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Skills 列表
delivery-gate
Stop hook that blocks Claude from finishing until quality checks pass. Detects rationalization patterns (surface text heuristics), stale learning logs (filesystem mtime), and low disk space. Complements self-audit by mechanically enforcing learning capture habits. Use when Claude should be mechanically blocked from declaring work finished before quality checks and learning capture actually pass.
affaan-mmailtrap-email-integration
Guides agents through integrating transactional email sending via Mailtrap's Email API, including sandbox testing, domain verification, and API authentication. Use when implementing email-sending features, debugging delivery issues, or setting up safe dev/staging email testing.
affaan-mml-adoption-playbook
End-to-end methodology for AI agents and software engineers to add machine learning algorithms to existing non-ML codebases. Covers problem framing, data readiness, architectural decoupling, and baseline model integration. Use when adding a machine learning capability to a codebase that has none, from problem framing through a baseline model.
affaan-mgenerating-python-installer
Commercial-grade Python installer expert for Windows: Nuitka extreme compilation, dist slimming, DLL footprint analysis, and Inno Setup packaging to ship the smallest, fastest installers. Use when a Python app must ship as a minimal, fast-starting Windows installer; not for basic script-to-exe conversion. 中文触发：Nuitka 极限优化、Python 商业打包、极限编译 Python、dist 瘦身、DLL 分析、最小安装包、最快启动、商业级打包风格
affaan-mteam-agent-orchestration
Run team-based orchestration for agent squads using work items, ownership, agent Kanban, merge gates, and control pane handoffs. Use when coordinating an agent squad with work items, ownership, Kanban, and merge gates.
affaan-mdynamic-workflow-mode
Design task-local harnesses, eval gates, and reusable skill extraction for Claude dynamic workflow mode and other adaptive agent harnesses. Use when building a task-local harness, adding eval gates, or extracting a reusable skill from ad-hoc work.
affaan-mkubernetes-patterns
Kubernetes workload patterns, resource management, RBAC, probes, autoscaling, ConfigMap/Secret handling, and kubectl debugging for production-grade deployments. Use when writing or reviewing Kubernetes manifests, or debugging probes, RBAC, autoscaling, or resource limits.
affaan-mcodehealth-mcp
Real-time structural Code Health via CodeScene MCP — review before edits, verify score deltas after changes, gate commits and PRs. Use when reviewing code quality, refactoring, checking if AI changes degraded a file, or before commit/PR.
affaan-mintent-driven-development
Turn ambiguous or high-impact product and engineering changes into scoped, verifiable acceptance criteria before or alongside implementation. Use when a user asks to clarify a feature, define acceptance criteria, de-risk a security/data/migration/integration change, prepare implementation requirements for another agent, or make a complex request testable. Do not trigger for trivial edits, straightforward fixes, active debugging, code review, or implementation requests whose acceptance conditions are already clear unless the user explicitly invokes this skill.
affaan-morch-pipeline
Shared orchestration engine for the orch-* skill family. Defines the gated Research-Plan-TDD-Review-Commit pipeline, the size classifier, the agent map, and the two human gates that the orch-* operation skills delegate to. Not usually invoked directly. Not usually invoked directly; it applies when an orch-* skill delegates its gated Research-Plan-TDD-Review-Commit pipeline.
affaan-minherit-legacy-style
Legacy-project style inheritance skill. Use when the user types /inherit-legacy-style, or when onboarding an AI coding agent onto a hand-written legacy project and you need to prevent "style drift" (the model imposing its pretrained mainstream idioms onto the project). Language- and framework-agnostic — it aligns meta-architecture only, not syntax. Once run, it becomes a behavioral constraint on all subsequent coding tasks. Do NOT use for pure research or one-off questions unrelated to code-style alignment.
affaan-mconfig-gc
Garbage collection for your Claude Code configuration. Periodically scans ~/.claude (skills, memory, hooks, permissions, MCP servers, caches) for redundant, stale, orphaned, or low-value items, then walks the user through a confirm-each-deletion cleanup. Use when the user says "clean up my config", "config GC", "too many skills", "audit my setup", "my .claude is bloated", or asks for a periodic config review.
affaan-mtaste
A creative-direction (taste) layer for music videos and short-form edits in the angelcore / cloud-trance / hyperpop visual family. Distills a named-genre aesthetic vocabulary, a mood + color + light system, and a beat-synced editing grammar, then chains ECC's video skills (video-editing, fal-ai-media, remotion-video-creation, motion-*, content-engine) into one production pipeline. Use when the work is not just making a video function but making it feel intentional, when building a music video, a fancam/edit, a moodboard-driven reel, or when choosing a coherent visual direction for AI-generated b-roll.
affaan-mvue-patterns
Vue.js 3 Composition API patterns, component architecture, reactivity best practices, Pinia state management, Vue Router navigation, and Nuxt SSR patterns. Activates for Vue, Nuxt, Vite, or Pinia projects. Use when building or reviewing Vue 3, Nuxt, or Pinia code — Composition API, reactivity, or router navigation.
affaan-mbrand-discovery
Use when a brand needs to discover or articulate its identity through structured multi-session interviews. Covers purpose, positioning, audience, personality, voice, narrative, and founder-brand tension across 8 modules using laddering, 5 Whys, and projective techniques. Produces a resumable session with disk-persisted state and a master brandbook (90_SYNTHESIS.md).
affaan-mbenchmark-methodology
Use after competitive-platform-analysis has produced a tiered competitor set. Scores each competitor across nine weighted dimensions (positioning, voice, visual craft, offer packaging, evidence, enterprise-readiness, thought leadership, pricing, client's strategic tension) with explicit 1–5 rubrics and a tension-plot. Precedes competitive-report-structure.
affaan-mcompetitive-platform-analysis
Use when scoping a competitive landscape — identifying, categorising, and score-filtering a competitor set before any benchmarking begins. Decides who counts as a competitor, which tier they belong to, and which sources to mine. First step in the three-skill competitive pipeline; precedes benchmark-methodology.
affaan-magent-self-evaluation
Use after completing any non-trivial task. The agent self-rates its output on 5 axes — accuracy, completeness, clarity, actionability, conciseness — with concrete evidence per criterion. Produces a structured 1-5 scorecard with specific improvement suggestions.
affaan-mcompetitive-report-structure
Use after benchmark-methodology has produced scored competitor profile cards. Assembles findings into a decision-grade report: landscape map, competitor profiles, benchmarking matrix, white-space analysis, strategic recommendations, and team alignment trigger questions. Final step in the three-skill competitive pipeline.
affaan-muncloud
Use when managing an Uncloud cluster — deploying services, configuring Caddy ingress, adding static proxy routes for non-cluster devices, publishing ports, scaling, inspecting logs, or managing machines and volumes with the `uc` CLI.
affaan-mmarketing-campaign
End-to-end marketing campaign planning and execution. Covers audience research, positioning, campaign angle definition, landing page copy, email sequences, social posts, ad copy, short-form video scripts, and content calendars. Use as the orchestration layer for multi-channel product launches. Use when planning or executing a multi-channel product launch, or producing landing page, email, social, or ad copy.
affaan-mfrontend-a11y
Accessibility patterns for React and Next.js — semantic HTML, ARIA attributes, form labeling, keyboard navigation, focus management, and screen reader support. Use when building any interactive UI component or form.
affaan-mbenchmark-optimization-loop
Use when the user asks to make something faster, try many variants, run recursive optimization, benchmark latency/throughput/cost, or choose the best implementation by repeated measured tests.
affaan-mlatency-critical-systems
Use for latency-sensitive systems such as realtime dashboards, market data, streaming agents, execution gateways, queues, caches, or HFT-like infrastructure where freshness and p95 latency matter. Use when p95 latency or data freshness matters — realtime dashboards, market data, streaming agents, queues, or caches.
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