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Found 24 Skills
Use when the user asks to "design an early access program", "set up a waitlist and beta stages", or "define beta graduation criteria"; produces a waitlist→concept→alpha→beta→GA stage ladder with per-stage purpose and opt-in semantics, quantified graduation criteria per stage (labeled Estimated), a cohort-gating and invite-throttling plan, tester recruitment with launch-day social-proof prep, a feedback-loop spec where every status change notifies its subscribers, and a referral-loop mechanism spec (invite codes, anti-abuse). Not for waitlist acquisition strategy or the capture-flow spec — use list-growth-designer; not for the canonical stage record — use launch-registry. waitlist/内测阶梯/抢先体验/毕业标准/反馈闭环
Initialize, inspect, and maintain a hierarchical memory system for an ML research project across paper, code, worktrees, slides, reviewer simulation, rebuttal, experiments, claims, evidence, risks, and actions. Use this skill whenever the user wants cross-session project memory, project bootstrapping context, feedback-loop tracking, claim-evidence-risk-action alignment, worktree memory, or consistency between code results, paper writing, slides, reviews, and rebuttal.
Write a high-quality prompt for any LLM or AI assistant — Claude, Claude Code, ChatGPT, Gemini, Cursor, Windsurf, Copilot, or any coding / chat agent. Use this skill whenever the user asks to write, improve, refine, shorten, or rewrite a prompt; asks "how should I phrase this for [model]" or "what's a good prompt for [task]"; describes a task they want an AI to do but hasn't yet formulated it as a prompt; or pastes an existing prompt and asks for revision. Based on Boris's (Anthropic, Claude Code creator) prompt methodology — short and accurate prompts, plan-before-code, feedback loops, persistent context in files. The universal principles (short, plan-first, feedback-loop, no-padding) apply to any LLM; the Claude-Code-specific anchors (CLAUDE.md, @file, slash commands) only apply when the target is Claude Code. If the user's intent is unclear (target model, deliverable, scope, or whether the AI has a way to self-verify is missing), ask 1–3 targeted clarifying questions via AskUserQuestion before writing the prompt.
Diagnosis loop for hard bugs and performance regressions. Use when the user says "diagnose"/"debug this", or reports something broken/throwing/failing/slow.
Iterative design-to-code feedback loop. Critique → adjust → ship cycle for tightening visual fidelity between brief and built UI.
Guide competency framework development and operation. Use when building training that produces capability, when existing training doesn't produce competence, when structuring knowledge for multiple audiences, or when setting up feedback loops to surface gaps.
Self-evolving context protocol that captures insights, prevents repeated mistakes, and evolves project documentation through structured feedback loops.
Patterns for building AI agents that learn from their own execution, detect failure modes, and improve autonomously. Covers feedback loops, performance regression detection, memory curation, skill extraction, and meta-learning architectures. Use when building agents that need to get better over time, managing auto-memory, or designing self-correcting systems.
Execute written implementation plans: first read and critically review the plan, then implement in small batches (default 3 tasks), produce verification evidence per batch and pause for feedback; must stop immediately and ask for help when blocked/tests fail/plan unclear. Trigger words: execute plan, implement plan, batch execution, follow the plan.
Synthesize user feedback from multiple channels and identify patterns to inform product decisions. Use when analyzing feedback, prioritizing feature requests, conducting NPS surveys, or understanding user sentiment. Covers feedback collection, categorization, prioritization frameworks, and closing the feedback loop.
Use when a codebase, product, workflow, runtime, or organization needs purpose-first whole-machine stewardship: understand what the whole system is trying to produce, then improve the machinery, tooling, feedback loops, operability, and developer flow that let it produce that output.
Use when the workflow needs to self-correct, improve over time, or establish feedback loops and evaluation cycles.