“completion”的搜索结果

Activates Standard of Excellence enforcement for the current session. Load before starting any task to apply completion standards: finish the whole thing, fix the root cause, ship the complete working result. Blocks partial solutions, workarounds, deferred threads, and invented content limits when the permanent solve is within reach. Triggers: 'do the whole thing', 'boil the ocean', 'standard of excellence', 'finish it completely', before starting any implementation, refactoring, or multi-step task where partial output is a risk. Does NOT apply to: read-only queries, one-line typo fixes, pure knowledge questions, or single-output summarize requests.

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Independent LLM judge evaluates task completion separately from the executing agent, catching false success claims by reviewing task goal, actions taken, final state, and evidence. Produces PASS/FAIL with confidence score and reasoning.

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Use when receiving code review feedback (especially if unclear or technically questionable), when completing tasks or major features requiring review before proceeding, or before making any completion/success claims. Covers three practices - receiving feedback with technical rigor over performative agreement, requesting reviews via code-reviewer subagent, and verification gates requiring evidence before any status claims. Essential for subagent-driven development, pull requests, and preventing false completion claims.

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Validate acceptance criteria and feature completion. Use when checking if features pass, validating test results, verifying acceptance criteria, or determining feature completion status.

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Voert de iteratieve desk-review-&-completion-cyclus uit op een regelrecht-corpus — plannen, harvesten/migreren/uitbreiden, multi-agent valideren, bevindingen 4-weg classificeren (modellering-fout / wetgevings-fout / engine-limitatie / acceptabele untranslatable), synthese + heroverweging, documenteren (wetgevings-/stelselfouten-analyse, fixes-plan, engine-limitaties, diagrammen, corpus-status), verifiëren tegen externe bronnen (resolutie-tracker + bronnen-dossier), en een eindrapport per cyclus. Gebruik dit bij corpus-review op correctheid/coverage zonder live expert-sessie, het opstellen van een wetgevings-notitie-bron, schema-migratie, het harvesten van nieuwe wetten, of het aansturen van een autonome completion-loop. Dossier-agnostisch; de regelrecht-methode is de vaste taal. Voor live-validatie met experts: zie regelrecht-audit-products.

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Use when building CLI tools, implementing argument parsing, or adding interactive prompts. Invoke for parsing flags and subcommands, displaying progress bars and spinners, generating bash/zsh/fish completion scripts, CLI design, shell completions, and cross-platform terminal applications using commander, click, typer, or cobra.

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Create custom modules for [Harbor Boost](https://github.com/av/harbor/tree/main/boost), an optimizing LLM proxy. Use when building Python modules that intercept/transform LLM chat completions—reasoning chains, prompt injection, structured outputs, artifacts, or custom workflows. Triggers on requests to create Boost modules, extend LLM behavior via proxy, or implement chat completion middleware.

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Always use this skill before claiming any work is done, fixed, passing, or complete — even if it seems obvious. Also use when receiving code review feedback from any source (human or automated), when finishing a task or major feature, before committing or creating a PR, or when about to express satisfaction with work. Covers three practices: receiving feedback with technical rigor (no performative agreement), triggering code review via /code-review command or cook/fix pipelines, and verification gates (run the command, read the output, then make the claim). Never skip — the cost of false completion claims is higher than the cost of verifying. References: code-review-reception.md, requesting-code-review.md, verification-before-completion.md.

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Prove completeness for non-trivial implementation, migration, translation, content, integration, and audit work by inventorying the full affected surface, tracking coverage, auditing omissions, and refusing unsupported completion claims.

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Prevents premature exit and infinite loops. Three mechanisms: Completion Promises, Scratchpads, Loop Detection.

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Never claim completion without fresh verification evidence

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Use when claiming 'done', suggesting a commit, push, or PR — runs the evidence gate so completion claims come from fresh output in this message, not memory or earlier runs.

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dev-graph node を metadata で検索したいとき、依存・tombstone・completion 状態を副作用なしで確認したいときに使う。

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Azure OpenAI SDK for .NET. Client library for Azure OpenAI and OpenAI services. Use for chat completions, embeddings, image generation, audio transcription, and assistants. Triggers: "Azure OpenAI", "AzureOpenAIClient", "ChatClient", "chat completions .NET", "GPT-4", "embeddings", "DALL-E", "Whis...

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ISTQB/JSTQB のテスト完了（test completion）活動のうち、テスト実行結果の評価と完了基準（exit criteria）判定を支援するスキル。テスト実行ログ（test-execution-log.md）と test-plan の完了基準を突き合わせ、完了とみなせるかを判定した test-summary-report.md を作る。テスト完了時の最終レポートにも、テスト途中の中間評価にも使える。「テスト結果をまとめて」「完了基準を満たしたか判定して」「テストレポートを書いて」「今どこまでテストが進んだか評価して」と依頼されたときに使う。単発のテスト作成依頼（単にユニットテストを書きたいだけ）は対象外。/test-report <テスト対象名> で明示的に呼び出されたときのみ使用する。

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Run verification commands and confirm output before claiming success

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Verify completion and success claims with fresh evidence. Use before claiming a task is complete, a fix works, tests pass, or a feature is ready for GO.

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Build with OpenAI's stateless APIs - Chat Completions (GPT-5, GPT-4o), Embeddings, Images (DALL-E 3), Audio (Whisper + TTS), and Moderation. Includes Node.js SDK and fetch-based approaches for Cloudflare Workers. Use when: implementing chat completions with GPT-5/GPT-4o, streaming responses with SSE, using function calling/tools, creating structured outputs with JSON schemas, generating embeddings for RAG (text-embedding-3-small/large), generating images with DALL-E 3, editing images with GPT-Image-1, transcribing audio with Whisper, synthesizing speech with TTS (11 voices), moderating content (11 safety categories), or troubleshooting rate limits (429), invalid API keys (401), function calling failures, streaming parse errors, embeddings dimension mismatches, or token limit exceeded.

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协助完成OpenClaw测试版、稳定版和长期维护版的准备、发布、恢复及验证，涵盖已批准修复的移植、验证记录、发布后检查和收尾工作。

• 准备OpenClaw测试版和稳定版
• 将已批准修复移植到长期维护版
• 恢复中断的发布

通过指定的Night Watch负责人协调运维人员配置的Team服务器更新，跟进现有更新机制的验证、数据库迁移与恢复结果，直至更新正式验收。

• 将Team更新到官方main分支
• 协调失败或延期更新的后续执行
• 协调数据库迁移与恢复

将功能开发、大规模代码重构和拉取请求审查交给Codex、Claude Code或OpenCode在后台执行，并处理独立检出目录、进度监控及完成或失败通知。

• 将功能开发交给后台代理
• 在独立工作目录中进行大规模重构
• 请编码代理审查拉取请求

通过gh查看和创建GitHub议题与拉取请求，发表评论，检查评审和自动测试结果；调查失败日志、重新运行任务，并确认已获授权的合并状态。

• 创建或更新议题和拉取请求
• 检查拉取请求的评审与检查结果
• 调查CI失败日志并重新运行任务

通过先写测试、确认失败再实现的流程添加功能或修复缺陷。只编写让测试通过所需的代码，再保持测试通过并整理代码，完成前运行项目的完整测试集。

• 从测试开始实现新功能
• 用测试复现并修复缺陷
• 保持测试通过并重构代码

将模糊的功能需求或影响较大的变更整理为验收标准或交接资料，明确范围、前提、风险、预期行为与验证方法，并检查现有规格中的遗漏和矛盾。

• 梳理模糊的功能需求
• 制定认证变更或数据迁移的验收标准
• 向其他智能体或团队交接实施要求