Research & Learning skills

法律研究备忘录·输出法律检索与风险分析的研究备忘录·触发：研究备忘录、法律检索、风险分析

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Generates structured U.S. art law summaries with Bluebook citations, doctrinal analysis, and stakeholder guidance. Use when researching art law precedents, advising on art transactions, preparing for art market litigation, or surveying a doctrine or statute across ownership, provenance, copyright, VARA, NAGPRA, authentication, NFTs, or AI-generated art.

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Produces structured aviation law summaries with Bluebook citations covering U.S. and international regulatory frameworks, treaties, and case law. Use when drafting an aviation law summary, FAA/DOT/TSA compliance overview, Montreal/Warsaw Convention liability analysis, air service agreement review, accident liability synopsis, or aviation regulatory research memo.

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Drafts the appellant's opening brief challenging a lower court decision in federal or state appellate courts. Covers issue selection, standard of review framing, record citation, argument structure, and procedural compliance under FRAP 28/32 or state equivalents. Use when preparing an appellant's opening brief, selecting appellate issues, structuring appellate arguments, or demonstrating reversible error on appeal.

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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.

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Generates a cross-referenced U.S. nonprofit annual filing package for state charity-bureau registration. Produces Full Compliance Package, Form-Field Mode, or Executive Briefing with source citations, disclosure cross-checks, and filing instructions. Trigger when the user drafts annual report submissions, renewal packets, penalty-of-perjury certifications, or charity bureau filings. Also trigger on Form RRF-1, CHAR500, BCO-10, nonprofit annual report, charity registration renewal, or fundraiser disclosure attachments.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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Saves learning notes from the current conversation. Classic mode makes concise notes, Mastery mode keeps detailed training artifacts.

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Interactive Swift/iOS tutor with two modes. Classic explains first, Mastery uses a predict-first Core Loop with gates.

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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.

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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.

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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.

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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.

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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.

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Use after evidence is in hand and before presenting one precise claim as established; calls the Jev MCP tool jev_check_research.

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