「human review」の検索結果

Product-launch command desk (Busabase App-in-Skill) built on the RAMP discipline — Research / Assemble / Mobilize / Prove. The agent assembles the launch checklist and drafts launch assets, channel submissions (Product Hunt / Hacker News / directories), press pitches, and the launch-day runbook; the human approves the launch-readiness gate and steers launch day in a review UI. Use when the user invokes $kelly-launch or /kelly-launch, or mentions launching / shipping a product, a launch checklist or runbook, launch readiness, RAMP, Product Hunt / Show HN / press-kit / launch-email drafting, or wants to review and approve launch assets and channel submissions before they go public. This is launch OPERATIONS — the checklist, assets, submissions, and runbook — not a launch video (for a promo video use product-launch-video).

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 signs of AI-generated writing from text. Use when editing or reviewing text to make it sound more natural and human-written. Based on Wikipedia's comprehensive "Signs of AI writing" guide. Detects and fixes patterns including: inflated symbolism, promotional language, superficial -ing analyses, vague attributions, em dash overuse, rule of three, AI vocabulary words, negative parallelisms, and excessive conjunctive phrases.

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。

Iterate on a PR until actionable CI passes and high/medium review feedback is addressed. Use for PR CI failures, review feedback, or green-check loops; do not wait for human approval, draft status, or merge gates.

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。

Use OpenAI Codex CLI as a peer collaboration partner — same standing as Claude, not a one-shot tool. Bring Codex in EARLY (ideation, framing decisions, stuck moments), not only for late peer-review. Hand it strategic context (target, research, draft, reasoning), iterate conversationally over ~3-8 turns, converge before surfacing to the human. Invoke when the user says "check / iterate / ideate with Codex" or "ask Codex" — and proactively when stuck between two reasonable framings, pressure-testing a load-bearing claim or structure, or about to ask the human to pick between options two agents could resolve.

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。

Use when the user wants a full feature action plan (backend + frontend + tests) with the PWDEVIA 7-question method before any code is written — 'planejar a feature X', 'criar plano de feature', 'plan the user CRUD'. Interviews the human (max 2 rounds) and writes .planning/feat/features/{slug}/plan.md. Do NOT use to write code (feat-exec), for small 1–3 file changes (feat-quick), backend-only or UI-only scope (feat-backend / feat-frontend), tests for existing code (feat-test) or code review (feat-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。

Review code changes against linked GitHub issue specs to catch implementation drift — building something plausible but wrong. Checks each acceptance criterion against actual code.

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 local diff or PR for unnecessary durable surface: new files, public types, interface methods, DTOs/view models, helpers, routes, DI registrations, services, repositories, and dependencies that should reuse existing owners instead.

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。

Three-axis orchestrator: (1) clean section boundaries — naming, routes, views, ViewModels, DB ownership, cross-section access; (2) internal cohesion — no EF in service layer, caching in service only, proper interfaces, reusable ViewComponents, architecture-test coverage; (3) focused tests — grouped under the section, covering invariants/negatives/triggers, pruned of redundancy. Push-and-bot-review loop. Use when a sizable PR landed, a section shows arch drift, or /pr-review returned a non-trivial violation list. Camps is the gold-standard reference.

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。

Run multiple Reforge-guided section-refactor lanes in parallel, each in its own worktree/branch off origin/main, with a score-blind adversarial review panel gating every commit, one draft PR per section. Use when the user wants to reduce Reforge surface/internal score across several sections at once via architecturally-correct deletions (dead surface, entity-leak removal, cross-section read-splits) — not by relocation, parameter bags, or accessibility-dodging. Wraps the per-lane process in .codex/skills/humans-refactor. Triggers: 'refactor swarm', 'parallel section refactors', 'run reforge refactor on Users/Tickets/...', 'burn down surface score across sections'. Has an intensity dial (light|standard|deep) and a workflow|solo execution mode to trade token burn against autonomy per unit of output.

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 signs of AI-generated writing from text. Use when editing or reviewing text to make it sound more natural and human-written. Based on Wikipedia's comprehensive "Signs of AI writing" guide. Detects and fixes patterns including: inflated symbolism, promotional language, superficial -ing analyses, vague attributions, em dash overuse, rule of three, AI vocabulary words, negative parallelisms, and excessive conjunctive phrases.

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 and verify unverified wiki pages — "review unverified pages", "verify wiki content", verification pass, after a major ingest cycle, before content release. The gate that turns AI-generated knowledge into trusted knowledge.

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。

Three-layer verification pipeline for AI output. Extracts verifiable claims, finds supporting or contradicting sources via web search, runs adversarial review for hallucination patterns, and produces a structured verification report with source links for 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。

Use when a systematic, scoping, or rapid review needs a reproducible pipeline: protocol framing (PICO/SPIDER/PCC), a logged search string, title/abstract and full-text screening with recorded exclusion reasons, and PRISMA flow counts, with the human as the deciding screener.

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 signs of AI-generated writing from text. Use when editing or reviewing text to make it sound more natural and human-written. Based on Wikipedia's comprehensive "Signs of AI writing" guide. Detects and fixes patterns including: inflated symbolism, promotional language, superficial -ing analyses, vague attributions, em dash overuse, rule of three, AI vocabulary words, negative parallelisms, and excessive conjunctive phrases.

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 and rate OpenClaw skills on ClawdTM. See what humans and AI agents recommend.

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 of Cognitive Systems Engineering applications and current practice in safety-critical domains

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。

Lazy senior dev mode: the smallest change that fully solves the task, and a reply a busy human understands in one read. Use on any coding task (writing, fixing, refactoring, reviewing, choosing dependencies) and when the user says "ponytail", "be lazy", "simplest solution", "yagni", or complains about over-engineering or bloat. Levels: lite, full (default), ultra.

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。

Guide a human review of a commit, PR, file, or directory. Use when invoked by name

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。

Use when a coding task should be driven end-to-end from issue intake through implementation, review, deployment, and acceptance verification with minimal human re-intervention.

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。

Use after generating code, after accepting AI suggestions, or when reviewing AI-written modules. Also use when code works but feels brittle, when error handling seems thin, when orphaned resources or missing cleanup are suspected, or when the agent claims done but hidden debt may exist. Catches the specific failure patterns AI agents produce that humans would not.

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。

Turns marketing prompts into tested, versioned production assets: A/B prompt evaluation against structured test cases, immutable prompt version history with diffs, ready-to-use marketing prompt templates (ad copy, email campaigns, social posts, landing pages, SEO meta), and an LLM-governance playbook for marketing teams (claim discipline, disclosure rules, human-review gates). Use when a marketing team relies on AI-generated content and needs prompt quality to be measurable and safe — or when the user mentions 'prompt engineering,' 'improve my prompts,' 'prompt templates,' 'prompt versioning,' 'AI content workflow,' or 'AI governance for marketing.'

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 writing patterns from prose so it reads like a person wrote it. Use when writing, editing, or reviewing user-facing text (blog posts, docs, marketing copy, PR descriptions), or when asked to "deslop", "de-AI", or "make it sound human".

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。

Fresh-session RFP and proposal-questionnaire response for {{projectName}}. Parses a new inbound RFP from {{rfp_label}} in Gmail and its attached document in Google Drive, matches each question to our vetted past-answers library and product docs, drafts the full response in a new Google Doc inside {{drafts_folder}}, flags anything it can't answer confidently, and leaves an unsent Gmail draft linking it — holding the completed response for a human to review and submit.

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。

Daily compliance-drift check for {{projectName}}. Reads AWS resource state and audit logs across {{aws_regions}}, compares against the compliance policy stored in skills and memory, files every finding, and drafts proposed remediation as a reviewed change — never applying a fix. Load this when the daily cron fires the sweep, or a human asks for a compliance, drift, or policy check.

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。