742 skills · sorted by relevance
Call the free Agnes text model API for chat completions, streaming answers, coding help, tool-calling experiments, and OpenAI-compatible text generation. Use when the user asks for 免费文本, 免费的文本模型, free text API, Agnes text, Agnes-2.0-Flash, free LLM calls, or wants to use a free chat/completion model through an Agent skill.
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Builds command-line tools in Go with Cobra, the library behind kubectl, Hugo and the GitHub CLI: subcommands, flags, argument validation, generated help and shell completions. Use when a user asks to create a Go CLI, add subcommands or flags, validate arguments, bind flags to config files and environment variables with Viper, or generate bash, zsh, fish or PowerShell completions.
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Expert in designing engaging learning experiences - completion optimization, multimedia learning, interactivity, gamification, and retention strategies. Covers the science of how people learn and practical techniques to make courses that students actually finish. Use when "learning experience, course engagement, completion rate, make learning fun, gamification, interactive course, learning design, " mentioned.
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Assess nontrivial completion evidence.
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Expert guide for LLM observability in production — token cost tracking, hallucination rate monitoring, latency analysis, prompt/completion logging, model comparison dashboards, and AI-specific tracing (Langfuse, Helicone, Lunary, OpenTelemetry GenAI) / Panduan ahli observabilitas LLM di produksi — pelacakan biaya token, pemantauan tingkat halusinasi, analisis latensi, logging prompt/completion, dashboard perbandingan model, dan tracing khusus AI.
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Verifies a completion, fix, passing, compatibility, release, or readiness claim with fresh task-specific evidence mapped to the actual requirements. Use before saying a change is done, before merge, after review fixes, and at release gates. Not a substitute for diagnosing the cause of a failing check.
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Review code quality, receive feedback with technical rigor, verify completion claims. Use before PRs, after implementing features, when claiming task completion. Includes scout-based edge case detection and native Task pipeline orchestration.
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Rigorous self-assessment checklist before marking any task as complete. Use when about to claim task completion, before final commit, when user asks "is it done?", or when transitioning from implementation to reporting. Prevents premature completion claims by requiring evidence for every assertion.
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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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Close a completed GitHub milestone and generate a completion summary. Use when a sprint or release is finished and needs to be officially closed. Audits open and closed issues, offers to carry forward open items to a new or existing milestone, closes the milestone, updates Project V2 Status to Done for closed issues, and produces a completion report.
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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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