「proven」の検索結果

Claude Code agent generation system that creates custom agents and sub-agents with enhanced YAML frontmatter, tool access patterns, and MCP integration support following proven production patterns

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Search the brand's stored knowledge and memory, ranked with provenance. "what are our brand voice guidelines"

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Embed C2PA provenance in AI-generated images, video or PDF by script. "add content credentials to this asset"

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Run fable-mode execution discipline on Claude Fable 5.1 — the top of the escalation ladder and the strongest staged run available. Routes the task to the @fable-orchestrator agent with its model overridden to Fable (`model: "fable"`), which stages the work, delegates artifact production to @fable-worker-sonnet / @fable-worker-haiku, and cold-checks deliverables with @fable-verifier. Trigger when the user explicitly asks for thorough/systematic/"deep work" handling on the strongest model ("fable on fable", "stage this on fable", "deep work mode, fable", "run this on 5.1"), or when a fable-opus run reports the task exceeds Opus's ceiling. Benchmarked 2026-09-02: zero correctness delta on Fable 5.1 at ~1.1× tokens, ~2× wall — a provenance runner, not an accuracy runner. Do NOT use for ordinary single-pass tasks — prefer fable-opus, fable-sonnet, or fable-haiku when the task doesn't need peak reasoning. Fable is plan-included; bulk work still routes down-tier.

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Screen a client, counterparty, payer or corporate against the sanctions lists published by the designating authorities themselves — the UK Sanctions List (FCDO), OFSI, the UN Security Council, the EU consolidated list and OFAC's SDN and non-SDN lists. Use when onboarding, before taking a payment, before completing a transaction, or whenever a name must be checked against sanctions. Produces candidates and provenance, never a clearance: the record names every list searched with its publication date and count, states what was NOT searched, and ends at a blank decision block. Fails closed — a cache that is missing, damaged, truncated or stale is reported NOT SEARCHED and the process exits non-zero, so nothing can read an incomplete run as clean. Matches across transliteration, diacritics, homoglyphs and initials, because nobody spells a name the way the publisher does. Standard library only; screening opens no network connection.

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When a solution seems impossible and the path forward is blocked. Refuses to take no for an answer: classifies what kind of "no" you hit, then runs lateral-thinking techniques (assumption autopsy, inversion, first principles, constraint toggling, analogical transfer, and more) until the wall cracks or is proven load-bearing. Generates at least 10 angles before evaluating any. AGENTS: use this on yourself when an API doesn't support what you need, an approach has failed twice, or a "that's not possible" sentence is forming — run the fast path BEFORE reporting a dead end. Archives walls to ~/.config/makerskills/unstuck/archive/. Sits upstream of decide. Triggers on "/unstuck," "I'm stuck," "this seems impossible," "we hit a wall," "there's no way to," "they said no," "dead end," "out of options," "I've tried everything," "work around this."

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When the user needs marketing ideas, inspiration, or strategies for their SaaS or software product. Also use when the user asks for 'marketing ideas,' 'growth ideas,' 'how to market,' 'marketing strategies,' 'marketing tactics,' 'ways to promote,' or 'ideas to grow.' This skill provides 139 proven marketing approaches organized by category.

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Expert knowledge for Content Safety in Foundry Control Plane development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when using Content Safety APIs, Docker containers, text blocklists, media provenance, or groundedness checks, and other Content Safety in Foundry Control Plane related development tasks. Not for Azure Content Understanding in Foundry Tools (use azure-content-understanding), Azure Security (use azure-security), Azure Sentinel (use azure-sentinel), Azure Defender For Cloud (use azure-defender-for-cloud).

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Applies Eric Ries's Lean Startup methodology for building products under extreme uncertainty. Use when iterating toward product/market fit, designing MVPs, deciding whether to pivot or persevere, setting up actionable metrics, or accelerating the Build-Measure-Learn loop. Triggers include 'how do we test this idea fast', 'what should our MVP look like', 'our metrics look good but we're not growing', 'should we pivot', 'we're building features no one uses', 'how do we measure validated learning', 'vanity metrics vs real metrics', 'how to do innovation accounting'. NOT for companies with proven product/market fit scaling a known playbook (use Crossing the Chasm), not for determining Market Type (use Four Steps), not for sales methodology (use SPIN Selling), not for pricing strategy (use Monetizing Innovation).

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Write viral Twitter/X threads and Reddit posts with proven structures, hooks, and engagement tactics. Includes templates for story threads, listicle threads, contrarian takes, tutorials, and case studies. Can post directly to Reddit. Trigger phrases: "write a thread", "Twitter thread", "X thread", "viral thread", "thread writer", "tweetstorm", "thread template", "thread about", "turn this into a thread", "reddit post", "write a reddit post".

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139 proven marketing ideas organized by category with implementation guidance. Use when asked for marketing ideas, growth strategies, campaign brainstorms, or "what should I do to grow." Trigger phrases: "marketing ideas", "growth ideas", "how to grow", "marketing strategies", "campaign ideas", "promote my product", "marketing brainstorm", "growth tactics", "what should I try", "marketing playbook".

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Publish a generated harness to npm — runs the smoke test, signs the witness manifest, and dispatches `npm publish --provenance` from your tagged release.

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Use when the user wants a VALID experiment instead of a guess — hypothesis, one variable, sample size and runtime math, statistical significance, primary versus secondary metrics, multi-arm designs, and a results template, across Optimizely, VWO, and native Meta and Google tests. Trigger on 'A/B test', 'split test', 'how long should I run the test', 'is this result significant', 'test two versions', 'which creative is actually better'. Also use when a winner was declared after two days on tiny numbers. Not for — scaling the proven winner, see `55-scaling-ads-global`; analyzing data already collected, see `13-data-analysis-global`; auditing the account, see `21-ads-audit-global`.

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Use for 小北在读研 / XiaoBei academic paper-to-PPT workflows: generate thesis-defense or project-report decks from uploaded papers, literature, reports, technical documents, and optional PPTX visual templates. Produces a source-grounded outline, an imagegen visual scheme, one complete bitmap per slide, an imagegen provenance manifest, and an image-only PPTX. Do not use when the user needs editable slide text or native editable charts.

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Use for software supply-chain security assessment covering SBOM, SCA, CI/CD pipelines, container images, build integrity, dependency provenance, and vulnerability reachability.

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Captures redacted, provenance-aware cybersecurity observations with shared status fields, independent ground truth, and honest limitations. Use when recording exercise evidence, findings, validation results, or security-review handoffs.

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Brand-consistent asset sets from DESIGN.md — hero, section illustrations, icon sets, OG image — generated through whichever channel the user actually has (grok build image_gen, Codex $imagegen, Gemini, xAI/Recraft/OpenAI keys) as recorded by omd:setup, with a provenance and cost ledger. No channel → prompt pack + manual queue, never stock. Trigger: '이미지 세트 만들어', '에셋 생성', 'generate assets', 'hero image', '아이콘 세트', 'OG 이미지'.

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。

Brand-consistent asset sets from DESIGN.md — hero, section illustrations, icon sets, OG image — generated through whichever channel the user actually has (grok build image_gen, Codex $imagegen, Gemini, xAI/Recraft/OpenAI keys) as recorded by omd:setup, with a provenance and cost ledger. No channel → prompt pack + manual queue, never stock. Trigger: '이미지 세트 만들어', '에셋 생성', 'generate assets', 'hero image', '아이콘 세트', 'OG 이미지'.

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Create per-subsection evidence packs (NO PROSE): claim candidates, concrete comparisons, evaluation protocol, limitations, plus citation-backed evidence snippets with provenance.

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Audit one research Workspace for declared Unit outputs and Pipeline target Artifacts, writing `output/CONTRACT_REPORT.md`; use for mid-Run coverage snapshots or final delivery completeness, not deep provenance integrity.

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Answer a hard question by fanning it out to a PANEL of models running in parallel — each answering independently with web search and bash, none seeing the others' work — then having Opus 4.8 judge every response into a structured analysis (consensus, contradictions, partial coverage, unique insights, blind spots) and write a final answer grounded in it. The panel is two independent Opus 4.8 runs (slug opus4.8-4.8), Opus 4.8 + GPT-5.5 via codex (opus4.8-gpt5.5), Opus 4.8 + Gemini 3.1 Pro via agy (opus4.8-gemini3.1pro), or all three (opus4.8-gpt5.5-gemini3.1pro). Opus always judges and writes the final answer — the pipeline can't be reversed. Runs on local CLI subscriptions (no metered API), and saves a timestamped provenance .md per run. Use this whenever the user asks to "run it through Fusion", says /fusion, wants a multi-model / panel / ensemble answer, wants a question cross-checked across models, or wants a higher-confidence answer with consensus and blind spots surfaced — even if they don't say "fusion". General-purpose: any topic (research, law, strategy, technical, personal). Best for high-stakes research, design calls, and debugging where being confidently wrong is expensive.

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Freshly judge whether a finished change and its claims meet original acceptance: PASS, FAIL or NOT_PROVEN. Use when: asked for a go/no-go, sign-off or independent verdict.

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Explain why a change, incident or session went as it did, separating proven causes from coincidence. Use when: a postmortem or retro is selected by name.

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Pick the next work in an epic or bead graph; closed is not proven. Use when: asked what is next or whether an epic is done.

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。