データ・AIのスキル

要求分析ツリーのの元となるTSVファイルをすべて作成（Phase 4）

匠Methodの全成果物（コアモデル、価値マネジメント評価、企画立案）を一括作成する

価値分析モデルの元となるTSVファイルをすべて作成（Phase 3）

価値デザインモデルの元となるTSVファイルをすべて作成（Phase 2）

Use when designing, reviewing, modifying, migrating, optimizing, securing, operating, troubleshooting, or decommissioning data architectures across data model exploration, SQL, ISO SQL conformance, NoSQL, vector, graph, document, time-series, NewSQL, search, in-memory, SQLite, CDC/Debezium, DBMS storage/query/recovery internals, database incident prevention, hardware acceleration, cache, big-data, lakehouse, and warehouse systems.

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Use when the user uploads a screenshot of a hero section or landing page (or a URL/description of one) and wants a copy-paste-ready developer prompt to recreate it exactly in a tool like Lovable, Bolt, Replit, or Cursor. Produces prompts with exact hex colors, Tailwind classes, animation timing tuples, and component structure — not vague descriptions.

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Compare your data to a partner's data in the marketplace. Given a dataset you already own with person/edge data, this skill walks you through picking a partner data source to match against, choosing which identifier types to match on, optionally selecting which enrichment attributes to attach, and then submits the report — returning overlap, match counts, and demographic coverage. Use when: "how does my data compare to your marketplace", "compare my data to [partner]", "how much overlap do I have with [supplier]", "run a match report", "match my customers against 3P data", "see what enrichment is available for my dataset", or any open-ended question about marketplace overlap. (narrative-identity)

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Register a Snowflake table or view as a Narrative dataset from SQL, with no Configuration screen and no browser. Not for installing or configuring the native app, and not for querying a dataset that already exists. Use when: "register this table as a dataset", "register a Snowflake dataset from SQL", "onboard my_table to Narrative", "script dataset registration", "add this view to my data plane". (narrative-common)

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Audit a dataset or access rule before it joins an identity-graph build (access rules behave like datasets in NQL here). Enumerates failure modes (hub identifiers, high-degree nodes, suspicious values, over-connected identifiers), tests hypotheses in parallel, quantifies damage by rows / edges / entities, and proposes minimal filters ranked by severity. When issues are found, returns a validated `CREATE MATERIALIZED VIEW` NQL the caller can run to produce a graph-ready clean source; if the data passes, says so and recommends it unchanged. Plans and authors the clean-view NQL; does not execute it. Use when: "audit this dataset before the graph build", "find bad edges in <source>", "check identity data quality", "recommend filters for the graph build", "quantify damage from <identifier_type>", "pre-graph DQ". (narrative-identity)

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Produce a coverage & quality profile of a Narrative dataset (or access rule): row count, per-column null/fill rate, cardinality, ranges, top-values, inferred column shape, and quality flags. Reads bundled stats + sample first, recovers missing/stale stats by configuring and recalculating them, and escalates to a cheap `/write-nql` query only for a measure no stat can provide. Descriptive, not prescriptive. Use when: "profile dataset N", "what does dataset N look like", "coverage and quality of <dataset>", "what id types does N emit", "null rates / cardinality for <dataset>", "is this dataset's stats fresh". (narrative-common)

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Generate, evaluate, and improve Rosetta Stone attribute mappings for a Narrative dataset. Use when: "map this dataset to Rosetta Stone", "suggest normalized attributes for dataset N", "evaluate the mappings on dataset N", "why is this mapping low confidence", "fix this expression", "improve this NQL mapping expression". (narrative-common)

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Interactively build a Narrative identity graph workflow from one or more first-party datasets and (optionally) third-party data sources. Confirms each input dataset is mapped to the Rosetta Stone graph edge attribute (mapping it via /generate-rosetta-stone-mappings if not), then composes and submits a workflow that unions every edge source and labels connected components. Use when: "build an identity graph", "generate an identity graph", "create an identity graph", "stitch these datasets into a graph", "make a graph workflow", "label connected components on these datasets", "I want a person graph / household graph / device graph". (narrative-identity)

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。

Create a look-alike audience from a seed audience and a candidate population dataset. Classifies Rosetta Stone attributes, generates the same materialized-view scoring pipeline Lookalike Studio emits (Naive-Bayes categorical weights + Gaussian continuous similarity), gates on approval, submits via `narrative_workflows_create`, and monitors the build to completion. Use when: "create a lookalike audience", "find more users like this segment", "expand my seed audience to 500k similar users", "score the population against my customers", "build a look-alike of dataset X". (narrative-audience)

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Apply a set of Rosetta Stone attribute mappings to a Narrative dataset by wrapping them in a one-shot workflow that calls the `CreateRosettaStoneMappingsIfNotExist` task. Consumes the structured output of `/generate-rosetta-stone-mappings`, normalizes the generator's snake_case to the workflow task's camelCase, re-validates every expression against the dataset's current schema, gates on user approval, submits via `narrative_workflows_create`, polls the triggered run, and reports per-mapping created / conflict / failed. Use when: "apply these mappings to dataset N", "create the Rosetta Stone mappings I just generated", "push the mappings I saved earlier to <dataset>", "productionize this mapping list", "submit the suggested_mappings array". (narrative-common)

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。

Write, validate, and (optionally) execute an NQL query against a Narrative dataset. Drafts the query from the user's question, runs `narrative_nql_validate` until it compiles, explains the query in plain English, and only runs it on explicit approval (or when invoked with `--run`). Use when: "write an NQL query for X", "query this dataset", "validate this NQL", "run NQL against dataset <id>", "how many rows match Y", "show me the top N records from <dataset>". (narrative-common)

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。

Verify a tested web agent's real behavior from its recorded Pi or Codex session and selected screenshots before scoring. The judge prompt provides absolute artifact paths; use targeted queries to compare actual outputs, errors, and the final answer with the recorded behavior, then return only the verdict JSON. Only for ego-bench judging—never to drive a browser or rerun the agent.

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 self-contained HTML/CSS benchmark and comparison charts in an ego-inspired Browser-Native Editorial style. Use for blue-white product metrics, model benchmarks, A/B comparisons, efficiency reports, difficulty breakdowns, and compact technical data panels.

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。

Compare standardized metrics across multiple ego-benchmark-harness runs by recalculating them from tasks.jsonl and session JSONL, never from HTML reports. Covers overall aggregates (success rate/average score), Odysseys per-rubric pass rates, duration distributions (avg/p50/p90/max), turns/tools/cost, error and exception counts, per-task success matrices, helper distributions, and the share of DOM operations. Use whenever users ask to compare data or metrics across runs, calculate aggregate/Odysseys/duration/turn/error/helper metrics, inspect differences among runs, or compare multiple rwb/real-world-bench runs. Use pi-session-analyzer instead for deep forensic analysis of why an individual task was slow or failed.

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。

Reconstructed reference notes on US FAA approval guidance for airborne software and airborne electronic hardware, built from four public-domain sources (FAA Order 8110.49A, AC 20-115D, AC 20-152A, AC 00-69; documents dated 2017-07-21 to 2022-10-07, pinned 2026-10-12). Use for how FAA certification staff plan and conduct software reviews (certification liaison, desk versus on-site review, review planning agreements), how the level of FAA involvement (LOI) is picked and scored with the Appendix A worksheets, how software conformity inspection works (software part and installation conformity, ASE and ASI tasks, Form 8120-10), what AC 20-115D recognizes and how DO-178B processes and tool qualification transition to DO-178C/DO-330 (supplements, FLS, UMS, legacy software changes), and what AC 20-152A adds for airborne electronic hardware (simple versus complex custom devices, robustness and HDL code coverage, tool assessment, previously developed hardware, COTS IP, COTS devices, circuit board assemblies), and what AC 00-69 adds as best practices for change impact analysis, data and control coupling, and design-level error handling (best practices, not guidance and not a means of compliance). SCOPE LIMITS: FAA guidance only, and guidance is not regulation (ACs are acceptable means, not the only means); no RTCA DO-178C or DO-254, EUROCAE ED-12C or ED-80, supplement, or SAE ARP text, which are copyrighted and named only (see the avionics-signpost pack); no AC 00-72 (named, not packed); no DO-178C software-level objective tables; no EASA AMC 20-115D or AMC 20-152A content (use easa-rules); reflects the pinned source dates, not later FAA actions; synthesized reference notes, not legal advice and not a substitute for the source documents. LICENCE: Public Domain (US Government work, 17 U.S.C. 105).

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を書く・直すときに、どこに何をどのlevelでlogへ残すかを定める。新しいscript、batch、bot、API処理、backtest、data処理を実装するとき、または不具合調査でlogが足りないと分かったときに使う。

Run a second, harder escalation pass using the original glazer prompt words verbatim. Use when prompts say `/tw-glazer`, when a first escalation still feels incremental, or when you want the exact original rhetoric preserved while forcing a sharper replacement rather than more polish.

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Turn 'should never happen' into 'cannot happen' by defining owned inductive invariants and enforcing them at parse/construct/API/DB/lock/txn boundaries with a verification signal. Use when prompts mention invariants, impossible states, validation sprawl, cache/index drift, idempotency/versioning, retries/duplicates/out-of-order events, race/linearization bugs, loop correctness, or hardening another workflow (for example /tw-fix) with invariant checks first.

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。

Run one explicit escalation pass using the original glaze prompt words verbatim. Use when prompts say `/tw-glaze`, when the first answer feels merely adequate, or when you want the exact original rhetoric preserved while pushing for a materially stronger direction.

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Post-launch review of a shipped PRD against the brief's success metrics. Did we move the metric? What did the data actually show? Which assumptions held, which broke? Which accepted-gap decisions resolved (confirmed vs. invalidated)? What would we do differently? Triggers on "outcome review", "did it work", "post-launch review", "did we move the metric", "retro the feature", "was it worth it". Produces a Confluence outcome-review page and updates the brief + linked decisions.

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。