「approval」の検索結果

Use when the user asks for a phase-gated refactoring or redesign loop - session-blind workers map and trace the area, empirical probes precede the design, an approval gate guards implementation, and a conformance audit re-checks the branch after any design change. State persists in .episteme/DESIGN-<slug>.md. Requires the refactor agent roles registered by scripts/install-codex.sh. Keywords: refactor loop, redesign, design gate, enumerate before design, conformance audit.

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Autonomous multi-step investigation — deep research carried end-to-end without per-step approval; explicit request only, never for normal feature work.

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。

Load this skill before implementing a fix for a barrier that may originate in a shared component, design system, renderer, or external dependency rather than this project's own code. Decide whether the responsible fix is local, an existing installed capability, or an upstream contribution before writing a workaround. Absolutely always record any temporary local divergence with an owner and removal condition. Under no circumstances open, comment on, or submit anything in an external project without explicit human approval.

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Use when adding, reviewing, or debugging Launch at Login in a macOS app — SMAppService registration, the requiresApproval state, a Settings toggle that reflects reality, and migration off deprecated login-item APIs.

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Produce a self-contained HTML implementation plan for explicit approval before coding.

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。

飞书审批 API：审批实例、审批任务管理。

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Use when auditing a repository for evidence-backed gaps between code, SPECs, architecture, and documentation — before a release, after a review, or when the user asks what is missing or divergent. Confirmed gaps become Draft SPECs via write-specs, a tracked GitHub Epic via create-issues, and orchestrated execution via orchestrator, with an explicit approval gate before any external action.

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End-to-end orchestrator: from a business idea, app idea, or existing app to running Azure deployment with cost estimates and pre-deploy approval. Analyzes your app, auto-detects the right Azure services, scaffolds infrastructure code, and deploys — tailored to your app, not a template.

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Batteries-included agent component for React/Next.js from ui.inference.sh. One component with runtime, tools, streaming, approvals, and widgets built in. Capabilities: drop-in agent, human-in-the-loop, client-side tools, form filling. Use for: building AI chat interfaces, agentic UIs, SaaS...

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Runs a bounded spec-build-review development loop with explicit scope, stop conditions, and human approval gates for risky or ambiguous work.

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 vulnerability exception and risk acceptance tracking system with approval workflows, compensating controls documentation, and expiration management.

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Full dev cycle. Planning tier for clarify and plan; worker tier after approval for autonomous execution, tiered lint, thread rotation, and feedback fixes. Start every change here.

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Generate formal IT/hardware purchase request documents from informal requirements. Use when creating purchase justifications, cost-benefit analyses, ROI calculations, vendor comparisons, or MARP presentation slides for management approval.

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Expert guidance for Salesforce system development and operations management. Use this skill when working with Salesforce configuration, customization, bug analysis, troubleshooting access/permission issues, designing system architecture, or implementing custom Apex/LWC development. Covers sharing settings, approval processes, trigger patterns, integration design, and data modeling best practices.

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Create, verify, transfer, and resume compact project handoff packets for long-running Codex work. Use after a second observed automatic context compaction, immediately after a third compaction, when attention or constraints degrade, a major phase ends, the user asks to move work into a fresh task, work must pause, or an existing handoff needs to be imported. Do not interrupt after the first normal compaction. Always ask before creating a new task, transfer the global roadmap only after explicit consent, preserve the old task title when available, then require the fresh task to ask what work matters most. Preserve objectives, constraints, decisions, evidence, approval boundaries, workspace state, validation, and risks without treating an old summary as current truth.

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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)

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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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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)

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Use when building serverless AI SDK for Python endpoints, handling hook approvals, deferring hooks, or resuming runs across requests.

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Publish or refresh one or a few free-AI-credit offers from screenshots or text — official sources win, unverifiable offers dropped, validates, diffs, commits on approval. Don't use for catalog-wide re-checks, scraping, or bulk imports.

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테스트 없는 기존 코드에 행위 보존 안전망(Characterization → Approval → Mutation)을 구축한 뒤 개선 스킬로 핸드오프. "레거시에 테스트 추가", "안전망 구축", "characterization test" 요청 시 사용. /tdd-legacy로 호출.

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Review whether a Clawix UI pattern, visual decision, copy change, or component extraction belongs in canon, debt, exception, or protected-surface scope.

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Knowledge base from FAA-STD-025f (Preparation of Interface Documentation). Use for IRD/ICD family definitions, common format and content, IRD shall-requirements, ICD design characteristics, verification/VRTM, and CM/revision/approval of FAA Enterprise Subsystem interfaces. Covers rev F preparation rules only; does not replace FAA-STD-005 drawing/spec prep, Order 1800.66 full CM procedures, or non-FAA interface standards.

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Manages your own skill landscape: surveys and compares existing skills (Audit mode), researches the web for new skills/plugins (Explore mode), and is at the same time the installer that generates lean subskills (Skill-Finder, family umbrella, maintenance skills) instead of loading a monolith. Use this skill for "compare/audit skills", "which skills are duplicated", "form skill families", "clean up/consolidate skills", "maintain the skill register", "find skills/plugins for topic X", "install new skills", "browse the skill marketplace", or for `/skill-explorer`. Delivers a sub-report per family and a globally numbered decision list; installs/uninstalls only after a security check and explicit approval.

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。