调研、论文、教育、考证学习（4417 个）
Drafts and analyzes U.S. appellate amicus curiae briefs for non-parties with Rule 29/Rule 37 compliance, unique perspective development, and Bluebook-ready citations. Use when asked to draft or review an amicus brief, friend-of-the-court brief, non-party brief, or anything involving FRAP 29, Supreme Court Rule 37, or appellate amicus practice.
中文简介正在准备中。当前显示原文说明。
Produces structured U.S. adoption-law summaries for litigation and client-facing use. Use when asked to summarize adoption law, draft pre-filing research briefs, create jurisdiction-specific issue memoranda, or prepare client education documents. Triggers: adoption summary, domestic adoption, intercountry adoption, Hague adoption, foster care adoption, TPR in adoption, stepparent/relative adoption, post-adoption rights.
中文简介正在准备中。当前显示原文说明。
Audit and curate the `learnings` memory namespace — the one namespace nothing re-derives, so the only one that rots. Runs moflo's mechanical audit to nominate stale, unused, and near-duplicate entries, then decides entry by entry whether to keep, retire, compress, or merge, and propagates the result to the shared artifact. Use when memory search returns stale or duplicated hits, after retiring a big chunk of work whose supporting entries went stale with it, or as a periodic pass once the namespace passes a few hundred entries.
中文简介正在准备中。当前显示原文说明。
Adaptive learning for moflo agents via ReasoningBank: trajectory storage, verdict judgment, memory distillation, consolidation, and MMR retrieval. Use when building agents that should improve from experience across runs.
中文简介正在准备中。当前显示原文说明。
Verify, customize, or opt out of moflo's AUTOMATIC durable-learning sharing across git worktrees / Conductor workspaces on one machine. As of the worktree-auto-sharing change this is on by default — learnings converge across a repo's worktrees with no setup. Use when the user asks "is memory shared across my worktrees?", "where are my worktree learnings stored?", "turn off worktree memory sharing", "share learnings across my separate clones", or "point my worktrees at a custom store". For a team or cross-machine (git-tracked) sharing, use /memory-team instead.
中文简介正在准备中。当前显示原文说明。
Guided setup for sharing moflo's durable learnings through a git-tracked artifact — for a whole TEAM on one repo, OR for one person across several MACHINES (a team of one). Use when the user says "share learnings with my team", "commit our moflo memory", "sync memory across my laptop and desktop", "set up memory.team_artifact", "auto-export learnings on every PR", or "learnings.jsonl keeps conflicting". Installs a pre-commit hook so the artifact rides along in every commit. An existing learnings.jsonl moves to the conflict-free layout automatically. For many worktrees on ONE machine, use /memory-worktree.
中文简介正在准备中。当前显示原文说明。
Persistent memory patterns for moflo agents — session memory, long-term knowledge, pattern learning, and cross-session context via moflo's node:sqlite + HNSW vector store. Use when building stateful agents or assistants that need to remember across runs.
中文简介正在准备中。当前显示原文说明。
Deliberate session retrospective — look back over what you just did, distill the durable, reusable lessons (not session trivia), and write them to the learnings memory namespace, deduped against what is already stored. Use at the END of a meaningful chunk of work to capture high-signal lessons worth keeping long-term. The curated counterpart to moflo's passive session-continuity capture.
中文简介正在准备中。当前显示原文说明。
Structured multi-hop web research with explicit confidence gating — plan the inquiry, search (WebSearch/WebFetch), score your own confidence, and keep digging until the answer is well-supported or a hop cap is hit, then emit a cited synthesis. Learns across sessions by storing each research case to memory and reusing prior strategies. Use when a question needs more than one search — comparisons, current-best-practice questions, anything where a single lookup leaves you unsure.
中文简介正在准备中。当前显示原文说明。
Use when creating publication-grade scientific figure images or prompts, including graphical abstracts, mechanism diagrams, anatomy diagrams, surgical step figures, experimental workflows, pathology mechanisms, device principles, and structured paper illustrations. If the user asks to generate the image, use ChatGPT image generation.
中文简介正在准备中。当前显示原文说明。
Refine an existing implementation plan with a second iteration. Re-analyzes the codebase for gaps, missing tasks, and wrong dependencies. Use after /unikit-plan or to improve a /unikit-fix plan, or when the user says "improve the plan", "what's wrong with the plan", or "update the plan from research". Optional +check flag validates refinements via a fresh-context subagent.
中文简介正在准备中。当前显示原文说明。
Interactive Swift/iOS tutor with two modes. Classic explains first, Mastery uses a predict-first Core Loop with gates.
中文简介正在准备中。当前显示原文说明。
Saves learning notes from the current conversation. Classic mode makes concise notes, Mastery mode keeps detailed training artifacts.
中文简介正在准备中。当前显示原文说明。
Run a self-retrospective on a Skmtc-related session — generator authoring, CLI configuration, debugging, or any sustained interaction involving `@skmtc/core`, `@skmtc/cli`, or `@skmtc/gen-*` packages. Captures three distinct outputs: (1) friction entries (mistakes, surprises, overridden defaults, multi-cycle struggles), (2) a "knowledge acquired" block recording facts the agent learned during the session that weren't in its training — the primary signal for doc gaps — and (3) a closing priority list ranking the top 2-3 findings for docs/skills triage. Wins are logged only when they identify a pattern worth codifying that isn't already in a skill. Use this skill when the user asks to "retro", "skmtc retro", "log friction", "reflect on this session", "what did we learn", "run a retrospective", or after substantive Skmtc work (generator authoring, multi-step debugging, non-trivial CLI configuration) where capturing observations would help improve skills, docs, or Skmtc itself. Distinct from the `skmtc-cli` and `skmtc-generator` skills — those guide *doing* the work; this skill captures observations *about* the work. Do not invoke this skill during ordinary work; it is end-of- session (or mid-session-checkpoint) only.
中文简介正在准备中。当前显示原文说明。
Research complex questions and synthesize relevant evidence into a clear, cited answer. Useful for competitive and market analysis, company research, prospect discovery, enrichment, historical comparisons, and evaluating conflicting claims. Adapt the depth and format to the question; use when a focused search or single-page read is insufficient.
中文简介正在准备中。当前显示原文说明。
Self-learning pre/post-task hooks and model/task routing from the Ruflo CLI, on a ruOS desktop. Use to add coordination, formatting, and learning around Claude Code operations, or to route work by complexity/cost.
中文简介正在准备中。当前显示原文说明。
Use when starting work in a repository under repositories/ that may lack CI configuration. Detects missing CI workflows (GitHub Actions, GitLab CI, CircleCI) and alerts the user to add one. Skips repos marked as research-only.
中文简介正在准备中。当前显示原文说明。
Retrospective learning from a completed experience. Takes a project, incident, decision that played out, or time period; gathers ground truth (observations) separately from recollections; spawns reflectors applying different lenses (what-worked-vs-got-lucky, what-didn't, what-surprised, system-rewards-vs-intent, decisions-that-aged, what-to-tell-past-self, patterns-that-recur) in isolation; synthesizes into updated mental models as first-class output. Produces feedback only — no code, no tickets, no artifacts.
中文简介正在准备中。当前显示原文说明。
Use after evidence is in hand and before presenting one precise claim as established; calls the Jev MCP tool jev_check_research.
中文简介正在准备中。当前显示原文说明。
Use when you hit a real decision boundary and need Jev MCP to pick among jev_route_model, jev_route_task, jev_guard_tool_call, jev_check_research, jev_review_completion, or jev_decide.
中文简介正在准备中。当前显示原文说明。
Capture a solved, verified problem as a durable learning in `docs/solutions/` so the next agent or engineer does not repeat the mistake or redo the investigation, and keep the `CONCEPTS.md` glossary in step. Use after a fix, debugging session, or design decision whose reasoning is not readable from the final code, tests, or existing docs — "document this", "write up what we learned", "capture this learning", "add it to solutions", "that worked, save it for next time". Writes nothing for routine fixes the code already explains. For auditing or cleaning up existing learnings, use `learn-with-docs-refresh`.
中文简介正在准备中。当前显示原文说明。
Audit and maintain the learnings in `docs/solutions/` against the current codebase — fix drifted paths and links, merge overlapping docs, replace guidance the code no longer supports, delete obsolete docs, reconcile the `CONCEPTS.md` glossary, then report and commit. Also bootstraps `CONCEPTS.md` on request and, when asked, culls docs whose reasoning the code already explains. Use for "refresh the learnings", "audit docs/solutions", "are our solution docs still accurate", "clean up / prune the learnings", "create a CONCEPTS.md", or after a refactor, rename, migration, or dependency upgrade that likely invalidated documented solutions. Not for capturing a new learning — use `learn-with-docs`.
中文简介正在准备中。当前显示原文说明。
Neural search via Exa MCP for web, code, and company research. Use when the user needs web search, code examples, company intel, people lookup, or AI-powered deep research with Exa's neural search engine.
中文简介正在准备中。当前显示原文说明。
Multi-source deep research using firecrawl and exa MCPs. Searches the web, synthesizes findings, and delivers cited reports with source attribution. Use when the user wants thorough research on any topic with evidence and citations.
中文简介正在准备中。当前显示原文说明。