「iab」の検索結果

Operate managed Aeon instances from memory/instances.json - health-check, dispatch, and status snapshots (control), plus a fleet scorecard of runs, tokens, cost, and reliability (scorecard).

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Three-mode idea engine - generate collides the week's zeitgeist with what you can ship into scored wedges; validate viability-screens the idea backlog; memo writes evidence-backed startup memos.

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Turns the raw company-name string on a lead row into the short human form a person would say out loud, so it can be dropped into email copy. Triggers on "clean these company names", "company name variable", "strip the LLC and Inc", "the company names look robotic", "normalize company names for the campaign". Outputs company_clean, one short string per row.

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Names 2 other right-fit people at the prospect's own company so an email can ask "is this you or them?" without ever naming the person being emailed. Triggers on "name two other people at the company", "who else works there", "should I be talking to someone else", "colleague name variable", "other prospects line", "wrong person CTA". Outputs other_prospects, a ready-to-drop phrase like "Jeff Barg or Rob Cook".

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Reusable approval-loop pattern for fanning out lead personalization across parallel Claude Code Task sub-agents. Shows the user 1 sample personalization, collects feedback, runs 10 more, approves, runs 10 more — stops when 2 consecutive rounds have zero edits, then scales to the full list. ALWAYS uses Claude Code Task tool sub-agents — never an external Anthropic/OpenAI API key. Use when any skill needs per-lead custom variables (situation lines, value lines, CTAs).

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Turns the raw first-name field on a lead row into the name a person would actually be greeted by, so it can open an email. Triggers on "clean these first names", "first name variable", "the greeting says Hi DR MATTHEW", "strip the titles off the names", "some rows have the whole name in the first-name column", "normalize first names for the campaign". Outputs first_name_clean, one short string per row.

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Framework for running single-variable cold email experiments. Defines experiment types (list-only, copy-only, combined), confidence weighting, minimum sample sizes, and success criteria. Use when the user wants to improve a campaign, test a new list vs old one, or compare copy variants. Prevents the

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Comprehensive product research and opportunity analysis for Amazon sellers. Analyzes demand, competition, profit potential, market entry barriers, and validates product ideas. Covers product sourcing, pricing strategy, and go-to-market planning. Use when the user asks about researching a product to sell, validating product ideas, product opportunity analysis, market research for Amazon, competition analysis, profit potential, should I sell this product, product viability, or any general product research questions.

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Get JSON out of the model reliably. Prefer tool_use with a schema over prompted-JSON, validate on receive, retry on parse fail. Use when downstream code will parse the response.

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Replace arbitrary timeouts with condition polling for reliable async tests

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Evidence-first development — surround the implementation with an executable spec and a gauntlet of constraints (tests, types, coverage, mutation) so line-by-line review becomes optional. Use when the user explicitly asks for high-assurance or evidence-first work ("reliable", "TDD", "prove it works", "I won't read the code"), or when the change touches high-stakes domains (money, auth, data loss, concurrency, public API). For routine changes where the user just wants normal tests, write good tests directly instead of invoking this loop.

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Perform bulk code refactoring operations like renaming variables/functions across files, replacing patterns, and updating API calls. Use when users request renaming identifiers, replacing deprecated code patterns, updating method calls, or making consistent changes across multiple locations.

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Weekly. Confirms that the registrar, the host, and the hosted database still match what the inventory says, in one read only pass per project, and names every drift with the exact screen, the exact current value, and the exact intended value. It records that a variable exists and never its value, it fixes nothing inside an account, it buys nothing in any state, and it never rotates a key.

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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 when the user runs /gsd-workstreams or asks to list, create, switch, status, progress, complete, or resume parallel workstreams for concurrent milestone work. The active one is tracked in the GSD_WS variable.

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The HyperFrames composition contract — build one renderable project. Use for composition structure, the `data-*` timing attributes, `class="clip"`, tracks, sub-compositions, variables, framework-owned media playback, deterministic-render rules, and validation. Also covers Tailwind projects and the STORYBOARD.md / SCRIPT.md plan formats. Read before writing composition HTML.

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Your software-engineering brain trust. Named personas across 11 cells covering cloud architecture, reliability/SRE/observability, data and storage, security, FinOps, languages and runtimes, systems programming, web and frontend…

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Wrap any command-line tool into a JSON-emitting agent skill. Use when you want to make a CLI reliably callable and parseable by an AI agent — introspect its --help, define a structured-output (--json) contract and a stable error contract, then generate a SKILL.md wrapper with verified examples. Triggers on "wrap this CLI", "make X agent-usable", "generate a skill for this command", "turn a tool into an agent skill".

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Phase 2 of the prd-taskmaster pipeline: spec generation and task parsing. Loads a template (comprehensive|minimal), fills it with DISCOVER-phase constraints and answers, validates the spec (placeholders_found, grade thresholds), parses the PRD into tasks via task-master, runs TaskMaster's native complexity analysis, and expands every task into verifiable subtasks. Autonomous-safe. Declares GENERATE complete so HANDOFF can follow.

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用于在PyQt5应用程序中管理类内部变量作用域，通过实例变量(self.variable)替代全局变量(global)，实现跨函数的数据共享、默认值初始化及条件更新。

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Break a plan, spec, or conversation into dependency-aware, independently verifiable tickets. Use explicitly when the user wants work sliced for local planning or an issue tracker.

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Identify negotiable contract terms and draft options with commercial tradeoffs and legal review questions. Use for this Bot Generator Bot template; creates drafts or simulations within its declared scope.

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Implements transactions, idempotency and interrupted-operation recovery; not schema migration or a mandatory background-job platform.

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Audits existing tests for risk coverage, reliable oracles and maintenance value; does not edit tests.

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。