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Track exact provenance for every operation on ENCODE data — tool versions, reference files, scripts, parameters, and timestamps — to enable publication-ready methods writing. Use when the user processes ENCODE files, runs any bioinformatics tool, creates filtered/merged datasets, runs pipelines, performs liftover, uses R/Python/Bash for analysis, or needs to document their analysis chain for reproducibility and publication. Also use when the user says "write me methods" to auto-generate methods sections from the provenance log. This skill implements comprehensive provenance documentation: every tool, every version, every reference file, every parameter, every accession — no shortcuts. Use this skill for ANY processing step, ANY file transformation, ANY analysis operation on ENCODE data.

日本語の概要は準備中です。原文の説明を表示しています。

Weekdays, file work only, no browser at all. Reads every ledger, every queue file, and every run record since its cursor, turns the member's ticks into replied lines and closed cards, computes the first response and time to resolution clocks the Friday report is built from, folds the card inbox, re-renders the desk board, and writes the short morning brief the member opens first. It holds every outbound action unless you released the channel, and it never touches a credential.

日本語の概要は準備中です。原文の説明を表示しています。

Weekdays, heavy browser lane. Opens the member's own signed in profile, post, and notification surfaces for every platform in the channel plan, confirms that everything published since the last run is genuinely live, reads the counts attached to each post, captures every comment, mention, quote, reply, and inbound message into a queue the member answers by hand, and drafts a suggested reply for each one. Read only everywhere and totally read only on LinkedIn. It sends only where you released the channel, never replies, spends only where you released it, and never touches a credential.

日本語の概要は準備中です。原文の説明を表示しています。

Generate or redesign evidence-based README files for apps, libraries, CLIs, monorepos, and agent skills. This skill should be used for /readme-write, /readme, generate readme, write readme, improve readme, 帮我写 README, 美化 README, README を書いて, README 작성해 줘, напиши README, or their equivalents in any language. Confirm reader and visual preferences before generation; every README includes a source-backed flowchart, and every language edition keeps identical content apart from the language. Git-driven updates are routed to the companion readme-update skill.

Continuously monitor a GitHub PR for merge conflicts, CI failures, and CodeRabbit review comments, then automatically fix any issues found. Polls every 5 minutes and loops until every check is green. Use this skill whenever a PR has just been created or code has been pushed to a PR branch — it should be the default follow-up action after any PR creation or push. Also trigger on: "watch this PR", "guard this PR", "monitor CI", "keep fixing until green", "PRを監視して", "CIが通るまで 直して", /pr-guardian. When a PostToolUse hook reports that a push or PR creation just happened, proactively invoke this skill to start monitoring without waiting for the user to ask.

Harbor intake for external work — the captain only handles unresolved decisions. Sweeps incoming issues and PRs, verifies every claim before disposition, reuses every decision already made, and hands the maintainer a docket whose pending items each carry one question with options, recommendation, impact and evidence. Agent-autonomous for facts and for actions covered by standing authorization; signed for every new judgment. Never merges.

日本語の概要は準備中です。原文の説明を表示しています。

Build a TYPED citation/reference graph over an ingested corpus — not just embeddings. Flat similarity retrieval cannot tell you that document A *overrules* B, *distinguishes* C, or *relies_on* D. This skill extracts every inter-document reference, classifies the edge TYPE with LLM judgment, and writes first-class typed edges via `gbrain link`, so `gbrain graph-query --type` can walk the argument ("everything this brief relies on, minus anything overruled since"). Every cite-heavy corpus is the same shape: law, academic papers, patents, regulatory filings, a book's bibliography.

日本語の概要は準備中です。原文の説明を表示しています。

Ingest meeting transcripts from ANY meeting recorder into brain pages with attendee enrichment, entity propagation, and timeline merge. One unified pipeline: normalize the source into a standard transcript record, split multi-meeting recordings, resolve speakers by evidence, create the page, pass every surprising claim through the consistency check (transcript + brain + plausibility), enrich every entity, then run the verification checklist — substance AND sequence. A meeting is NOT fully ingested until the enrich skill has processed every entity AND the verification checklist passes, including the sequence verify (PASS or explicit user waive).

日本語の概要は準備中です。原文の説明を表示しています。

Daily vendor-onboarding intake for {{request_label}}. Validates the W-9, banking form, and signed contract for every new vendor request against a fixed checklist, records every vendor's status in {{vendor_register}}, drafts a follow-up for anything missing or invalid, and posts the batch to {{review_channel}}. Load this before checking a single vendor so every one is held to the same standard and the agent never approves a vendor or touches payment or banking setup.

日本語の概要は準備中です。原文の説明を表示しています。

Evaluates an ML GPU cluster for a cloud trial or acceptance test: environment dump, isolated newest PyTorch, matmul FLOPS (MAMF/MSMF) on every GPU while the others compute, intra-node all-reduce bandwidth and per-call latency of every collective, the same inter-node on every node you were given (omit those sections if there is only one node), fio on local disk and shared FS, dated markdown report. Use when the user asks to evaluate a cluster, kick the tires on trial nodes, run cluster acceptance, or measure GPU/network/storage. Canonical copy: https://github.com/stas00/ml-engineering/blob/master/skills/evaluate-cluster/SKILL.md

日本語の概要は準備中です。原文の説明を表示しています。

Draft a brief section in house style, consistent with the case theory — every fact cited, every case checked, every argument tied to the theory. Use when the user says "draft the [section]", "write the statement of facts", "argument section on [issue]", or needs a first draft of a brief section.

日本語の概要は準備中です。原文の説明を表示しています。

Governs how the OpenBot browser app reads and writes server data — every request goes through `client` in app/src/lib/client.ts, every read is a queryOptions factory in app/src/lib/<entity>/queries.ts, every write is a mutationOptions factory in app/src/lib/<entity>/mutations.ts, and components consume them through useQuery/useMutation. Use when adding or changing a screen that loads server data, calling a /api/... endpoint from the browser, adding a query key, writing a create/update/delete flow, deciding where a fetch belongs, or reviewing a diff that contains the word fetch under app/src. Don't use for server-side route handlers under server/ (that is not browser code), for form validation schemas (those live in lib/<entity>/form.ts), for page layout and Item rows, or for the AG-UI stream itself, which the runtime carries rather than the client.

日本語の概要は準備中です。原文の説明を表示しています。

Classical end-to-end empirical analysis workflow in the traditional Python econometric stack — pandas + numpy + scipy + statsmodels + linearmodels + pyfixest + rdrobust + econml + causalml + matplotlib/seaborn. **Defaults to economics empirical-paper style** (AER / QJE / AEJ) — every run produces a publication-ready output set with a multi-column regression table (M1→M6 progressive controls/FE) as the centerpiece, plus Table 1 (descriptives), mechanism / heterogeneity / robustness tables, and event-study + coefficient + trend figures. Covers the full 8-step pipeline an applied economist or quantitative social scientist runs on every paper — (1) data cleaning, (2) variable construction & transformation, (3) descriptive statistics & Table 1, (4) statistical diagnostic tests, (5) baseline empirical modeling, (6) robustness battery, (7) further analysis (mechanism, heterogeneity, mediation, moderation), (8) publication-ready tables & figures. **Also covers two parallel domain modes that share the same 8-step scaffolding** — **Mode A — Epidemiology / public health** (target-trial emulation via `zepid` / hand-rolled `pandas`, IPTW + g-formula + TMLE doubly-robust triplet via `zepid` / `econml` / `lifelines`, Mendelian randomization via `pymr` / `mrtool` (or `rpy2` → `MendelianRandomization`/`TwoSampleMR`), KM / AFT / Cox survival via `lifelines`, E-value sensitivity, principal stratification — STROBE / TRIPOD reporting), and **Mode B — ML causal inference** (DML via `econml.dml` / `doubleml`, S/T/X/R/DR meta-learners via `econml.metalearners` / `causalml`, causal forest via `econml.grf` / `causalml`, Dragonnet / TARNet / CEVAE neural causal via `causalml`, BCF via `pymc-bart` / `bcf-py`, matrix completion, CATE distribution + policy tree via `econml.policy` / `policytree-py`, off-policy evaluation, conformal causal via `mapie`, fairness audit via `fairlearn`, DAG learning via `causal-learn` / `cdt` / LLM-assisted). Prescribes which library to reach for at each step, shows the canonical code, and links to deeper `references/` files for variant-specific patterns. Use when the user asks for a **complete empirical analysis** in Python, wants to replicate an applied-economics paper from scratch, needs a reproducible workflow that is NOT opinionated on any single vertical package (contrast with StatsPAI), wants explicit control over every estimator and diagnostic, or asks "how do I write a full empirical pipeline in Python?". Also triggers when the user names a specific classical step in isolation — "winsorize at 1/99%", "run Breusch-Pagan", "build a Table 1 balance table", "do a placebo test", "event study plot", "mediation analysis" — and wants it wired into the broader pipeline. Mode A triggers on "target trial emulation", "IPTW", "TMLE", "Mendelian randomization", "STROBE", "公共健康", "流行病学". Mode B triggers on "DML", "double machine learning", "causal forest", "meta-learner", "Dragonnet", "BCF", "policy tree", "conformal causal", "fairness audit", "因果机器学习".

日本語の概要は準備中です。原文の説明を表示しています。

Use when the user asks to "turn this idea into posts for every platform", "write the X thread / LinkedIn post / 小红书 note", or "spec the carousel slides"; turns one idea into N platform-native ready-to-paste packages — post/thread, caption, Threads text, 小红书 note, link post with first-comment placement — each adapted per the dated platform norm card (never verbatim cross-posting), every product claim held to approved claims-ledger wording or flagged [needs source], every variant tagged {formula | hook family | CTA type | signal optimized}, plus a carousel slide-spec mode (intro/content/outro slide roles, per-element char budgets, 小红书 3:4 / IG 4:5 / LinkedIn document PDF artboards, text-safe-zone + alt-text checklist). Not for creator deliverable briefs — use brief-generator; not for repurposing existing assets with paid boost — use content-amplifier. 社媒文案/一稿多发改写/小红书笔记/轮播图脚本

日本語の概要は準備中です。原文の説明を表示しています。

Use when the user asks to "plan how our narrative lands on every surface", "write per-surface message-match specs", or "brief each creative builder from the canon"; maps the narrative-registry canon onto every flagship surface (homepage, pricing, store listing, sales deck, social bio, docs, email) as a per-surface message-match spec — what claim, pillar, proof, and objection reframe each surface must carry — and cuts a handoff brief to each discipline's creative builder (content-writer, ad-creative-builder, email-creative-builder, social-creative-builder, message-house-builder), so the same story lands consistently everywhere. Not for writing the finished surface copy — use each discipline creative builder; not for the tier-scoped launch asset manifest — use launch-asset-packager; not for scoring cross-surface consistency — use narrative-quality-auditor; not for claim adjudication — use offer-claims-registry. 叙事落地/逐面消息一致/创意交接简报/画布传导

日本語の概要は準備中です。原文の説明を表示しています。

MANDATORY for every coding agent (Claude Code, Codex, or any other) on every change-set — every applicable source file the agent creates or updates MUST start with the project's copyright/authorship header (file overview + exact author line). Use automatically whenever writing a new file or editing an existing one; do not wait to be asked. Covers JS/TS/TSX/CJS/MJS, Python, shell, and CSS. Includes the audit script to verify repo-wide compliance.

日本語の概要は準備中です。原文の説明を表示しています。

Reviews and migrates deprecated wildcard (*) values in Power Pages Web API fields site settings to least-privilege explicit Dataverse columns. Use whenever a user mentions Web API wildcard or select-all remediation, fields settings containing *, data-exposure review, wildcard deprecation readiness, or Web API failures after wildcard retirement. Applies to both traditional sites using HTML, CSS, JavaScript, Liquid, and downloaded YAML, and SPA sites using React, Vue, Angular, Astro, or TypeScript. The agent must inspect every source Web API call and consumer, report exact fixes for every wildcard, report every already-explicit configuration, apply approved edits, and verify no wildcard remains.

日本語の概要は準備中です。原文の説明を表示しています。

Build a near-complete company list for any ICP by learning how your real customers label themselves, pulling every industry and keyword they use, judging every single row with a cheap AI model (Jev or gpt-5-nano), and running a Claygent lookalike loop until it runs dry. The method from the SCULPT 2026 talk "Build the perfect company list". Use when a database filter feels too narrow or too noisy, when someone says "find every company like our customers", "our TAM looks too small", or "why is this list full of junk".

日本語の概要は準備中です。原文の説明を表示しています。

Code review for the current change, before it is pushed. One command runs the security, secret, dependency and lint scanners that fit the changed files, a separate reviewer that checks every scanner finding and is given every changed line, and prints a short receipt with the path of the full report. Use before every git push, when asked for a code review, a security scan, or to review changes, a diff or a pull request, and when a push was blocked or warned by OpenQodex.

日本語の概要は準備中です。原文の説明を表示しています。

Runs once by hand on the first day and once a month after that, light browser lane. On the first run it researches the business from its own published surfaces before asking anything, writes the strategy folder, creates the ledgers, seeds the opening cards, builds the single page dashboard, reconciles the schedule table, and registers the eight recurring jobs. On every monthly run it re-reads a month of the kit's own evidence, applies what changed, adds a tab for a channel that gained volume, rebuilds, and reconciles every registered job against the table. It holds every outbound action unless you released the channel, and it never enters a credential.

日本語の概要は準備中です。原文の説明を表示しています。

Weekdays, heavy browser lane. Reads every support channel named in strategy/channels.md, meaning the mailbox, the helpdesk queue, the review and rating listings, the marketplace pages, and the forums where the member's products are discussed, and captures every new or changed item as one dated, sourced, severity graded line on the ticket ledger. It replies to nothing, reacts to nothing, marks nothing read or resolved, and takes no action of any kind on LinkedIn. It holds every outbound action unless you released the channel, and it never touches a credential.

日本語の概要は準備中です。原文の説明を表示しています。

Weekdays, heavy browser lane. Reads the member's own saved searches and the named sources for one buyer segment, captures candidate accounts together with the contactable people attached to them, scores every row against the named tests in the qualification file, and writes each one with the evidence that qualified it. Read only on every people surface, LinkedIn included. It holds every outbound action unless you released the channel, and it never touches a credential.

日本語の概要は準備中です。原文の説明を表示しています。

Weekly, browser heavy, read only on every screen. Fixes a scoring window that neither double counts nor loses an hour, reads the member's search performance console and analytics property, verifies each range actually applied before reading a figure, reconciles every number against what this Employee published, writes the week's scoreboard and the short rolling state file every other routine reads, and files what it found as cards rather than doing the work itself. It types nothing on any screen, changes no setting, publishes nothing, and never touches a credential.

日本語の概要は準備中です。原文の説明を表示しています。

Monthly, browser conditional. On its first run it discovers the member's properties from the sites they name, writes the three strategy files, creates every ledger and folder the kit reads, files the opening cards, and registers the eight scheduled jobs. Every month after that it re-reads the evidence rather than its own previous conclusions, rebuilds the topic map and the internal link map, corrects any property fact it can prove wrong, and records every change with the path of the evidence that forced it. It holds publishing and spending unless you released the channel, and it never touches a credential.

日本語の概要は準備中です。原文の説明を表示しています。