3.9K skills · sorted by relevance
Turn implementation plans, pull request changes, and technical concepts into HTML visual explanations with Plannotator styling. Present diagrams, mockups, and review priorities through an annotation interface, with approval controls for plans and proposals.
Investigate Bifrost GitHub issues by examining reports, related code, and library documentation. Produce an implementation plan with impact analysis and regression tests, including a demonstrated failing test for bugs before requesting approval to fix them.
Guide a development team from an assigned user outcome through implementation, testing, and handoff. It covers reproducing defects, checking behavior against requirements, involving design when needed, and reporting the exact changes, verification results, and remaining uncertainty.
Uses a known or suspected vulnerability to find related code through Trailmark's graph of calls, types, and implementations. Produces ranked review targets with reasons and limitations for follow-up security analysis.
Assess how applications use encryption, signed tokens, and random values. Identify implementation weaknesses, select relevant testing tools, and document causes and fixes for issues involving encryption modes, RSA parameters, predictable randomness, and key derivation.
Create a technical design document before implementation by answering questions about the problem, scope, architecture, risks, and delivery plan. The skill adjusts sections to project size and covers security, testing, monitoring, and recovery where relevant.
Plan and implement features with a workflow that adapts to their complexity. It connects testable requirements to tasks, tests, and small commits, then checks the finished work against the specification through an independent verification pass.
Choose a visual direction for landing pages using WebGL, a browser graphics technology. Balance brand style with readable copy, clear calls to action, mobile performance, accessibility, and implementation effort, then define fallbacks and build priorities.
Clean up DeepChat's specification-driven development documents after implementation and validation. Review feature, issue, and architecture files, remove completed or obsolete material when evidence supports deletion, and preserve maintained specifications while checking references.
Plan substantial DeepChat changes with a written design proposal and a clear implementation path. Record decisions, track progress, review the completed change, and select focused validation for features, complex bugs, or architecture updates.
Turn a feature request into an implementation plan before coding. Five planning phases cover requirements, interface design, technical choices, research, and metrics, producing ordered tasks, a test plan, and records explaining major decisions.
Validate an implementation plan before execution through structural checks and specialist agent reviews. Check dependencies, files, tests, and architectural decisions, then update the plan to resolve findings, requesting human decisions when existing guidance is insufficient.
Guide new feature work from requirements and planning through revisions and implementation. Create Markdown plans with acceptance criteria, track progress, and ensure frontend tasks have a UI design before development begins.
Implement pending tasks from an OpenSpec change, a planned set of development work. The skill reads relevant specifications and design files, makes focused code changes, and updates task completion as work progresses.
Organize gh-aw Go code with guidance on splitting files by function, placing shared helpers, and keeping tests near implementations. It also explains string cleanup conventions and replacement implementations for features unavailable in WebAssembly builds.
Develop user-visible improvements for cc-wf-studio, verify their basis in the code, and assess their value. Record qualifying proposals as locked GitHub issues, creating a queue of concrete ideas for later implementation without changing code.
Create an interactive HTML implementation plan that connects proposed behavior to mockups, code, and data structures. Readers can explore details, choose options, edit schemas, and return comments before development starts.
Run a research task through five ordered stages: brainstorming, planning, implementation, review, and documentation. The workflow checks that brainstorming and planning files exist before proceeding, then reviews and documents what the implementation stage produced.
Keep implementation work moving by turning routine uncertainty into explicit assumptions. The agent follows existing project patterns, makes small reversible changes, validates results, and reports its decisions, completed changes, checks, and remaining risks.
Review new and existing tests for meaningful behavior coverage, duplication, and dependence on implementation details. Gather evidence before cleanup, preserve tests that protect independent contracts, and verify that removed tests leave their regression checks covered elsewhere.
Fetch dependency source code to give AI agents deeper implementation context. Use when the agent needs to understand how a library works internally, read source code for a package, fetch implementation details for a dependency, or explore how an npm/PyPI/crates.io package is built. Triggers include "fetch source for", "read the source of", "how does X work internally", "get the implementation of", "opensrc path", or any task requiring access to dependency source code beyond types and docs.
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Takes a new customer from signature to working — setting a definition of live that both sides agreed before the contract was signed, planning and staffing the implementation, running data migration and integration realistically, training the people who will actually use it, and handing over to the ongoing relationship. Use this to design an onboarding motion, rescue a stalled implementation, work out why customers who bought never went live, or scope the services a deal actually needs.
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AI DevKit · Analyze and simplify existing implementations to reduce complexity, improve maintainability, and enhance scalability. Use when users ask to simplify code, reduce complexity, refactor for readability, clean up implementations, improve maintainability, reduce technical debt, or make code easier to understand.
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Converts an arxiv paper into a minimal, citation-anchored Python implementation. Trigger when user runs /paper2code with an arxiv URL or paper ID, says "implement this paper", or pastes an arxiv link asking for implementation. Flags all ambiguities honestly. Never invents implementation details not stated in the paper.
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