「evaluate」の検索結果

Use when published content has to be audited — pull channel data, classify winners and losers by percentile across pillar, format, and hook, find the pattern behind performance, and decide what to keep, kill, or scale. Trigger on 'content audit', 'which posts are working', 'content performance review', 'why did that one go viral', 'our reach is dropping', 'evaluate what we posted last month'. Also use when the user only says content does not seem to work anymore. Not for — the plan for next period built on these findings, see `40-next-content-plan-global`; paid ad account review, see `21-ads-audit-global`; the stakeholder report, see `07-marketing-report-global`.

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Post-run self-evaluation system that scores agent output on correctness, clarity, actionability, and conciseness. Use after /team runs, skill executions, or when explicitly asked to evaluate output quality.

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Classify and advance issues or external pull requests through the configured triage workflow. Use explicitly to inspect queues, evaluate an item, recommend a state, or apply an authorized transition.

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Evaluate proposed repository harness changes with pinned Darwin tools and explicit evidence limits.

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Evaluates observed product outcomes against a prior hypothesis; does not run experiments or change user treatment.

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Evaluates new product opportunities through demand, channels and economics before committing to build.

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Research Elixir/Phoenix/Ecto topics or evaluate Hex libraries; Use when learning about libraries, patterns…

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Research Elixir/Phoenix/Ecto topics or evaluate Hex libraries (--library). Use when learning about libraries, patterns, or comparing approaches. Searches HexDocs, ElixirForum, GitHub.

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Research Elixir/Phoenix/Ecto topics or evaluate Hex libraries (--library). Use when learning about libraries, patterns, or comparing approaches. Searches HexDocs, ElixirForum, GitHub.

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Self-improving loop for plugin skills. Reads program.md, proposes one mutation per iteration, evaluates against deterministic scorer, keeps improvements via git, reverts failures. Targets weakest skill+dimension. Use with /loop for overnight runs.

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Adaptive content summarization - auto-detect content type and produce relevant summary

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Autonomous experiment loop: hypothesize > modify > test > evaluate > keep/discard > repeat. Run N experiments automatically with measurable metrics. Works for performance optimization, A/B testing, prompt engineering, and any measurable improvement task.

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Evaluate LLM systems using automated metrics, LLM-as-judge, and benchmarks. Use when testing prompt quality, validating RAG pipelines, measuring safety (hallucinations, bias), or comparing models for production deployment.

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Diagnoses and fixes skills in the dotnet/skills repository that lose to their own baseline, fail to activate, time out, or return "no credible improvement". Use when an evaluation verdict is a regression or underpowered, when a skill regressed after a change, when /evaluate reports no results, or when deciding whether a weak skill should be strengthened or retired. Do not use for scaffolding a brand-new skill (use create-skill) or a brand-new eval (use create-skill-test).

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Use ML.NET to train, evaluate, or integrate machine-learning models into .NET applications with realistic data preparation, inference, and deployment expectations. USE FOR: ML.NET integration; local model training or retraining; inference pipelines, model loading, evaluation, and deployment review. DO NOT USE FOR: unrelated stacks; generic tasks that do not need this specific guidance. INVOKES: inspect the repository context, edit targeted files, and run relevant build, test, lint, or validation commands when changes are made.

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Use when you need to add or evaluate Maven dependencies that improve code quality or domain modeling — including nullness annotations (JSpecify), static analysis (Error Prone + NullAway), functional programming (VAVR), architecture testing (ArchUnit), or money and currency support (JavaMoney) — and want a consultative, question-driven approach that adds only what you actually need. This should trigger for requests such as Add Maven dependencies; Add JSpecify nullness dependencies; Add Error Prone NullAway dependencies; Add VAVR functional dependencies; Add ArchUnit architecture testing dependencies; Add JavaMoney dependencies. Part of Plinth Toolkit

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Use when Java design, refactoring, or implementation tradeoffs should be evaluated with Kent Beck's simple design rules, including passes the tests, reveals intention, has no duplication, and has the fewest elements. This should trigger for requests such as Apply simple design rules; Review this design with Beck's rules; Choose between these refactoring options; Keep this Java design simple. Part of Plinth Toolkit

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Operating-system distillation of LlamaIndex — the leading RAG / document-agent framework. Activate when the calling agent must build, debug, harden, or evaluate a Retrieval-Augmented Generation pipeline over unstructured/private data, decide between RAG primitives (Index types, retrievers, query engines, routers, agents), or pick LlamaIndex vs LangChain / Haystack / raw vector store for a coding task. Encodes the 5-layer mental model (Documents → Nodes → Indices → Retrievers → Query Engines / Response Synthesizers), the canonical RAG bootstrap SOP from baseline `VectorStoreIndex` through hybrid + reranker + eval-loop hardening, the official 13-failure-mode checklist, and 5 dilemma cases distilled from docs, GitHub issues, and 2025 production post-mortems.

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Decomposed, multi-criteria metric design for LLM pipelines. The metric IS the model — change the metric and the optimizer changes behavior. Decompose by default; bool during compile, float during eval; calibrate against human; mitigate judge bias. Search keywords: LLM-as-judge, llm as judge, eval metric, evaluation score, scoring function, rubric, RAGAS, G-Eval, judge bias, verbosity bias, how to evaluate LLM output.

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Apply timeless marketing and business principles to any problem. Use when someone needs strategic thinking, wants to evaluate a marketing decision, needs a framework for a tough choice, or mentions "first principles," "should I do X," "what would work here," or wants to think through a marketing problem systematically.

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Toolkit management: create and evaluate skills and agents, manage routing tables, generate Claude.md.

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Scoring framework for test-kitchen cookoff and omakase-off. Invoked at Phase 4 to evaluate implementations using 5-criteria scoring. Do not invoke directly - called by cookoff/omakase-off.

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Same-epoch comparison of training runs across wandb, neptune, tensorboard, or mlflow. Aligns runs at the student's current step (never current-vs-final-of-baseline) and separates proxy metrics from downstream targets. Use when the user asks to compare runs, check if a run is improving, track lag against a baseline, rank experiments, or evaluate run-vs-run performance.

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Use when the user wants to analyze dataset bias, create stratified samples, evaluate fairness, or plan dataset collection. Triggers on phrases like "dataset bias", "stratified sample", "class imbalance", "data distribution", "fairness analysis", or "ethical review".

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