「line art」の検索結果

End-to-end plan → implement → review pipeline that takes one triaged Gaia issue to a merged-ready PR, with Kill Criteria on the issue and zero follow-up debt. Use when someone says: "resolve this issue", "take #N end-to-end", "work the P0 queue", "fix and ship this issue", "run the resolver", "close out this issue properly", or /gaia-issue-resolver. Works any gaia-research org repo — gaia-skill-tree, gaia-research, gaia-skill-heaven — while the issue itself lives on the headquarters tracker, gaia-research/gaia-skill-tree. Assumes the issue is already triaged and carries a P0–P4 label; if it does not, run /gaia-triage first.

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Phase 3 of the feature-pipeline suite. Use this after /fp-plan has received explicit user approval and printed the M2-plan stop hook — when you need to translate an approved numbered plan into committed, pushed code. Triggers on: "implement the plan", "start coding", "commit the plan", "write the code", "fp-implement", "Phase 3", "plan is approved let's code", "next step after plan", "execute the plan", "apply the plan", "make the changes". Reads planApproved and branch from state; refuses to run if approval hasn't been recorded. Commits one atomic change per plan step, pushes after each commit, handles obstacles cleanly, and hands off to /fp-review when done. Never squashes or skips steps — the one-commit-per-step rule keeps the review history readable and rollback surgical.

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Plan a feature before writing any code. Use this skill at the START of non-trivial feature work — when the user says they want to "add", "build", "implement", "create", "develop", or "ship" a feature, endpoint, component, page, service, or capability. Especially trigger when the user's prompt is short or under-specified ("add a dashboard", "build an auth flow", "let's do user activity reports") — those need planning, not direct execution. The skill walks through requirements clarification, codebase exploration, sketches a Mermaid architecture/flow diagram of the proposed design, and produces a plan.md document the user can hand off to a fresh AI session for execution. Skip only when the change is genuinely trivial (typo fixes, single-line tweaks, renames). When in doubt, plan.

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Prove a UI or generation feature actually works before asking a human to look at it. Covers A/B-ing an imported design against its source render, driving controls headlessly so a dead button cannot ship, proving the generator really spawns, and launching the real host (VCV Rack, a DAW). Use before any "take a look" on imported UI, plugin shells, or prompt-to-artifact pipelines.

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Before designing, training, or auditing ANY model that replicates human-annotated labels, audit the annotation protocol's INPUT — the exact document/evidence the human labelers consulted — and give the model that same input. Use when: (1) designing a classifier/LLM extractor whose target is a hand-coded label set, (2) a label-replication model shows low recall concentrated in a label subset and the diagnosis on offer is "the label's information is not in the features", (3) reviewers propose construct splits (e.g. "designation vs record-evident"), adjudication sittings, or per-domain stop rules to explain residual disagreement with gold, (4) validating an extraction pipeline against labels transcribed from a source document. Symptom of the underlying failure: elaborate theory accumulates to explain why gold is "partially unpredictable" when the model was simply never shown the document the annotators read.

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Orchestrate audio team: audio-director + sound-designer + technical-artist + gameplay-programmer for full audio pipeline from direction to implementation.

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Use when generating or editing diagrams, charts, visual assets, posters, UI mockups, product images, infographics, academic figures, comics, avatars, storyboards, brand boards, or image-edit workflows, especially when exported PNGs need visual QA and bounded self-repair. Prefer deterministic Mermaid/SVG/HTML/canvas sources for text-heavy diagrams; use HTML technical diagrams for browser-ready system blueprints, architecture, deployment topology, agent runtime, memory flow, before-after architecture, workflow, sequence, data-flow, lifecycle, runbook, PII/data-lineage, and state-machine diagrams. Do not use for ordinary UI polish without generated imagery.

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Sales enablement, technical presales and deal acceleration for B2B SaaS revenue teams. Use this skill when planning outbound campaigns, running discovery calls, building deal strategy, creating battle cards, writing proposals, responding to RFPs, preparing a product demo, scoping a proof of concept or pilot, answering a security questionnaire or the AI annex buyers now attach to it, executing sales sequences, coaching qualification, or deciding what the website's AI chat agent is allowed to say. Also triggers on: outbound, cold outreach, reply handling, reply classification, out-of-office replies, auto-replies counted as replies, our reply rate is inflated, prospect replied remove me, opt-out by reply, is this list ready to send, launch readiness, CRM segment outbound, we emailed a current customer, outbound hit an open opportunity, ownerless leads, prospects engaging our competitors, competitor connections, displacement list, is this personalization creepy, can we mention they visited our site, signal freshness, deal strategy, pipeline, battle card, objection handling, common objections, why do we keep hearing this objection, mine call transcripts for objections, objection register, proposal, RFP, discovery call, MEDDPICC, sales sequence, qualification, sales engineer, solutions engineer, presales, demo plan, demo script, POC, proof of concept, pilot success criteria, technical evaluation, technical win, security questionnaire, vendor security review, SOC 2 questionnaire, AI security questionnaire, AI annex, AI vendor due diligence, the buyer is asking if we train on their data, do you train on customer data, which model do you use, model provider sub-processor, AI subprocessor disclosure, where does inference run, can the customer turn the AI feature off, AI-CAIQ, CSA AI Controls Matrix, ISO 42001, ISO/IEC 42001, NIST AI RMF, EU AI Act Article 50, are we a provider or a deployer, AI feature disclosure, our model vendor changed and nobody told the buyer, technical objection, mutual action plan, MAP, close plan, mutual close plan, buyer enablement, the deal is stalled, deal stalled after the demo, how do I get this deal to close, close date keeps slipping, JOLT, buyer indecision, no-decision loss, website chatbot, chat widget, AI chat agent, AI SDR, inbound AI SDR, conversational marketing, live chat, what should our chatbot be allowed to say, our chatbot quoted the wrong price, the bot promised a discount, do we have to disclose it is AI, EU AI Act chatbot disclosure, chatbot handoff to sales, chat transcript review, prompt injection on our chatbot, Drift, Qualified, Intercom.

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Use when a task needs the judgment of a Clinical Nurse Specialist — diagnosing why a unit-level outcome metric (CLABSI, falls, pressure injury) is off benchmark, designing and scaling a practice-change pilot, appraising whether a new clinical guideline actually fits the local population, writing a quarterly outcomes report to nursing leadership, or coaching staff nurses through a new protocol without formal authority over them. US hospital/health-system default; state nurse practice acts vary. A reasoning aid, not clinical or legal advice.

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Use when a task needs the judgment of a Crane and Tower Operator — deciding whether current gust readings require a stand-down given a specific load's sail area, running a blind lift through a signal person when the pick or landing zone is outside the cab's sightline, reading a fixed installation's load chart at the counterweight/jib configuration actually installed, calculating each crane's load share on a two-crane tandem pick, or judging when to set the jib to free-slew ahead of a wind event.

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Use when a task needs the judgment of an Aircraft Cargo Handling Supervisor — verifying a built ULD against its type's certified max gross weight before it's spotted for loading, running a hazmat item through the segregation table against its actual compartment neighbors, reconciling an actual cargo build against the dispatcher's load plan and deciding whether it clears a last-minute-change threshold or needs a reissued loadsheet, calling a ULD's lock/net restraint good or rejecting it, or running ramp FOD and ground-equipment clearance discipline around an aircraft during turn.

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统一写作流水线。任何文档/文章/报告均走此流程：研究→草稿→逻辑自检→真实性校对→画图规划→批量画图→图片自审→审稿→格式转换（HTML/LaTeX/MD）。可从任意阶段入场（已有草稿直接审核，已审稿直接转格式）。触发词：「写一篇/帮我写/写一份/我有初稿了/帮我检查逻辑/帮我配图/从画图开始/从[Stage名称]开始继续/转成HTML/转成LaTeX」。注意：「只要审稿」请直接触发 article-proofreading；「帮我审稿」不触发本 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。

Guideline for designing, implementing, and verifying secure APIs following OWASP API Security Top 10 (2023) best practices. Use when the user wants to: (1) review API code or design for security vulnerabilities, (2) design a secure REST, GraphQL, or gRPC API architecture, (3) implement API authentication and authorization (OAuth2, JWT, API keys, mTLS), (4) configure rate limiting, input validation, or CORS, (5) audit API endpoints for BOLA, BFLA, or mass assignment vulnerabilities, (6) create API security checklists or verification plans, (7) fix API security bugs or harden existing APIs, (8) set up API security testing (OWASP ZAP, Schemathesis, Burp Suite), or (9) handle any API security concern including SSRF prevention, resource consumption limits, business flow protection, API inventory management, and secure third-party API consumption.

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Comprehensive guide for building, configuring, customizing, and deploying Docsify documentation sites. Use when the user wants to (1) initialize a new Docsify site, (2) add or organize Markdown pages, sidebars, navbars, or cover pages, (3) configure `window.$docsify` options, (4) customize themes / CSS variables / fonts, (5) install built-in or third-party Docsify plugins (search, GA, emoji, zoom, copy-code, comments, pagination, tabs, etc.), (6) write a custom Docsify plugin using lifecycle hooks, (7) use Docsify Markdown helpers (callouts, link attributes, image attributes, heading IDs, task lists, embed files with `:include`), (8) deploy to GitHub Pages, GitLab Pages, Netlify, Vercel, Firebase, Docker, Nginx, etc., (9) enable PWA / offline mode, virtual routes, or Vue compatibility, or (10) upgrade a Docsify site from v4 to v5. Triggers on mentions of "docsify", "_sidebar.md", "_navbar.md", "_coverpage.md", "$docsify", or `docsify-cli`.

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Use this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like "the xlsx in my downloads") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.

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Use when deploying or managing Kubernetes workloads. Invoke to create deployment manifests, configure pod security policies, set up service accounts, define network isolation rules, debug pod crashes, analyze resource limits, inspect container logs, or right-size workloads. Use for Helm charts, RBAC policies, NetworkPolicies, storage configuration, performance optimization, GitOps pipelines, and multi-cluster management.

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Build e-commerce storefronts: product catalog, cart, checkout, payments, and post-purchase flows. Use for online stores and marketplaces.

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Set up mobile CI/CD pipelines for Flutter, React Native, or native iOS/Android — GitHub Actions workflows for testing, code signing, TestFlight/Play Store distribution, build number management, Fastlane integration, and artifact retention

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Audit laboratory automation systems -- LIMS architecture, instrument connectivity (SiLA 2, OPC-UA, serial drivers), sample tracking and chain of custody, protocol workflow engines, data acquisition pipelines, and regulatory compliance (21 CFR Part 11 electronic records/signatures.

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Analyze manufacturing defect detection and quality control systems — computer vision inspection pipelines, SPC control charts, Six Sigma process capability (Cp/Cpk), defect classification taxonomies, root cause analysis tooling, and measurement system analysis.

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Audit ML experiment tracking infrastructure for reproducibility gaps, parameter logging completeness, metric capture, artifact management, and pipeline orchestration. Covers MLflow, Weights and Biases, DVC, Sacred, Neptune, Hydra configs, model registries, and produces a reproducibility scorecard (0-30) with actionable fixes for data science teams.

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Analyze disaster prediction and early warning systems — model accuracy for flood, earthquake, wildfire, hurricane, and tsunami hazards, data pipeline reliability from sensor networks and satellite feeds, alert distribution latency and channel coverage.

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Plan a marketing campaign end-to-end using Allan Dib's Before/During/After framework (The 1-Page Marketing Plan). Produces a phase-specific campaign brief with goal, audience, message, channels, assets, timeline, budget, and success metrics. Use when the user asks to plan a campaign, brief a campaign, "we need a campaign for X", launch plan, GTM plan, marketing plan for <product>, demand gen plan, or any multi-channel marketing initiative. Reads ICP, services, and KPIs from knowledge/. For a full quarterly strategy rather than one campaign, see marketing-plan scope in positioning-doc and kpi-review. For the content schedule inside the campaign, see social-calendar. For the post-campaign review, see retro.

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Write paid ad copy for LinkedIn, Google, Meta, and YouTube. Produces multiple variants per platform, each targeting a different Eugene Schwartz awareness level and Cialdini persuasion principle. Use when the user asks for ad copy, ad variants, "write LinkedIn ads", Google ads, Meta ads, paid social copy, ad headlines, or wants creative for a paid campaign. Reads brand voice, ICP, and positioning from knowledge/. For organic social posts, see linkedin-post or social-calendar. For the landing page the ad points at, see landing-page-writer. For testing variants, see ab-copy-writer.

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。