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Audit a completed AI development phase to check whether its planned evaluations were implemented. Produce EVAL-REVIEW.md with a score, verdict, identified gaps, and a plan to address missing coverage.
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Turn completed case intake into a demand letter draft for payment, contract breaches, or other claims. Review confidentiality and admission risks, refine the wording, and produce a Word document with a checklist for follow-up after sending.
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Manage Google Tasks through gog: view task lists, add or update tasks, mark work complete, and remove entries. The workflow includes explicit account selection and checks before changes.
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Run repeated, independent security audits of a repository or selected directory with Codex Security. It combines validated findings into a report that records the reviewed scope, exclusions, and any incomplete coverage.
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Structures biological experiments so inference is valid by construction, covering Fisher's principles (randomization, replication, local control), the experimental-vs-observational unit distinction and pseudoreplication (Hurlbert 1984; Lazic 2018), randomization mechanics (complete, restricted, stratified, rerandomization, run-order), blocking layouts (randomized complete block, Latin square, incomplete block), factorial designs and interactions, and the split-plot/nested error strata hidden inside multi-batch genomics. Use when deciding the experimental unit and what counts as a replicate, planning randomization and run order, choosing a blocked/factorial/split-plot/nested layout, avoiding pseudoreplication in cell-culture or animal studies, or specifying the random-effects structure of the analysis model. For assigning samples to sequencing batches/lanes/plates and batch-effect correction see experimental-design/batch-design; for regulated clinical-trial randomization see clinical-biostatistics.
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Guides systematic review of processing documentation for completeness against GDPR Articles 5, 13-14, 24, 28, and 30. Activate when auditing documentation or preparing for inspections. Keywords: documentation review, processing records, completeness, privacy notices, RoPA.
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Automatically complete partial code snippets while satisfying semantic constraints including variable types, invariants, pre/post-conditions, interface contracts, and expected input/output behavior. Use when users provide incomplete code with specific requirements like "complete this function that takes a list and returns sorted unique elements" or "fill in this method body that must maintain the invariant that x stays positive" or "implement this interface method with these type constraints." Produces compilable, executable code with tests and a constraint satisfaction report.
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Closes a completed development branch by checking the final diff and proof, presenting merge, pull-request, keep, or discard options, and cleaning up only after the user or repository workflow chooses a path. Use when feature work is complete and the branch must be integrated or retired. Not for claiming a feature is complete before verification.
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Validate a completed plugin refactor against its task evidence, baseline assessment, documentation, and current validators. Use after implement-refactor or when deciding whether refactoring is complete.
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Structures biological experiments so inference is valid by construction, covering Fisher's principles (randomization, replication, local control), the experimental-vs-observational unit distinction and pseudoreplication (Hurlbert 1984; Lazic 2018), randomization mechanics (complete, restricted, stratified, rerandomization, run-order), blocking layouts (randomized complete block, Latin square, incomplete block), factorial designs and interactions, and the split-plot/nested error strata hidden inside multi-batch genomics. Use when deciding the experimental unit and what counts as a replicate, planning randomization and run order, choosing a blocked/factorial/split-plot/nested layout, avoiding pseudoreplication in cell-culture or animal studies, or specifying the random-effects structure of the analysis model. For assigning samples to sequencing batches/lanes/plates and batch-effect correction see experimental-design/batch-design; for regulated clinical-trial randomization see clinical-biostatistics.
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The sprint completeness oracle — a mechanical, exit-code-driven check that every feature in a sprint manifest is provably complete across three dimensions (a done-record reference, resolvable edge tests, and a resolvable wiring test). Fails loud with a per-feature gap list naming the missing dimension. Read-only. Use to gate a sprint before it is called done (Slice A of the sprint-mode epic), or any time you want a completeness reading.
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Expert-level mathematical logic knowledge. Use when working with propositional logic, first-order logic, proof theory, model theory, computability theory, Godel incompleteness theorems, set theory, type theory, or formal verification. Also use when the user mentions 'formal proof', 'Godel', 'incompleteness', 'Turing machine', 'decidability', 'Zermelo-Fraenkel', 'axiom of choice', 'model theory', 'completeness theorem', 'computability', 'formal language', or 'type theory'.
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Author a completed-spec fixture — a seeded intent graph plus a believable four-phase interview transcript — for a brunch feature, so it can feed any downstream tooling that consumes a completed spec or dogfood the UI. Use when you need a real completed specification in .brunch/brunch.db for testing or demo: pick a feature from memory/SPEC.md (or a free description), choose a stress profile (coverage|clean), and produce a registered scenario seed + an interview-questions doc that match exactly.
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Operational guide for integrating the NV oOS Complete purchase flow (vendor checkout API client) into standalone WordPress plugins — docs-hub 0.5.3 (stack added in PR #6963, shipped 0.5.2 → 0.5.3) and nvoos-content-graph 1.0.10 (live). Covers the vendor API contract (session/verify/health, nvoos-oos-complete product, 424/502 error passthrough), the five-class client stack (config, vendor client, license store, installer, REST controller), the purchase-modal JS/CSS port recipe (class-prefix rename, keep-the-JS-identical trick), wp.org commerce compliance (External Services disclosure, WPORG-REVIEW-COMMERCE-NOTES.md, js.stripe.com service exception), and the checkout test conventions (direct controller calls, pi_ fixture rules, zero-HTTP assertions). Use when adding the Get NV oOS Complete upsell/checkout to a plugin, porting the commerce stack to a new standalone plugin, extending the purchase flow, or debugging a checkout integration.
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Structures biological experiments so inference is valid by construction, covering Fisher's principles (randomization, replication, local control), the experimental-vs-observational unit distinction and pseudoreplication (Hurlbert 1984; Lazic 2018), randomization mechanics (complete, restricted, stratified, rerandomization, run-order), blocking layouts (randomized complete block, Latin square, incomplete block), factorial designs and interactions, and the split-plot/nested error strata hidden inside multi-batch genomics. Use when deciding the experimental unit and what counts as a replicate, planning randomization and run order, choosing a blocked/factorial/split-plot/nested layout, avoiding pseudoreplication in cell-culture or animal studies, or specifying the random-effects structure of the analysis model. For assigning samples to sequencing batches/lanes/plates and batch-effect correction see experimental-design/batch-design; for regulated clinical-trial randomization see clinical-biostatistics.
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Generate the success criteria for a task or question, then review work against them. Given a task, goal, or open-ended question, decompose it into scenarios, evaluation perspectives, and fine-grained weighted YES/NO criteria using the Recursive Expansion Tree (RET) method; if work is supplied, score it criterion-by-criterion and surface what is missing or could be better. Use when asked to self-review or check your own work, judge whether a task is done well or completely, build a definition-of-done or completeness checklist, create an evaluation rubric or grading criteria, score or grade answers to a question, set up an LLM-as-judge rubric, or when the user mentions self-review, completeness check, success criteria, evaluation criteria, scoring rubric, Qworld, or the RET algorithm.
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Structures biological experiments so inference is valid by construction, covering Fisher's principles (randomization, replication, local control), the experimental-vs-observational unit distinction and pseudoreplication (Hurlbert 1984; Lazic 2018), randomization mechanics (complete, restricted, stratified, rerandomization, run-order), blocking layouts (randomized complete block, Latin square, incomplete block), factorial designs and interactions, and the split-plot/nested error strata hidden inside multi-batch genomics. Use when deciding the experimental unit and what counts as a replicate, planning randomization and run order, choosing a blocked/factorial/split-plot/nested layout, avoiding pseudoreplication in cell-culture or animal studies, or specifying the random-effects structure of the analysis model. For assigning samples to sequencing batches/lanes/plates and batch-effect correction see experimental-design/batch-design; for regulated clinical-trial randomization see clinical-biostatistics.
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Structures biological experiments so inference is valid by construction, covering Fisher's principles (randomization, replication, local control), the experimental-vs-observational unit distinction and pseudoreplication (Hurlbert 1984; Lazic 2018), randomization mechanics (complete, restricted, stratified, rerandomization, run-order), blocking layouts (randomized complete block, Latin square, incomplete block), factorial designs and interactions, and the split-plot/nested error strata hidden inside multi-batch genomics. Use when deciding the experimental unit and what counts as a replicate, planning randomization and run order, choosing a blocked/factorial/split-plot/nested layout, avoiding pseudoreplication in cell-culture or animal studies, or specifying the random-effects structure of the analysis model. For assigning samples to sequencing batches/lanes/plates and batch-effect correction see experimental-design/batch-design; for regulated clinical-trial randomization see clinical-biostatistics.
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根据OpenClaw的功能分类和QA证据，复核质量、功能完整性及长期支持评价，验证输入数据并更新成熟度评分卡文档，同时记录评价理由和复核条件。
在报告任务完成或修复成功前运行测试、构建等检查，核对完整输出，并在提交修改或创建拉取请求前，以当前实际结果为依据说明状态。
将规格文档或工单中的任务落实为代码，定期进行类型检查和测试，完成后使用code-review技能审查变更，并提交到当前分支。
检查已完成预订的Itô GPU环境是否可用于机器学习训练。目前不执行训练，而是说明尚未提供的功能，并整理未来交接所需的条件。
Generate complete songs locally from lyrics and tags describing mood, instruments, or genre. The skill guides setup, input formatting, memory-saving settings, and MP3 output using the open-source HeartMuLa music models.
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Review a week's calendar, tasks, and notes to identify completed commitments, overdue work, stalled projects, and follow-ups. Turn those findings into a realistic next-week plan that accounts for scheduled commitments and available capacity.
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