「audit」の検索結果

CP-7 retrospective: audit checkpoints, evidence quality, action items, and harvest candidates. Closes the V-model cycle and feeds the next run. Use via /retro after ship, escalate, or significant session.

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Inspect an agent repository and optional traces, interview the user, write reviewed Task Specs, build and audit Harbor tasks, and bootstrap reusable project World Knowledge Skills. Use for agent evals, benchmark design, Task generation, controlled Environments, synthetic data, Verifiers, Harbor runs, calibration, or continuous benchmark maintenance.

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INVOKE THIS SKILL when routing a LangGraph agent with a decision model (TypeSafe Jev, SemIf) instead of an LLM, or when auditing an existing agent for LLM calls that only produce a routing decision. Covers langchain-typesafe Noul/Choice/Score, reading answers correctly, threshold design, and LangSmith Gateway wiring.

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Use when deciding WHERE to point SEO effort, not how to write a page. Triggers: a new site or brand with no rankings and no authority ("cold start", "starting from zero", "nobody knows us"), deciding what to double down on, a page or cluster that ranks but earns nothing, hunting emerging or newly-coined keywords before competitors arrive, "should we build a cluster or one page", "what do we write next", "we get impressions but no clicks", "our traffic plateaued despite publishing", whether to chase a head term at all, or turning any of it into recurring automation. Also use when asked why an SEO effort stalled despite consistent output. NOT for writing an article, keyword expansion mechanics, or auditing a single tactic for penalty risk.

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Use when implementing a new Spark expression in DataFusion Comet. Walks through cloning latest Spark master to study the canonical implementation, checking the upstream datafusion-spark crate before writing native code, building the Comet serde and Rust wire-up from the contributor guide, then running audit-comet-expression to drive a test-coverage iteration loop.

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Use when picking the next Spark expression to implement natively in Comet, or when asked whether a specific codegen-dispatched expression is worth a native Rust implementation. Scores the candidate on how likely a native path can be 100% Spark-compatible and how much throughput or allocation it would save versus the JVM codegen dispatcher, records the verdict in the per-expression audit log so disqualified candidates are not re-litigated, and files a GitHub issue for the recommendation.

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Build, test, and drive iOS apps in the Simulator with 30 token-efficient scripts. Use when asked to build or test an Xcode project, launch an app on a simulator, tap/type/swipe through its UI by accessibility label, take screenshots or visual diffs, audit accessibility, read logs or diagnose hangs, or manage simulators (boot, erase, permissions, location, push, appearance). macOS only; needs Xcode, Python 3.12+ and idb.

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Use when auditing an AISTATS submission for OpenReview readiness, abstract/full-paper deadlines, the 8-page submission body, double-blind anonymity, supplementary material, reproducibility checklist, dual-submission policy, reviewer volunteer requirements, desk-reject triggers, and final-week submission sequencing.

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Use when strengthening AISTATS reproducibility evidence, including the official reproducibility checklist, statistical assumptions, proofs, datasets, hyperparameters, random seeds, compute, uncertainty estimates, baselines, code/data release statements, and checklist-to-claim consistency audits.

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Use when designing or auditing AISTATS experiments, simulations, baselines, statistical tests, uncertainty estimates, ablations, random seeds, hyperparameters, compute, dataset handling, and claim-to-evidence fit, with emphasis on experiments that validate theorems rather than chase leaderboards.

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Use when designing or auditing figures for an Advanced Materials manuscript, including the lead figure, multi-panel characterization figures, and the Table-of-Contents graphic, where visual quality is a house expectation. Designs figure strategy; does not run the analysis or write prose.

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Use when designing or auditing the experiments of an ACM MM (ACM Multimedia) paper — matched baselines per modality, ablations that isolate the cross-modal fusion, user studies or QoE measurement where the claim is subjective, dataset and media licensing, and honest compute reporting, so evidence supports a multimedia claim.

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Use when designing or auditing ACM CCS experiments, attack demonstrations, adaptive-attack defense evaluations, security measurements, baselines, overhead and cost reporting, ablations, and claim-to-evidence fit, with emphasis on evidence that survives an adversarial program committee rather than leaderboard wins.

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Use when building or auditing the related-work section of an ACM MM (ACM Multimedia) paper — covering the multimedia literature spread across vision, audio/speech, language, HCI/QoE, and systems, handling arXiv-speed concurrency, keeping citations double-blind, and verifying that cited "ACM MM papers" are ACM MM and not ICMR, MMSys, CVPR, or TOMM.

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Use when auditing an ACM CCS submission for HotCRP readiness, dual-cycle abstract registration and full-paper deadlines, the 12-page ACM sigconf body, anonymization, the ethics considerations appendix, artifact-availability posture, dual-submission policy, per-cycle submission caps, desk-reject triggers, and final-week sequencing.

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Use when auditing an ACM MM (ACM Multimedia) submission for OpenReview readiness — thematic-area choice, the 6-8 page ACM sigconf budget, references-only overflow, double-blind anonymity versus the single-blind Reproducibility/Open-Source/Dataset tracks, supplementary media, dual submission, desk-reject triggers, and last-week sequencing.

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Use when writing the contribution and implications section for a 《会计研究》 (Accounting Research) manuscript — articulating both the accounting-theory contribution (role of accounting information) and concrete implications for standard-setters, regulators, and audit/reporting practice. Use after results and tables are final.

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Use when building or repairing the literature review for 《会计研究》 (Accounting Research) — entering the accounting research lineage (earnings quality, conservatism, audit quality, disclosure, value relevance) rather than piling recent Chinese empirical citations, and positioning the gap as an accounting contribution.

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Use when analyzing the material of an Accounting, Organizations and Society (AOS) manuscript — coding and interpreting qualitative field data, estimating experimental and survey models, or running theory-laden archival analyses, with an audit trail appropriate to each tradition. Analyzes and reports; it does not design the study (aos-methods).

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Use when strengthening reproducibility evidence for an ACL paper reviewed through ACL Rolling Review, covering the Responsible NLP checklist end to end, hyperparameter and compute reporting, prompt and decoding disclosure for LLM experiments, data contamination auditing, variance across runs, and checklist-to-paper consistency.

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Use when designing or auditing experiments for an ACL paper, covering tuned LLM baselines, multi-dataset and multilingual evaluation, statistical significance and variance, human evaluation with agreement reporting, contamination and prompt-sensitivity controls, ablations, and error-analysis expectations in NLP reviewing.

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Use when the mechanism analysis for a 《会计研究》 (Accounting Research) manuscript is missing or mis-specified — the channel must be an information mechanism (reduce information asymmetry, raise disclosure quality, constrain earnings management, improve audit assurance), not a generic pricing or financing channel. Use after the main result holds.

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Use when auditing an ACL submission before an ACL Rolling Review cycle deadline, covering long/short paper page budgets, the mandatory Limitations section, Responsible NLP checklist, anonymized PDFs and supplements, preprint declarations, dual-submission rules, resubmission linking, and ARR desk-reject triggers on OpenReview.

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Use when judging whether a manuscript's landing point is an ACCOUNTING issue (accounting information / disclosure / audit / standards) versus a corporate-finance mechanism, before drafting for 《会计研究》 (Accounting Research). Use this to decide fit and to re-route finance-mechanism papers to a finance journal before any other acr-* skill.

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。