Loading...
Loading...
Found 910 Skills
Help set up and troubleshoot rendering for task files and glossary files. Use when the user asks about PlantUML, AsciiDoc, VS Code, JetBrains, preview rendering, Java, or Graphviz needed to review rendered task or glossary files.
YAML functions: !terraform.state, !terraform.output, !store, !store.get, !env, !exec, !include, !template, !literal, !random, !aws.*, !cwd, !repo-root
Optional skill. Reconstruct a human-review-preparation file from an existing pull request, merge request, branch diff, or commit range in a repository the user trusts. Use when the user wants retrospective understanding of already-implemented changes, AI-side assessment and recommendations, and an optional provider-specific sharing variant written to a local file when needed.
Extract and structure personal context from AI chat transcripts into themed markdown files. Use when (1) Processing Claude, Claude Code, or other AI conversation exports, (2) Building personalized AI assistants from chat history, (3) Creating context files for Claude Projects, GPTs, or Gems, (4) Consolidating scattered knowledge from multiple conversations. Optimized for Claude Haiku.
Mandatory only on the task-file path of `spec-loop-plan-task` after implementation approval. Use when implementation deviates from the approved task, when uncertainty must be clarified instead of guessed through, when new blocking questions arise, or before presenting the current task or subtask to the User. Governs clarification routing, canonical task updates when explicit User clarification, accepted review feedback, or explicit post-implementation User approval changes the current task definition, the post-implementation `Implementation notes` check, and whether the increment may move to `review`. May be applied in parallel with other implementation-related skills.
Mandatory unless the user explicitly opts out. Use when non-trivial work on features, bug fixes, refactorings, or changes to code, tests, configuration, dependencies, runtime assets, or design requires explicit planning before implementation.
Helps users install, run, and integrate YouSkills (visual manager for local AI skills). Use when the user asks about you-skills, local skills visualization, viewing installed skills, npx you-skills, port 12434, or integrating with Codex/Cursor/OpenClaw skills.
Use this skill when adding authentication, handling user input, working with secrets, creating API endpoints, or implementing payment/sensitive features. Provides comprehensive security checklist and patterns.
Convex backend specialist. Use this agent for any code inside a `convex/` directory — function definitions, schemas, indexes, queries, mutations, actions, HTTP endpoints, cron jobs, file storage, auth wiring, and component installation. Knows the object-form function syntax, validator patterns, resource limits, and component ecosystem that generic Claude routinely gets wrong.
Guides stable API and interface design. Use when designing APIs, module boundaries, or any public interface. Use when creating REST or GraphQL endpoints, defining type contracts between modules, or establishing boundaries between frontend and backend.
阿里云百炼模型精调训练入口：用户要精调、微调、训练自己的模型（fine-tune，支持 SFT / SFT-LoRA / DPO / DPO-LoRA / CPT， 覆盖文本、语音、图像）、校验或上传训练数据集、看训练进度和日志、挑 checkpoint、导出精调产物、 把专属模型部署成服务时使用 `bl dataset` / `bl finetune` / `bl deploy`。链路是 validate 校验数据 → upload 拿 file-id → finetune create 建任务 → watch 看进度 → export 导出 → deploy 上线，需要 API key； 写操作先用 `--dry-run` 预览。反触发：用户点名火山方舟/ark 的精调不走本 skill；只是要选哪个模型走 bailian-model-recommend；用现成模型生图生视频走 bailian-gen；百炼其他资源管理走 bailian-cli。 官方安装：`npx skills add modelstudioai/cli --all -g`（与共享协议 bailian-protocol 同装）。
Mine GitHub, GitLab, and git history for identities, infrastructure, and leaked credentials using commit author emails, GitHub code search, the commit .patch endpoint, trufflehog, gitleaks, git log pickaxe, and full-ref history scans. Use when investigating a developer or organisation on GitHub, finding leaked API keys, AWS access keys or tokens in code, enumerating org members and their personal repos, recovering secrets deleted from HEAD but present in history or forks, or checking exposed .git directories, gists, and CI logs.