「human review」の検索結果

Reviews one employee software request (tool, purpose, users, data handled, cost, urgency) against the approved-tools list and the policies the user provides and returns a draft review for the reviewer: match state on the list with the row quoted, approved tools the list itself describes as covering the same need, policy clauses that bear on the request with quoted text, questions for the requester and the reviewer, and one suggested decision with its basis. Never approves, procures, installs or adds a tool to the list. Use when the user asks to "review this software request", "is this tool on the approved list", "check this request against our software policy", "do we already have something approved that does this" or "prepare the decision note for this tool request". Do not use for a full vendor security assessment, use vendor-risk-screening-brief instead; for sorting mixed requests, use request-intake-triage. Drafts for human review; never approves, authorises or signs off.

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。

Reviews a proposal, business case, deck or rollout plan in character as a Works Council Representative archetype and returns a DRAFT review in the chat with a verdict, findings that cite the exact passage, employee-impact risks, what would change the verdict and five interrogation questions. Use when the user asks to "review this as the works council would", "what would the works council say about this rollout", "prepare me for the staff council consultation", "find the employee-impact gaps in this plan" or "pressure-test this deck from the employee representative side". Do not use for people strategy, restructuring or HR policy, use chro-reviewer instead; for whether frontline staff will adopt or work around the change, use frontline-skeptic-reviewer. Drafts for human review; never approves, authorises or signs off.

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。

Reviews a proposal, business case, deck, plan or restructuring paper in character as a Chief Human Resources Officer archetype and returns a DRAFT review with a verdict, findings that cite the exact passage, people and organisation risks, what would change the verdict and five interrogation questions. Use when the user asks to "run a CHRO review", "what would HR say about this", "pressure-test the people impact" or "prepare this for the people committee". Do not use for the employee representatives' own view of a plan, use works-council-reviewer instead. Drafts for human review; never approves, authorises or signs off.

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Reviews a proposal, business case, deck or plan in character as a Chief Business Officer archetype, covering commercial model, partnerships, deal structure, market positioning, strategic fit and opportunity cost, and returns a DRAFT review with a verdict, findings that cite the exact passage, commercial risks and five interrogation questions. Use when the user asks to "run a CBO review", "pressure-test this partnership", "check the deal structure" or "what would a commercial executive say about this". Do not use for pipeline, quota, sales-motion or forecast reviews, use cro-reviewer instead. Drafts for human review; never approves, authorises or signs off.

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。

Reviews a proposal, business case, deck or plan in character as a Chief Marketing Officer archetype and returns a DRAFT review in the chat with a verdict, findings cited to specific passages, marketing risks, what would change the verdict and five interrogation questions. Use when the user asks to "run a CMO review", "pressure-test the positioning", "what would a CMO say about this", "challenge the launch plan" or "check the messaging and audience". Do not use for price economics, margins or payback, use cfo-reviewer instead; for sales pipeline mechanics, use cro-reviewer. Drafts for human review; never approves, authorises or signs off.

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。

Reviews a proposal, business case, deck or plan in character as a Chief Information Security Officer archetype and returns a DRAFT review with a verdict, findings cited to specific passages, security and compliance risks and the five interrogation questions a real CISO would ask. Use when the user asks to "run a CISO review", "pressure-test the security of this plan", "what would our CISO say about this" or "check the privacy and third-party risk". Do not use for contract, liability or regulatory-interpretation reviews, use general-counsel-reviewer instead. Drafts for human review; never approves, authorises or signs off.

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。

Reviews a proposal, business case, deck or plan in character as a Chief Data Officer archetype and returns a DRAFT review in the chat with a verdict, findings that cite the exact passage, data and AI governance risks, what would change the verdict and five interrogation questions. Use when the user asks to "run a CDO review", "pressure-test the data governance in this plan", "what would a CDO say about this", "check the metric definitions and data sources" or "prepare this for the data governance board". Do not use for security controls or third-party security risk, use ciso-reviewer instead; for contract, liability or regulatory terms, use general-counsel-reviewer. Drafts for human review; never approves, authorises or signs off.

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。

Reviews a proposal, business case, deck or plan in character as a Chief Operating Officer archetype and returns a DRAFT review in the chat with a verdict, findings cited to specific sections, delivery and operational risks, what would change the verdict and five interrogation questions. Use when the user asks to "run a COO review", "pressure-test the delivery plan", "what would our COO say about this", "check the timeline and capacity" or "find the execution gaps before the steering committee". Do not use for financial questions, use cfo-reviewer instead; for architecture or vendor questions, use cto-reviewer. Drafts for human review; never approves, authorises or signs off.

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。

Apply MCAF human-review-planning guidance for a large AI-generated code drop by reading the target area, tracing the natural user and system flows, identifying the riskiest boundaries, and prioritizing the files a human should inspect first. Use when the codebase is too large to review line-by-line and you need a practical review sequence plus a prioritized file list.

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。

Remove AI-generated tone and make writing sound more human in Japanese and English. Use when reviewing drafts for AIっぽさ, humanizing article/blog text, 文体チェック, AI感を消したい, or ChatGPT tells.

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.

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。

Reviews a set of knowledge files or a knowledge base listing and returns a draft hygiene table, one row per file: duplication signals with the matching file, staleness signals, conflicts between files on the same topic with both passages quoted, missing or unverified owners, and one proposed action per file (Keep, Update, Merge into, Retire, Assign owner, Confirm with owner) for the owner to decide. Never edits, merges, moves or deletes a file. Use when the user asks to "review our knowledge base for duplicates", "which KB articles are stale", "find conflicting articles", "which articles have no owner" or "run a hygiene check on these files". Do not use for review-date sweeps of a document library with owner reminders, use sharepoint-review-sweeper instead; for writing one article, use knowledge-article-drafter. Drafts for human review; never approves, authorises or signs off.

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。

Checks whether AI-assisted work defines review criteria, accessible context, explicit review loop, human judgment boundary, and feedback capture before delegating to an agent.

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。

Detect review findings that belong in CI/lint/formatter rather than human review.

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。

Copilot left 14 review comments on your PR — half are nits. Hours of fix → reply → resolve → re-request, and each round lands MORE comments. This skill runs loop engineering: auto-triggers Copilot Code Review via GraphQL (no @copilot mention), triages every open thread (Copilot, humans, advanced-security) with a fix / decline / escalate rubric, dispatches parallel fix sub-agents that obey the repo build/test/lint conventions, commits per iteration, replies+resolves citing the pushed SHA, then re-triggers until HEAD is reviewed with zero threads awaiting the agent's reply (remaining open threads are explicit hand-offs to the human — escalated declines, design tradeoffs). You merge a clean PR; the bot runs it. Trigger phrases: "address copilot comments", "run a copilot review loop", "fix this PR", "iterate on copilot feedback". Repo-agnostic, gh CLI + PowerShell. Full autopilot needs repo Triage/Write; external PR authors get single-iteration mode plus manual re-trigger (UI 🔄 or substantive-commit push).

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。

Creates and manages annotation configs (categorical, continuous, freeform label schemas) and annotation queues (human review workflows) on Arize. Applies human annotations to project spans via the Python SDK. Use when the user mentions annotation config, annotation queue, label schema, human feedback, bulk annotate spans, update_annotations, labeling queue, annotate record, or human review.

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Systematic Review Pipeline Orchestrator - Coordinates systematic literature review automation Manages the complete 7-stage PRISMA 2020 pipeline from research question to RAG system Delegates to specialized agents (I1, I2, I3) while enforcing human checkpoints Use when: conducting systematic reviews, building knowledge repositories, PRISMA automation Triggers: systematic review, PRISMA, literature review automation

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Use when the user has a judge/grader and human-labeled data, and wants to measure how well the judge agrees with humans, detect systematic biases, determine whether automatic evaluation can replace human review, or build a human-reduction roadmap. Also use when the user mentions calibration, TPR/TNR, judge validation, inter-rater agreement, Cohen's kappa, bias detection, or "is my automatic evaluation trustworthy." Merges the calibrate and align functions into one 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。

Help with humanities scholarly writing: sharpen research questions, map supplied readings, plan and draft arguments, review chapters, preserve authorial voice, and respond to reviewers. Use for history, philosophy, literature, and related argumentative scholarship, including 论文、改论文、文献综述、审稿意见、我手写我口, or “review this paragraph” when an academic draft is in context. Not for unrelated copywriting or a standalone literature-search pipeline.

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。

Review a Robonix change against the repository's coding guidelines and write a Markdown review. Use when asked to review a branch, a PR, or files, and before asking a human to review your own change.

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。

This skill should be used when the user asks for a "sensitivity reader", "authenticity reader", "cultural review", "is this portrayal okay", "real people in my novel", "defamation", "can I use song lyrics", "epigraph permission", "permissions", "quote permission", "fair use", "AI disclosure", "do I need to disclose AI", "send to my editor", "editorial round", "Word file for my editor", "editor review copy", "co-author", "collaborate on a book", "shared world", "back up my book", "does this echo my source", "similarity check", "check overlap with my earlier books", or wants to run human editorial, ethics, permissions, or collaboration workflows around a story project. NOT for contracts or selling rights (use publishing), or reader feedback rounds and review copies for readers, including the GitHub review-copy setup (use feedback-triage).

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Implements data subject rights mechanisms for AI systems including right to explanation of AI decisions, contestation procedures, human review, model output correction, and training data access. Covers GDPR Arts. 15-22 and AI Act Art. 86. Keywords: data subject rights, AI explanation, contestation, human review, training data access, model correction.

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Use before an agent-produced diff is committed, pushed, opened as a PR, merged, landed, or applied to user files when explicit implementation approval is missing. Trigger for review gate, review pack, approve before landing, diff first then land, human approval, merge gate, commit gate, push gate, or PR readiness. Produces a concise review pack, records risks and verification, and blocks landing until a human explicitly approves or approves with required fixes.

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Use this skill when a human needs to review a Deepline Play result and hand feedback, labels, or approval back to the agent for revision, evaluation, comparison, or bounded iteration. Triggers on “put this run in a Sheet,” “review these results,” “read my feedback,” “make this a standing rule,” “never regress on this case,” “compare these runs,” “keep improving,” or “add the revised run to the same spreadsheet.” Skip ordinary Play authoring or execution with no human review loop, generic spreadsheet work, and one-off CSV export.

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。