「line art」の検索結果

Create polished, validated architecture, workflow, sequence, data-flow, and lifecycle/state diagrams as explorable standalone HTML with inline SVG, dark/light themes, optional trace motion, and PNG/JPEG/WebP/SVG/WebM export. Accept plain-language requirements or pasted Mermaid flowchart, sequenceDiagram, and stateDiagram input; inspect repository evidence when the diagram must reflect real code. Use when the user asks to visualize system architecture, infrastructure, cloud/security/network topology, technical workflows, API call sequences, request lifecycles, data pipelines, ETL/ELT, data lineage, state machines, or to convert/beautify Mermaid. Also use for Archify, interactive or animated HTML diagrams, インタラクティブ図, 動く構成図. When an editable .drawio file is required, use the draw.io workflow instead and treat Archify HTML only as a supplement.

Audit and repair Gaia user-tree timelines so every skill's current rank is explained by a chain of Hero's Journey events. Trigger this skill when: a contributor's profile chart shows a stale rank (e.g. still shows 3★ after a demotion to 1★); someone asks to "trace what happened" to a skill's rank; `validate_timelines.py` is failing in CI; you need to backfill missing `demote` or `rank_up` events before cutting a release; a skill was reclassified or Star-Bar-reset and the user tree was never updated; or any time the registry node and user tree disagree on a skill's current level. Uses `scripts/trace_timeline.py` (dry-run + apply) and `scripts/validate_timelines.py` (CI gate). Do NOT hand-edit `timeline[]` arrays — the script synthesises properly labelled events with `previousValue`/`newValue` so the rank chart plots correctly.

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Marketing analytics and data-driven optimization for B2B SaaS campaigns. Use this skill when analyzing marketing performance, optimizing conversion rates, running A/B tests, conducting customer research, creating marketing dashboards, building attribution models, analyzing funnel metrics, or storytelling with data. Also triggers on: marketing ops, performance analytics, CRO, A/B testing, data storytelling, customer insights, tracking plan, event taxonomy, UTM taxonomy, GA4 audit, consent mode, instrumentation contract, sample size calculation, minimum detectable effect, test duration, not enough traffic to A/B test, statistical significance, Bayesian vs frequentist, sample ratio mismatch, experiment feasibility, martech stack, marketing tech stack audit, tool consolidation, build vs buy, vendor evaluation, shadow IT tools, ESP/CRM/platform migration, switching cost, cutover plan, suppression list migration, data portability, customer interviews, survey design, sampling frame, non-response bias, how many people should I interview, thematic saturation, voice of customer, jobs-to-be-done, buyer persona research, is this finding real, data enrichment, which enrichment provider should we buy, waterfall enrichment, contact data quality, match rate, our list data is bad, email verification, catch-all domain, accept-all, bounce rate from bad data, B2B data decay, how often should we re-verify contacts, ZoomInfo vs Apollo vs Clearbit evaluation, cost per usable record, where did this list come from, GDPR Article 14 notice, purchased list compliance, suppression screening, TAM list building, demand planning, marketing plan, how many leads do we need, what pipeline target should marketing carry, funnel model, top-down vs bottom-up plan, plan of record, annual marketing planning, quarterly planning, pipeline coverage we have to create, sales cycle lag, our leads cannot close this quarter, carry-in pipeline, scenario planning, conservative moderate aggressive forecast, stretch target, reforecast, re-forecast triggers, assumption register, capacity ceiling, addressable audience ceiling, marginal cost per lead, blended CPL is wrong, we missed plan and nobody knows why, AI policy for the marketing team, AI governance, AI acceptable use policy, which AI tools are we allowed to use, can I paste customer data into ChatGPT, shadow AI, AI use register, human review of AI output, when can we stop reviewing every AI draft, can the agent send on its own, the AI made a mistake now what, pull back AI autonomy, AI evals, our AI vendor changed the model, AI output went out wrong, prompt injection, can our AI read prospect replies safely, lethal trifecta, the AI leaked internal notes, turn off auto-generated ad assets, auto-applied recommendations, AI literacy training, EU AI Act Article 4, duplicate records in the CRM, dedupe before import, which value wins in a merge, CRM cleanup.

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Use this skill when designing, troubleshooting, or reviewing FSL Mobile worker workflows — job lifecycle management, offline data capture, customer signature collection, parts consumption, and geolocation-triggered status transitions. Trigger keywords: FSL mobile, field service mobile workflow, offline job lifecycle, technician mobile, service appointment status, briefcase priming, parts consumption mobile, signature capture FSL. NOT for installing, branding, or extending the mobile app itself — use admin/fsl-mobile-app-setup. NOT for offline priming limits and sync conflict resolution — use architect/fsl-offline-architecture.

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Produce a researched long-form article from a topic prompt via an orchestrated pipeline - research agent (first-person sources, working-definition gate), narrative-architecture outline, writer/cold-reviewer loop with an explicit ACCEPT/REVISE verdict contract, then a catalog-deslop pass with a regression gate. The orchestrator dispatches subagents only; the writer never judges its own draft. Use when the user says "article factory", "write an article about X", "run the article pipeline", or asks for a researched long-form piece produced end-to-end. For essays and micro posts in the user's own voice without a research stage, use the prose skill instead.

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Publishes HTML or Markdown as display.dev URLs with public, company, or private access. Supports signup, claiming, sharing, browsing, bounded reads, exact edits, and comment iteration. Use when the user asks to "publish this", "share this", "share with the org", "post this online", "make a private link", "share with [email]", "publish a report", "list my artifacts", "find an artifact", "publish a large file", "publish this generated HTML file", "read this artifact", "search inside this artifact", "change this passage", "make a copy of this artifact", "duplicate version", "create a display.dev account", "claim this publish", "add a company email domain", "transfer an email domain", "change the email domains add-on", "show my referral link", "show referral rewards", "watch comments on this artifact", "respond to comments", or "resolve this comment thread". Use whenever a request contains a display.dev artifact URL (`*.dsp.so/*`). Anonymous publishing returns a 30-day preview and browser claim URL.

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Create branded architecture, architecture delta, IT current-state, flowchart, sequence, state machine, ER/data model, timeline, swimlane, quadrant, radar/spider, polar chart (polar/radial lollipop), loop/flywheel, nested, tree, org chart, layer stack, exploded axonometric, axonometric plan, Venn, pyramid/funnel, treemap and marimekko, heatmap, bar and dumbbell, waterfall, line (slopegraph, ridgeline, streamgraph, bump), Gantt and scatter charts (bubble, beeswarm), high-level, process, medallion, data flow, DP integration, DP security matrix, Sankey, fishbone, Wardley map, kanban, user journey, service blueprint, deployment, dependency graph, UML class, story map, or database schema diagrams as HTML/SVG/PNG, with .drawio, Mermaid, and .excalidraw import, plus lifecycle phase maps, block decomposition trees, and onboarding guidance.

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EU AI Act (Regulation (EU) 2024/1689) operational compliance for compliance teams. Three Article-level decisions: (1) What's the risk tier of this AI system — prohibited (Art. 5), high-risk (Art. 6 + Annex III), limited-risk (Art. 50), or minimal-risk? (2) For high-risk systems, what's the Article 43 conformity assessment route (Module A internal control vs Module H full QMS + notified body) and what goes in the Annex IV technical documentation? (3) Per organizational role (provider / deployer / importer / distributor / authorized representative), what are the active obligations and deadlines? Use during AI system intake review, when planning conformity assessment, or when scoping deployer obligations. Cites Articles + Annexes for every output. NOT executive AI strategy (see chief-ai-officer-advisor). NOT a legal substitute.

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Use when reviewing or rebalancing direct vs. partner-led channel economics — computing fully-loaded cost-to-serve per channel, channel ROI with cash / LTV / marginal lenses, and optimal channel mix subject to constraints. For Head of Commercial, RevOps, and VP Sales doing quarterly channel review when pipeline is mixed (e.g., 60% direct + 40% partner-led) and nobody actually knows which channel makes money after CAC, support load, partner discount, deal-velocity differences, retention differential, and overhead allocation are all loaded in. Outputs cost to serve, channel ROI verdicts (DOUBLE-DOWN / MAINTAIN / DEFUND / EXIT), a sensitivity-tested channel-mix recommendation, and the diminishing-returns inflection (e.g., 'which channel actually makes money — direct or partner?').

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Comprehensive CI/CD pipeline exploitation methodology covering GitHub Actions injection vectors (expression injection via PR titles and issue bodies, workflow_run event abuse, GITHUB_TOKEN over-scoping, composite action supply chain compromise), Jenkins attack paths (Groovy sandbox escapes, script console remote code execution, Java remoting deserialization, credential store dumping, shared library injection), GitLab CI exploitation (YAML anchor injection, runner registration token abuse, CI variable extraction, protected branch bypass via merge request pipelines), and Azure DevOps pipeline agent compromise with service connection theft. Includes artifact poisoning techniques across all platforms, tooling guidance for gato and jenkins-attack-framework, and maps to MITRE ATT&CK T1195.002 (Supply Chain Compromise: Compromise Software Supply Chain). Covers enumeration of pipeline configurations, privilege escalation from contributor to code execution, lateral movement through pipeline trust boundaries, and persistence via modified workflow definitions. Each technique section provides working exploitation code, detection indicators, and defensive countermeasures.

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Draws original design-and-coding scenes as SVG cards (480x360) in a framed-panel cartoon style. Identifying traits are a soft pastel card with a periwinkle-violet panel framed in thin navy, outline-only clouds and a pale lavender floor band with dashes inside it; L-shaped corner brackets outside two corners; white speech bubbles, UI cards and bushes that break out over the frame edge; and a semi-real character with a thin navy outline (1.25 px), muted flat fills, navy hair with violet highlights, blush and short paired hatch marks. Covers people working with laptops, screens, UI parts and devices. Use when someone asks for a framed panel, window-frame or picture-frame illustration, pastel violet cartoon, outlined flat character scene, empty-state, onboarding or 404 art, a blog header, a feature spot, a support, about or team page illustration, or a marketing card with a person.

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Draws flat vector illustrations as SVG cards (480x360): slim, long-limbed people acting out one design or coding moment with one hero prop. Identified by no outlines on big shapes, thin white fold strokes as the only shading, navy only for hair, sneakers and a few hairlines, small heads (about 1:7) with big hands and white sneakers, and sparse floating confetti with no ground line, in lavender, indigo, sunshine yellow, coral and mint. Covers the measured palette, stroke numbers, character construction (tapered limbs, separated fingers, thumb side), poses, props, composition and a tested parts kit. Use when asked for flat illustrations, flat vector people, outline-free or no-outline characters, modern or corporate flat art, empty states, onboarding screens, 404 or error pages, coming-soon pages, blog headers, feature spots or marketing cards.

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Classical end-to-end empirical analysis workflow in the traditional Stata ecosystem — native Stata + reghdfe + ivreg2 + csdid + did_imputation + eventstudyinteract + sdid + rdrobust + rddensity + synth + synth_runner + psmatch2 + teffects + ebalance + coefplot + esttab + asdoc + binscatter. **Defaults to economics empirical-paper style** (AER / QJE / AEJ) — every run produces a publication-ready output set with a multi-column regression table (M1→M6 progressive controls/FE) as the centerpiece, plus Table 1 (descriptives), mechanism / heterogeneity / robustness tables, and event-study + coefficient + trend figures. Covers the full 8-step Stata pipeline an applied economist runs on every paper — (1) data import & cleaning (use/import, destring, misstable, duplicates, merge assert), (2) variable construction (gen/egen/winsor2/xtile/xtset with L./F./D.), (3) descriptive statistics & Table 1 (tabstat/balancetable/asdoc), (4) classical diagnostic tests (sktest/swilk/hettest/imtest/xtserial/xttest3/vif/dfuller/kpss/hausman/estat overid), (5) baseline modeling (reg/xtreg/reghdfe/ivreg2/ivregress/csdid/did_imputation/eventstudyinteract/sdid/rdrobust/synth/psmatch2/teffects/heckman/qreg/ppmlhdfe), (6) robustness battery (bacondecomp/honestdid/rwolf/ritest/wildbootstrap/oster), (7) further analysis (subgroup/triple-diff/interactions/medsem/marginsplot/binscatter by group), (8) publication-ready tables & figures (esttab/outreg2/estout/coefplot/marginsplot/rdplot/twoway combined). **Also covers two parallel domain modes that share the same 8-step scaffolding** — **Mode A — Epidemiology / public health** (target-trial emulation, IPTW + g-formula + TMLE doubly-robust triplet via `teffects ipw` / `teffects ipwra` / `teffects aipw` / `eltmle`, Mendelian randomization via `mrrobust` (IVW / Egger / weighted median) and `mregger` / `mrpresso`, KM / Cox / AFT / RMST survival via `sts` / `stcox` / `streg` / `strmst2`, E-value sensitivity via `evalue` (Linden-Mathur), principal stratification — STROBE / TRIPOD reporting), and **Mode B — ML causal inference** (DML via `ddml` / `pdslasso`, S/T/X/R/DR meta-learners via `crforest` and `ddml interactive`, causal forest via `crforest` / `cforest`, BART/BCF via `bart` / `bartCause`-style externals, CATE distribution + policy tree via `crforest`, off-policy evaluation, conformal causal externals, fairness audit, DAG learning via `pcalg` / external Python callouts). Use when the user asks for a complete Stata empirical analysis, wants a reproducible .do-file pipeline, needs a Stata counterpart to the Python StatsPAI / Full-empirical-analysis-skill, or names a specific Stata step in isolation ("run reghdfe with two-way clustering", "csdid event study", "winsor2 at 1%", "esttab to LaTeX", "coefplot with CI", "ivreg2 weak-IV test", "synth_runner placebos", "teffects psmatch balance check"). Mode A triggers on "target trial emulation Stata", "teffects ipw aipw", "eltmle", "mrrobust", "mregger weighted median", "stcox AFT survival", "strmst2", "evalue Stata", "STROBE Stata", "公共健康 Stata", "流行病学 Stata". Mode B triggers on "ddml Stata", "pdslasso", "crforest causal forest Stata", "policy tree Stata", "因果机器学习 Stata".

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Use this skill when OpenStoryline is already installed and the user wants to start the local MCP/Web services, create or continue a session, send editing instructions, perform multi-turn re-editing, and verify rendered video outputs, as well as Chinese requests like “启动 OpenStoryline”, “把 OpenStoryline 跑起来”, “用 OpenStoryline 剪视频”.

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Add a chart (line, bar, area, pie, or scatter) to a dataset's showcase in a PortalJS portal. Installs recharts, writes a reusable Chart component, and renders it in the showcase Views section. Use when visualizing a dataset already registered in datasets.json.

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Turn numbers into a chart — bar, line, area, pie, or doughnut. Use when asked to chart or graph data, visualize metrics/trends/breakdowns, or show numbers as a picture instead of a table. Produces a ready-to-render chart spec (renders live in the playground and exports as PNG) plus a one-line read of what the chart shows.

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Search LCSC Electronics for electronic components — find parts by LCSC number (Cxxxxx) or MPN, check stock/pricing, download datasheets, analyze specifications. Sister company to JLCPCB, same parts library. Sync and maintain a local datasheets directory for a KiCad project, or use batch MPN-list seeding (`--mpn-list`) for bulk workflows without a project. No API key needed — uses the free jlcsearch community API. Use this skill when the user mentions LCSC, JLCPCB parts library, JLCPCB assembly parts, production sourcing, Cxxxxx part numbers, needs to find LCSC equivalents for parts, is preparing a BOM for JLCPCB assembly, or wants to download datasheets and LCSC is available. For package cross-reference tables and BOM workflow, see the `bom` skill.

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EU AI Act (Regulation (EU) 2024/1689) compliance advisor — risk classification across all four tiers, all 9 prohibited practices (Art. 5, including the nudification/CSAM prohibition from Dec 2, 2026), all 8 Annex III high-risk use case areas, provider and deployer obligations (Arts. 9–17, 26), GPAI model obligations including the July 2025 Code of Practice (Arts. 51–55), conformity assessment and CE marking (Arts. 43–48), EU AI database registration, Art. 50 transparency (chatbots, synthetic media, AI-generated content), governance (AI Office, AI Board), penalties (Art. 99), confirmed phase-in timeline (Digital Omnibus, Reg. (EU) 2026/1744, in force July 27, 2026: Annex III deferred to Dec 2, 2027; Annex I to Aug 2, 2028), and cross-framework mapping to ISO 42001, NIST AI RMF, and GDPR. Use for any EU AI regulation, AI system classification, or AI compliance question. Current as of August 2026. GPAI enforcement powers active since August 2, 2026.

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EU NIS2 Directive (Directive (EU) 2022/2555) compliance advisor for essential and important entities: entity classification, Art. 21 risk management measures, Art. 23 incident reporting timelines (24h/72h/1 month), Art. 20 governance obligations, supply chain security (Art. 21(2)(d); coordinated risk assessments Art. 22), gap assessments, policy drafting, ISO 27001 alignment, and penalty exposure analysis. Also covers Commission Implementing Regulation (EU) 2024/2690, the technical/methodological sub-requirements for Art. 21(2) and the significant-incident thresholds binding on DNS/cloud/data-centre/MSP/MSSP/trust-service and other digital entities. Use for NIS2 readiness, transposition questions, ENISA technical implementation guidance, significant-incident thresholds, supervisory differences between essential and important entities, and cross-border coordination.

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EU NIS2 Directive (Directive (EU) 2022/2555) compliance advisor for essential and important entities: entity classification, Art. 21 risk management measures, Art. 23 incident reporting timelines (24h/72h/1 month), Art. 20 governance obligations, supply chain security (Art. 21(2)(d); coordinated risk assessments Art. 22), gap assessments, policy drafting, ISO 27001 alignment, and penalty exposure analysis. Also covers Commission Implementing Regulation (EU) 2024/2690, the technical/methodological sub-requirements for Art. 21(2) and the significant-incident thresholds binding on DNS/cloud/data-centre/MSP/MSSP/trust-service and other digital entities. Use for NIS2 readiness, transposition questions, ENISA technical implementation guidance, significant-incident thresholds, supervisory differences between essential and important entities, and cross-border coordination.

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GDPR Legitimate Interest Assessment (LIA) — Guided assessment for Art. 6(1)(f) GDPR using the EDPB three-step test. Based on EDPB Guidelines 1/2024, EDPB Opinion 28/2024 (AI models), the OSS Case Digest on Legitimate Interest (McIntyre 2026), CNIL AI guidance (June 2025), ICO guidance, and key CJEU case law. Use when: (1) user needs to assess whether legitimate interest is appropriate, (2) user asks about Art. 6(1)(f), "Interessenabwägung", "berechtigtes Interesse", "balancing test", or "LIA", (3) user wants to document a legitimate interest assessment, (4) user mentions the three-step test, (5) user evaluates necessity or proportionality under Art. 6(1)(f), (6) user discusses direct marketing, fraud prevention, IT security, employee monitoring, AI training, web scraping, or credit checks in context of legal basis, (7) user handles a right to object (Art. 21), (8) user asks about children's data, profiling, or cross-border data sharing under legitimate interest, (9) user compares legitimate interest against other legal bases. Do NOT use for: consent assessments (Art. 6(1)(a)), contract performance (Art. 6(1)(b)), DPIA workflows, or pure ePrivacy/cookie compliance questions — though the skill does address the ePrivacy/GDPR overlap where relevant.

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EU AI Act (Regulation (EU) 2024/1689) compliance advisor — risk classification across all four tiers, all 9 prohibited practices (Art. 5, including the nudification/CSAM prohibition from Dec 2, 2026), all 8 Annex III high-risk use case areas, provider and deployer obligations (Arts. 9–17, 26), GPAI model obligations including the July 2025 Code of Practice (Arts. 51–55), conformity assessment and CE marking (Arts. 43–48), EU AI database registration, Art. 50 transparency (chatbots, synthetic media, AI-generated content), governance (AI Office, AI Board), penalties (Art. 99), confirmed phase-in timeline (Digital Omnibus adopted June 29, 2026: Annex III deferred to Dec 2, 2027; Annex I to Aug 2, 2028), and cross-framework mapping to ISO 42001, NIST AI RMF, and GDPR. Use for any EU AI regulation, AI system classification, or AI compliance question. Current as of July 2026. GPAI enforcement powers activate August 2, 2026.

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Create branded architecture, architecture delta, IT current-state, flowchart, sequence, state machine, ER/data model, timeline, swimlane, quadrant, radar/spider, polar chart (polar/radial lollipop), loop/flywheel, nested, tree, org chart, layer stack, exploded axonometric, axonometric plan, Venn, pyramid/funnel, treemap and marimekko, heatmap, bar and dumbbell, waterfall, line (slopegraph, ridgeline, streamgraph, bump), Gantt and scatter charts (bubble, beeswarm), high-level, process, medallion, data flow, DP integration, DP security matrix, Sankey, fishbone, Wardley map, kanban, user journey, deployment, dependency graph, UML class, story map, or database schema diagrams as HTML/SVG/PNG, with .drawio, Mermaid, and .excalidraw import, plus lifecycle phase maps, block decomposition trees, and onboarding guidance.

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Conducts Data Protection Impact Assessments for biometric identification and authentication systems under GDPR Article 35 and Article 9 special category rules. Covers facial recognition, fingerprint, iris scanning, voice recognition, and behavioural biometrics. Applies EDPB Guidelines 3/2019, CNIL Reglement Type Biometrie, and ISO/IEC 24745 biometric template protection. Keywords: DPIA biometric, facial recognition, fingerprint, Art. 35, Art. 9, biometric template, special category.

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