multiple available AI providers working in parallel. Combine their findings into a report that preserves disagreements, identifies gaps, attributes sources, and recommends next steps with priorities.
中文简介正在准备中。当前显示英语简介。
Turn Markdown research and strategy outputs into office documents for sharing or presentation. Choose formats such as DOCX, PPTX, or PDF, organize sections for the audience, and apply consistent headings, tables, and slide formatting through document-skills.
中文简介正在准备中。当前显示英语简介。
Keep a persistent record of project bugs and blockers in a local Markdown file. List issues by severity and category, inspect individual records, and save resolution notes so problems and their fixes remain available across sessions.
中文简介正在准备中。当前显示英语简介。
Resume a previous workflow after a new conversation or context reset. Read saved project state and handoff notes, summarize recent progress and blockers, and identify the appropriate next action for planning, implementation, or delivery.
中文简介正在准备中。当前显示英语简介。
Review code with multiple AI perspectives to identify quality, security, performance, and design issues. The workflow also examines test evidence and unfinished implementation, then combines findings into a review that can be shared on a pull request.
中文简介正在准备中。当前显示英语简介。
Review system architecture using implementation, callers, and tests as evidence. Compare interface designs, clarify how components should interact, and receive a recommendation with migration steps and test implications before deciding whether to refactor.
中文简介正在准备中。当前显示英语简介。
Clarify a feature’s requirements with multiple AI providers, combining technical constraints with user and business needs. Produce a scoped definition covering priorities, edge cases, exclusions, and a requirements checklist before implementation begins.
中文简介正在准备中。当前显示英语简介。
Run a build-and-evaluation pipeline from a natural-language specification. Generate scenarios, implement the project, test scenarios withheld during implementation, and report scores against a target with failures and suggested next steps.
中文简介正在准备中。当前显示英语简介。
Breaks large changes, migrations, or multiple fixes into clearly scoped work packages. Independent workers use separate Git working directories, run checks, and report results, while dependency validation determines which packages can run together.
中文简介正在准备中。当前显示英语简介。
Review code or documents with available AI providers before delivery. Combine findings into a scored validation report that checks prior requirements and decisions, separates critical issues from warnings, and recommends next steps.
中文简介正在准备中。当前显示英语简介。
Research technical or business questions through multiple available AI providers, each assigned a different perspective. Combine usable results into a report covering findings, disagreements, evidence gaps, priorities, and recommended next steps.
中文简介正在准备中。当前显示英语简介。
Coordinates multiple AI providers to research, define, implement, and validate development tasks. It supports parallel task decomposition, comparison of proposed solutions, cross-model critique, and quality checks that feed into combined reports and final deliverables.
中文简介正在准备中。当前显示英语简介。
Save design documents with branch, author, and revision history so later sessions can recover earlier decisions. Keep previous versions intact, search related designs, and carry constraints and recommendations into implementation or review.
中文简介正在准备中。当前显示英语简介。
Create a structured specification in plain language from a feature description and multi-AI research. Define actors, behaviors, constraints, dependencies, and acceptance conditions, while checking completeness and keeping unresolved user decisions visible for later review.
中文简介正在准备中。当前显示英语简介。
Design and build a 10-30 second motion-design showreel or brand reel in the style of Nate's Opus 5.5 "CLAUDE / MOTION REEL": one brand motif transformed through labeled craft chapters, a persistent HUD, cuts locked to a 120-130 BPM grid, a sub-second flurry, and a held logo lockup that answers the opening. Use when asked for a showreel, sizzle reel, brand reel, motion reel, "a reel like the Opus one", "show off motion design for <brand>", a 15-second brand film cut to music, or to study why a reference reel works and rebuild that energy for another brand.
中文简介正在准备中。当前显示原文说明。