「gates」の検索結果

GStack canary method for staged release thinking, smoke gates, rollout checks, and rollback signals before or after deployment.

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。

GStack deployment setup method for planning environment, config, secrets, build, deploy, and verification gates before release operations.

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。

GStack land-and-deploy method for explicit landing and deployment workflows with tests, release notes, rollout gates, and rollback planning.

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 splitting content work between a raw drafting model/window, an editor-in-chief, and the user's final taste review. It keeps prompts loose enough for judgment while preserving facts, account boundaries, and publish gates.

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Guidelines and strategies for constructing meaningful test suites, property-based tests, metamorphic relations, and fuzzing harnesses. Trigger when: - Designing verification bounds, writing tests, creating test invariants, or implementing validation gates. - Prompt contains: testing, test design, property-based testing, PBT, metamorphic testing, fuzzing, integration tests, E2E.

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The map of nuxt-crouton's GitHub workflows and the deploy-pipeline anatomy — what each workflow gates, how a merge reaches Cloudflare, which secrets/tokens exist where, and how the agent-pipeline workflows chain. Use when a CI check fails and you need to know what it is, when asked "which workflow deploys X / why did staging update / what does this check gate / where is that secret / why didn't my label trigger anything", when wiring a new app into CI, or when debugging a deploy that never fired. Trigger phrases: "what runs on merge to main", "map of the workflows", "WORKER_SECRETS_JSON", "why is the daily sweep not running", "deploy-apps vs deploy-app", "which token does this workflow use".

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How to actually run every kind of test in nuxt-crouton (unit/e2e/typecheck/lint, verified commands) plus the HONEST coverage picture — which packages have real tests vs zero, the dormant skipped/todo cases behind three mocking walls, and what CI truly gates vs what CLAUDE.md claims. Use when asked "how do I run the tests", "is X covered by tests", "what does CI actually check", "why is this suite skipped", "where do I add a test", or before trusting a green build as evidence that something works.

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。

Turn a proven POC into a real, crouton-native, documented package(s) + a consuming app — the deliberate "step back and rebuild" checkpoint. SPEC-DRIVEN — the POC discovers a behaviour spec (captured at each sign-off), graduation freezes it as authoritative, the app is rebuilt the crouton way to satisfy it, and nothing is done until it's checked + signed off against two gates (experience + crouton-conformance). Use when a `pocs/*` app is proven and "done", or asked to "graduate this poc", "make it a real app", "extract the package".

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Verified lookup pack for crouton's schema→CRUD generation domain — the fieldsFile JSON format, the complete field-type catalog with aliases, the naming contract, the exact artifact list one generate run produces, the seed/default-layout pipeline, dialect defaults, and rollback semantics. Use when writing or reviewing a collection schema, when asking "what field types exist / what does `crouton config` actually generate / where did this file name come from / why did I get a pg schema", when a generated file's name or path doesn't match the docs, or when removing/regenerating a collection. Trigger phrases — "what field types are supported", "schema JSON format", "what files does crouton generate", "naming convention for collections", "rollback a collection", "dialect default". The generation WORKFLOW (steps + sign-off gates) lives in the `crouton` skill — this is the reference it doesn't carry.

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AID methodology phase gate enforcement. Ensures work follows correct 6-phase order (Discovery -> PRD -> Tech Spec -> Impl Plan -> Dev -> QA & Ship). Enforces gates, validates transitions. Use before any work to check the current phase and refuse work belonging to a later phase.

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Drives the Phase 4-5 dev pipeline state machine with sub-agent reviews and bounded autoresearch gates. Use when running /pipeline or executing an approved plan.

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Central entry point of the afonsoft agent harness. Use when starting a new project, resuming an existing one, planning features/Epics/releases, or running any multi-step agent-driven work. Validates and reconciles SPECs (SDD), audits the codebase and harness for gaps (security, architecture, performance, hygiene), proposes improvements, fragments work into GitHub Issues, delegates implementation/QA/review to specialized skills, and re-validates everything until delivery. Also use to review unapproved SPECs, reconcile open GitHub Issues with code, or run a final gap check before closing a release.

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AI-powered autonomous data extraction that navigates complex sites and returns structured JSON. Use this skill when the user wants structured data from websites, needs to extract pricing tiers, product listings, directory entries, or any data as JSON with a schema. Triggers on "extract structured data", "get all the products", "pull pricing info", "extract as JSON", or when the user provides a JSON schema for website data. More powerful than simple scraping for multi-page structured extraction.

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Audit, rename, and polish a GitHub repository across its local checkout and remote settings. Use when Codex needs to synchronize a local and GitHub repository name, set the description, homepage URL, or topics, create repository branding and a GitHub social preview, restructure a README, or prepare and publish a cohesive repository identity. Uses GitHub CLI for supported remote operations and delegates visual identity work to $create-context-aware-logos.

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Audit local AI coding-agent sessions with agenttrace for cost, tool failures, latency, anomalies, health, diffs, and CI gates.

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。

Orchestrate autonomous AI development pipelines through your Kanban board (Asana, GitHub Projects, Linear). Manages multi-worker Claude Code dispatch, deterministic quality gates, adversarial review, per-task cost tracking, and crash-proof pipeline execution.

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 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。

Multi-language test infrastructure for the Pump SDK — Rust unit/integration/security/performance tests, TypeScript Jest tests, Python fuzz tests, shell test orchestration, Criterion benchmarks, and CI quality gates.

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 structuring a multi-session learning roadmap with milestones, gates, and pacing calibrated to learner level and goal.

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。

Coordinate Codex work across the current task, internal subagents, user-owned tasks, worktrees, handoffs, formal review gates, or recurring automation. Use for delegation, parallel roles, another Codex task, cross-repo work, QA/review handoffs, scheduled follow-up, or task inspection and continuation. Chinese triggers include 子Agent/子进程/子对话, 另一个对话接手, 并行处理, 分给QA, 多仓库收口, 巡检, 持续推进, 定时自动继续, 创建/读取/转交任务.

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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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Translate a fuzzy analytical question into a rigorous investigation plan. Interrogates the ask, grounds the plan in the available data dictionary, applies analytical best practices, and produces a structured brief of query specifications for a downstream query-writing skill. Plans, does not write SQL. Use when: "why did X drop", "is there a relationship between A and B", "who are our highest-value customers", "what's driving the change in Y", "investigate this trend", "design an analysis for", "scope this analytical question". (narrative-common)

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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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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。