{"slug":"openraiser-academic-plotting","name":"academic-plotting","description":"Generates publication-quality figures for ML papers from research context. Given a paper section or description, extracts system components and relationships to generate architecture diagrams via Gemini. Given experiment results or data, auto-selects chart type and generates data-driven figures via matplotlib/seaborn. Use when creating any figure for a conference paper.","long_description":"---\nname: academic-plotting\ndescription: Generates publication-quality figures for ML papers from research context. Given a paper section or description, extracts system components and relationships to generate architecture diagrams via Gemini. Given experiment results or data, auto-selects chart type and generates data-driven figures via matplotlib/seaborn. Use when creating any figure for a conference paper.\nversion: 1.0.0\nauthor: Orchestra Research\nlicense: MIT\ntags: [Academic Writing, Visualization, Matplotlib, Seaborn, Plotting, Figures, Diagrams, NeurIPS, ICML, ICLR, LaTeX]\ndependencies: [matplotlib>=3.8.0, seaborn>=0.13.0, numpy, google-genai>=1.0.0]\n---\n\n# Academic Plotting for ML Papers\n\nGenerate publication-quality figures for ML/AI conference papers. Two distinct workflows:\n\n1. **Diagram figures** (architecture, system design, workflows, pipelines) — AI image generation via Gemini\n2. **Data figures** (line charts, bar charts, scatter plots, heatmaps, ablations) — matplotlib/seaborn\n\n## When to Use Which Workflow\n\n| Figure Type | Tool | Why |\n|-------------|------|-----|\n| Architecture / system diagram | Gemini (Workflow 1) | Complex spatial layouts with boxes, arrows, labels |\n| Workflow / pipeline / lifecycle | Gemini (Workflow 1) | Multi-step processes with connections |\n| Bar chart, line plot, scatter | matplotlib (Workflow 2) | Precise numerical data, reproducible |\n| Heatmap, confusion matrix | matplotlib/seaborn (Workflow 2) | Structured grid data |\n| Ablation table as chart | matplotlib (Workflow 2) | Grouped bars or line comparisons |\n| Pie / donut chart | matplotlib (Workflow 2) | Proportional data (use sparingly in ML papers) |\n| Training curves | matplotlib (Workflow 2) | Loss/accuracy over steps/epochs |\n\n**Rule of thumb**: If the figure has numerical axes, use matplotlib. If the figure has boxes and arrows, use Gemini.\n\n---\n\n## Step 0: Context Analysis & Extraction\n\nThe user will typically provide one of these inputs — not a ready-made specification:\n\n| Input Type | Example | What to Extract |\n|-----------|---------|-----------------|\n| Full paper / section draft | \"Here's our method section...\" | System components, their relationships, data flow |\n| Description paragraph | \"Our system has three layers that...\" | Key entities, hierarchy, connections |\n| Raw results / data table | \"MMLU: 85.2, HumanEval: 72.1...\" | Metrics, methods, comparison structure |\n| CSV / JSON data | Experiment log files | Variables, trends, grouping dimensions |\n| Vague request | \"Make a figure for the overview\" | Read surrounding paper context to infer content |\n\n### Extraction Workflow\n\n**For diagrams** (research context → architecture figure):\n\n1. **Read the provided context** — paper section, abstract, or description paragraph\n2. **Identify visual entities** — What are the main components/modules/stages?\n   - Look for: nouns that represent system parts, named modules, layers, stages\n   - Count them: if >8 top-level entities, consider grouping into sections\n3. **Identify relationships** — How do components connect?\n   - Look for: verbs describing data flow (\"sends to\", \"queries\", \"feeds into\")\n   - Classify: data flow (solid arrow), control flow (gray), error path (dashed red)\n4. **Determine layout pattern**:\n   - Sequential pipeline → left-to-right flow\n   - Layered architecture → horizontal bands stacked vertically\n   - Hub-and-spoke → central node with radiating connections\n   - Hierarchical → top-down tree\n5. **Assign colors** — One accent color per logical group/layer\n6. **Write every label exactly** — Extract exact terminology from the paper text\n\n**For data charts** (results → figure):\n\n1. **Read the provided data** — table, paragraph with numbers, CSV, or JSON\n2. **Identify dimensions**:\n   - What is being compared? (methods, models, configurations) → categorical axis\n   - What is the metric? (accuracy, loss, latency, F1) → value axis\n   - Is there a time/step dimension? → line plot\n   - Are there multiple metrics? → multi-panel or grouped bars\n3. **Choose chart type** automatically using this priority:\n   - Has a step/time axis → **line plot**\n   - Comparing N methods on M benchmarks → **grouped bar chart**\n   - Single ranking → **horizontal bar** (leaderboard)\n   - Correlation between two continuous variables → **scatter plot**\n   - Square matrix of values → **heatmap**\n   - Proportional breakdown → **stacked bar** (avoid pie charts)\n4. **Determine figure sizing** — Single column vs full width based on data density\n5. **Highlight \"our method\"** — Identify which entry is the paper's contribution and give it a distinct color\n\n### Auto-Detection Examples\n\n**Context → Diagram**: \"Our system has a Planner, Executor, and Verifier. Planner sends plans to Executor, Executor returns results to Verifier, Verifier feeds back to Planner on failure.\"\n→ 3 entities, cycle layout, dashed feedback arrow → **Workflow 1 (Gemini)**\n\n**Data → Chart**: \"GPT-4: MMLU 86.4, HumanEval 67.0. Ours: 88.1, 71.2. Llama-3: 79.3, 62.1.\"\n→ 3 methods × 2 benchmarks → **Workflow 2 (grouped bar)**, highlight \"Ours\" in coral\n\n---\n\n## Workflow 1: Architecture & System Diagrams (AI Image Generation)\n\nUse Gemini 3 Pro Image Preview to generate diagrams. **Choose a visual style first** — this is the single biggest factor in whether the figure looks professional or generic.\n\n### Visual Styles\n\nPick one style per paper (all figures should be consistent):\n\n#### Style A: \"Sketch / 简笔画\" (Hand-Drawn)\n\nWarm, approachable, memorable. Ideal for overview figures and system introductions. Looks like a whiteboard sketch refined by a designer.\n\n```\nVISUAL STYLE — HAND-DRAWN SKETCH:\n- Slightly irregular, hand-drawn line quality — lines wobble gently, not perfectly straight\n- Rounded, soft shapes with visible pen strokes (like drawn with a thick felt-tip marker)\n- Warm off-white background (#FAFAF7), NOT pure white\n- Fill colors are soft watercolor-like washes: muted blue (#D6E4F0), soft peach (#F5DEB3),\n  light sage (#D4E6D4), pale lavender (#E6DFF0)\n- Borders are dark charcoal (#2C2C2C) with 2-3px line weight, slightly uneven\n- Arrows are hand-drawn with slight curves, ending in simple open arrowheads (not filled triangles)\n- Text uses a rounded sans-serif font (like Comic Neue or Architects Daughter feel)\n- Small doodle-style icons inside boxes: a tiny gear ⚙ for processing, a lightbulb 💡 for ideas,\n  a magnifying glass 🔍 for search — rendered as simple line drawings, NOT emoji\n- Overall feel: a carefully drawn whiteboard diagram, clean but with personality\n- NO clip art, NO stock icons, NO photorealistic elements\n```\n\n#### Style B: \"Modern Minimal\" (Clean & Bold)\n\nConfident, authoritative. Best for method figures where precision matters.\n\n```\nVISUAL STYLE — MODERN MINIMAL:\n- Ultra-clean geometric shapes with crisp edges\n- Bold color blocks as backgrounds for sections — NOT just accent bars, but full section fills\n  using desaturated tones: slate blue (#E8EDF2), warm sand (#F5F0E8), cool mint (#E8F2EE)\n- Component boxes have ROUNDED CORNERS (12px radius), NO visible border — they float on\n  the section background using subtle shadow (1px, 4px blur, rgba(0,0,0,0.06))\n- ONE accent color per section used sparingly on key elements: Deep blue (#2563EB),\n  Emerald (#059669), Amber (#D97706), Rose (#E11D48)\n- Arrows are thin (1.5px), dark gray (#6B7280), with small filled circle at source\n  and clean arrowhead at target — NOT thick colored arrows\n- Typography: Inter or system sans-serif, title 600 weight, body 400 weight\n- Labels INSIDE boxes, not beside them\n- Generous whitespace — at least 24px between elements\n- NO decorative elements, NO icons — let the structure speak\n```\n\n#### Style C: \"Illustrated Technical\" (Icon-Rich)\n\nEngaging, explanatory. Good for tutorial-style papers and figures that need to be self-explanatory.\n\n```\nVISUAL STYLE — ILLUSTRATED TECHNICAL:\n- Each major component has a small MEANINGFUL ICON drawn in a consistent line-art style\n  (single color, 2px stroke, ~24x24px): brain icon for reasoning, database cylinder for storage,\n  arrow-loop for iteration, network nodes for communication\n- Components sit inside soft rounded rectangles with a LEFT COLOR STRIP (4px wide)\n- Background is pure white, but each logical group has a very faint colored region behind it\n  (#F8FAFC for blue group, #FFF8F0 for orange group)\n- Connections use CURVED bezier paths (not straight lines), colored by SOURCE component\n- Key data flows are THICKER (3px) than secondary flows (1px, dashed)\n- Small annotation badges on arrows: \"×N\" for repeated operations, \"optional\" in italics\n- Title labels are ABOVE each section in small caps, letter-spaced\n- Overall: like a well-designed API documentation diagram\n```\n\n#### Style D: \"Accent Bar\" (Classic Academic)\n\nThe default academic style. Safe for any venue, works well in grayscale.\n\n```\nVISUAL STYLE — CLASSIC ACCENT BAR:\n- Horizontal section bands stacked vertically, pale gray (#F7F7F5) fill\n- Thick colored LEFT ACCENT BAR (8px) distinguishes each section\n- Content boxes: white fill, thin #DDD border, 4px rounded corners\n- Section palette: Blue #4A90D9, Teal #5BA58B, Amber #D4A252, Slate #7B8794\n- Sans-serif typography (Helvetica/Arial), bold titles, regular body\n- Colored arrows match their SOURCE section\n- Clean, flat, zero decoration\n```\n\n### Curated Color Palettes\n\n**\"Ocean Dusk\"** (professional, calming — default recommendation):\n`#264653` deep teal, `#2A9D8F` teal, `#E9C46A` gold, `#F4A261` sandy orange, `#E76F51` burnt coral\n\n**\"Ink & Wash\"** (for 简笔画 style):\n`#2C2C2C` charcoal ink, `#D6E4F0` washed blue, `#F5DEB3` washed wheat, `#D4E6D4` washed sage, `#E6DFF0` washed lavender\n\n**\"Nord\"** (for modern minimal):\n`#2E3440` polar night, `#5E81AC` frost blue, `#A3BE8C` aurora green, `#EBCB8B` aurora yellow, `#BF616A` aurora red\n\n**\"Okabe-Ito\"** (universal colorblind-safe, required for data charts):\n`#E69F00` orange, `#56B4E9` sky blue, `#009E73` green, `#F0E442` yellow, `#0072B2` blue, `#D55E00` vermillion, `#CC79A7` pink\n\n### Checklist\n\n- [ ] **Extract from context**: Read paper/description, identify entities and relationships\n- [ ] **Choose visual style** (A/B/C/D) — match the paper's tone and venue\n- [ ] **Choose color palette** — or use one consistent with existing paper figures\n- [ ] Obtain Gemini API key (`GEMINI_API_KEY` env var)\n- [ ] Write a detailed prompt: style block + layout + connections + constraints\n- [ ] Generate script at `figures/gen_fig_<name>.py`, run for 3 attempts\n- [ ] Review, select best, save as `figures/fig_<name>.png`\n\n### Prompt Structure (6 Sections)\n\nEvery Gemini prompt must include these sections in order:\n\n```\n1. FRAMING (5 lines): \"Create a [STYLE_NAME]-style technical diagram for a\n   [VENUE] paper. The diagram should feel [ADJECTIVES]...\"\n\n2. VISUAL STYLE (20-30 lines): Copy the full style block from above (A/B/C/D).\n   This is the most important section — it determines the entire visual character.\n\n3. COLOR PALETTE (10 lines): Exact hex codes for every color used.\n\n4. LAYOUT (50-150 lines): Every component, box, section — exact text, spatial\n   arrangement, and grouping. Be exhaustively specific.\n\n5. CONNECTIONS (30-80 lines): Every arrow individually — source, target, style,\n   label, routing direction.\n\n6. CONSTRAINTS (10 lines): What NOT to include. Adapt per style — e.g., sketch\n   style allows slight irregularity but still no clip art.\n```\n\n### Generation Script Template\n\n```python\n#!/usr/bin/env python3\n\"\"\"Generate [FIGURE_NAME] diagram using Gemini image generation.\"\"\"\nimport os, sys, time\nfrom google import genai\n\nAPI_KEY = os.environ.get(\"GEMINI_API_KEY\")\nif not API_KEY:\n    print(\"ERROR: Set GEMINI_API_KEY environment variable.\")\n    print(\"  Get a key at: https://aistudio.google.com/apikey\")\n    sys.exit(1)\n\nMODEL = \"gemini-3-pro-image-preview\"\nOUTPUT_DIR = os.path.dirname(os.path.abspath(__file__))\nclient = genai.Client(api_key=API_KEY)\n\nPROMPT = \"\"\"\n[PASTE YOUR 6-SECTION PROMPT HERE]\n\"\"\"\n\ndef generate_image(prompt_text, attempt","tagline":"Generates publication-quality figures for ML papers from research context. Given a paper section or description, extracts system components and relationships to generate architecture diagrams via Gemini. Given experiment results or data, auto-selects chart type and generates data","category":"research","tags":["academic-writing","visualization","matplotlib","seaborn","plotting","figures","diagrams","neurips","icml","iclr"],"author":"Orchestra Research","verified":false,"attribution":{"status":"registry_indexed","statusLabel":"Registry indexed","shortLabel":"REGISTRY INDEXED","sourceLabel":"github fast track","sourceDetail":"OpenRaiser/NanoResearch","creatorName":"Orchestra Research","creatorUrl":"https://github.com/OpenRaiser","sourceUrl":"https://github.com/OpenRaiser/NanoResearch/tree/main/skills/vendor-ai-research/academic-plotting","indexedBy":"OpenAgentSkill community index","claimUrl":"https://www.openagentskill.com/skills/openraiser-academic-plotting#claim-this-skill","claimCta":"Claim this skill","trustNote":"This listing was indexed from public sources and is not marked official until a maintainer claim is approved.","publicNote":"Attribution links to the public repository or creator profile. Creators can claim the listing to update ownership signals."},"stats":{"stars":1362,"forks":96,"verified_installs":0,"successful_runs":0,"total_outcomes":0,"rating":0,"review_count":0,"quality_score":49.04},"quality":{"score":82,"tier":"strong","label":"Strong","summary":"Solid option that is likely worth shortlisting for production workflows.","signals":[{"label":"GitHub stars","value":"1.4K","tone":"positive"},{"label":"Freshness","value":"13d ago","tone":"positive"},{"label":"Install ready","value":"Yes","tone":"positive"},{"label":"License","value":"MIT","tone":"neutral"}],"warnings":[]},"trust":{"version":"trust-score-v5","score":71,"base_score":79,"outcome_confidence":0,"tier":"review","label":"Sandbox only","summary":"Useful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.","recommendedAction":"Run only in a sandbox and compare close alternatives before using it for real work.","decision":{"install_policy":"human_review_before_install","auto_install_allowed":false,"human_review_required":true,"sandbox_first":true,"agent_action":"Compare alternatives before installing.","reasoning":["71/100 Trust Score v5","79/100 Trust Score v4 baseline","Needs more real agent outcomes before unattended install","Install path is available","Review before production"],"review_required_when":["The workspace contains production secrets, payments, private customer data, or irreversible actions.","The install command requests shell, network, credential, database, or broad filesystem access.","Outcome evidence is missing, recently failed, or required human review.","Production credentials, payments, or irreversible account changes without explicit human review","Sensitive private data before reviewing repository code, license, and permission surface","Automatic installation in a production workspace"]},"dimensions":[{"id":"github_adoption","label":"GitHub adoption","score":86,"weight":0.13,"status":"pass","detail":"1.4K GitHub stars"},{"id":"repo_activity","label":"Stars/forks activity","score":77,"weight":0.08,"status":"info","detail":"1.4K stars, 96 forks; 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Use when creating any figure for a conference paper.","category":"research","url":"https://www.openagentskill.com/skills/openraiser-academic-plotting","repository":"https://github.com/OpenRaiser/NanoResearch/tree/main/skills/vendor-ai-research/academic-plotting","github_repo":"OpenRaiser/NanoResearch"},"suited_tasks":["Research agents workflows","OpenAI Agents teams","teams that value GitHub adoption signals","Search sources","Extract claims","Synthesize findings","Load tabular data","Calculate trends"],"suited_agents":["Codex","Claude Code","Cursor","OpenAgentSkill CLI","OpenAI Agents","CLI"],"install":{"command":"npx skills add OpenRaiser/NanoResearch --skill academic-plotting","ready":true,"targets":[{"id":"openagentskill-cli","label":"CLI","kind":"command","value":"npx --yes https://github.com/Leon-Drq/openagentskill/releases/download/cli-v0.3.0/openagentskill-0.3.0.tgz add openraiser-academic-plotting"},{"id":"codex","label":"Codex","kind":"agent-prompt","value":"Install the \"academic-plotting\" agent skill from https://github.com/OpenRaiser/NanoResearch/tree/main/skills/vendor-ai-research/academic-plotting. Read its SKILL.md or equivalent instructions first, install only the files needed for this workspace, and summarize any required setup before using it. Skill purpose: Generates publication-quality figures for ML papers from research context. Given a paper section or description, extracts system components and relationships to generate architecture diagrams via Gemini. Given experiment results or data, auto-selects chart type and generates data-driven figures via matplotlib/seaborn. Use when creating any figure for a conference paper. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_<unique-id>\",\"skill_slug\":\"openraiser-academic-plotting\",\"task\":\"Install academic-plotting\",\"agent\":\"codex\",\"outcome\":\"success\",\"install_used\":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes."},{"id":"claude-code","label":"Claude Code","kind":"agent-prompt","value":"Add \"academic-plotting\" as a Claude Code skill from https://github.com/OpenRaiser/NanoResearch/tree/main/skills/vendor-ai-research/academic-plotting. Inspect the skill instructions, place the reusable skill files in the appropriate local skills location for this project, and report the activation steps. Skill purpose: Generates publication-quality figures for ML papers from research context. Given a paper section or description, extracts system components and relationships to generate architecture diagrams via Gemini. Given experiment results or data, auto-selects chart type and generates data-driven figures via matplotlib/seaborn. Use when creating any figure for a conference paper. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_<unique-id>\",\"skill_slug\":\"openraiser-academic-plotting\",\"task\":\"Install academic-plotting\",\"agent\":\"claude-code\",\"outcome\":\"success\",\"install_used\":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes."},{"id":"cursor","label":"Cursor","kind":"agent-prompt","value":"Turn \"academic-plotting\" from https://github.com/OpenRaiser/NanoResearch/tree/main/skills/vendor-ai-research/academic-plotting into a reusable Cursor project rule or agent instruction. Preserve the core workflow, adapt paths to this repo, and keep the rule scoped to tasks where it is relevant. Skill purpose: Generates publication-quality figures for ML papers from research context. Given a paper section or description, extracts system components and relationships to generate architecture diagrams via Gemini. Given experiment results or data, auto-selects chart type and generates data-driven figures via matplotlib/seaborn. Use when creating any figure for a conference paper. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_<unique-id>\",\"skill_slug\":\"openraiser-academic-plotting\",\"task\":\"Install academic-plotting\",\"agent\":\"cursor\",\"outcome\":\"success\",\"install_used\":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes."}],"handoff_url":"https://www.openagentskill.com/api/skills/openraiser-academic-plotting/install","manifest_url":"https://www.openagentskill.com/api/registry/manifest/openraiser-academic-plotting"},"trust":{"score":79,"label":"Strong shortlist","version":"trust-score-v4","install_policy":"human_review_before_install","evidence":{"stars":"1.4K GitHub stars","repoActivity":"1.4K stars, 96 forks","lastPushed":"13d since push","license":"MIT","repository":"https://github.com/OpenRaiser/NanoResearch/tree/main/skills/vendor-ai-research/academic-plotting","install":"npx skills add OpenRaiser/NanoResearch --skill academic-plotting","installSafety":"standard package or runtime install path","permissionSurface":"secrets or environment access, filesystem or document access","documentation":"Strong README/SKILL.md context","agentOutcomes":"No agent outcome data yet"},"outcome_evidence":{"total":0,"successes":0,"failures":0,"not_relevant":0,"success_rate":null,"recent_success_rate":null,"recent_failure_rate":null,"install_attempts":0,"install_success_rate":null,"risk_blocked":0,"setup_required":0,"avg_output_quality":null,"production_outcomes":0,"last_outcome_at":null,"label":"No agent outcome data yet"},"auto_install":{"allowed":false,"sandbox_required":true,"reason":"Human review or sandbox validation is required before automatic installation."},"best_for":["research","academic-writing","visualization","matplotlib","seaborn","plotting"],"known_risks":["Financial research output is not financial advice; require human review before any live investment decision.","Quality score needs review","Permission surface needs review: secrets or environment access, filesystem or document access","Dependency/runtime risk: credential or environment access, network or browser surface","Permission surface: secrets or environment access, filesystem or document access"]},"agent_proven":{"version":"agent-proven-v1","score":0,"tier":"unproven","label":"Needs first agent run","summary":"No agent outcome reports yet. 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require human review before any live investment decision","Financial research output is not financial advice; require human review before any live investment decision."],"agent_contract":{"task_input":"Use academic-plotting in an agent workflow","recommended_action":"Test manually in an isolated workspace and compare against safer alternatives.","install_policy":"review","minimum_review_before_use":["Trust: 79/100 Strong shortlist","Audit: 84/100 Needs review","Safety: 52/100 Avoid automatic install","Review repository, license, install command, and permission surface before production use."],"expected_agent_output":{"selected_skill":"openraiser-academic-plotting (academic-plotting)","install_command":"npx skills add OpenRaiser/NanoResearch --skill academic-plotting","risk_summary":"Needs review; Experimental; Review before production","verification_result":"Report the smallest successful task, files touched, warnings, and any missing setup."}},"outcome_feedback":{"endpoint":"https://www.openagentskill.com/api/agent/outcome","method":"POST","requires_resolve_event_id":true,"event_id_source":"Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.","expected_outcomes":["success","failed","not_relevant","blocked_by_risk","setup_required"],"payload_template":{"event_id":"<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>","skill_slug":"openraiser-academic-plotting","task":"Use academic-plotting in an agent workflow","agent":"codex","outcome":"success","install_used":true,"risk_blocked":false,"setup_required":false,"task_success":true,"output_quality":4,"error_type":null,"human_review_required":false,"workspace":"sandbox","time_to_useful_ms":120000,"notes":"Report the smallest successful task, setup friction, files touched, and risk notes."}},"endpoints":{"web":"https://www.openagentskill.com/skills/openraiser-academic-plotting","api":"https://www.openagentskill.com/api/agent/skills/openraiser-academic-plotting","audit":"https://www.openagentskill.com/skills/openraiser-academic-plotting/audit","eval":"https://www.openagentskill.com/api/agent/evals?slug=openraiser-academic-plotting&task=Use%20academic-plotting%20in%20an%20agent%20workflow&max_risk=medium","resolve":"https://www.openagentskill.com/api/agent/resolve?task=Use%20academic-plotting%20in%20an%20agent%20workflow&agent=codex&max_risk=medium","receipt":"https://www.openagentskill.com/api/agent/receipt?task=Use%20academic-plotting%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text","install":"https://www.openagentskill.com/api/skills/openraiser-academic-plotting/install","manifest":"https://www.openagentskill.com/api/registry/manifest/openraiser-academic-plotting"}},"machine_metadata":{"version":"openagentskill-agent-metadata-v2","skill":{"slug":"openraiser-academic-plotting","name":"academic-plotting","description":"Generates publication-quality figures for ML papers from research context. Given a paper section or description, extracts system components and relationships to generate architecture diagrams via Gemini. Given experiment results or data, auto-selects chart type and generates data-driven figures via matplotlib/seaborn. Use when creating any figure for a conference paper.","category":"research","url":"https://www.openagentskill.com/skills/openraiser-academic-plotting","repository":"https://github.com/OpenRaiser/NanoResearch/tree/main/skills/vendor-ai-research/academic-plotting","github_repo":"OpenRaiser/NanoResearch"},"suited_tasks":["Research agents workflows","OpenAI Agents teams","teams that value GitHub adoption signals","Search sources","Extract claims","Synthesize findings","Load tabular data","Calculate trends"],"suited_agents":["Codex","Claude Code","Cursor","OpenAgentSkill CLI","OpenAI Agents","CLI"],"install":{"command":"npx skills add OpenRaiser/NanoResearch --skill academic-plotting","ready":true,"targets":[{"id":"openagentskill-cli","label":"CLI","kind":"command","value":"npx --yes https://github.com/Leon-Drq/openagentskill/releases/download/cli-v0.3.0/openagentskill-0.3.0.tgz add openraiser-academic-plotting"},{"id":"codex","label":"Codex","kind":"agent-prompt","value":"Install the \"academic-plotting\" agent skill from https://github.com/OpenRaiser/NanoResearch/tree/main/skills/vendor-ai-research/academic-plotting. Read its SKILL.md or equivalent instructions first, install only the files needed for this workspace, and summarize any required setup before using it. Skill purpose: Generates publication-quality figures for ML papers from research context. Given a paper section or description, extracts system components and relationships to generate architecture diagrams via Gemini. Given experiment results or data, auto-selects chart type and generates data-driven figures via matplotlib/seaborn. Use when creating any figure for a conference paper. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_<unique-id>\",\"skill_slug\":\"openraiser-academic-plotting\",\"task\":\"Install academic-plotting\",\"agent\":\"codex\",\"outcome\":\"success\",\"install_used\":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes."},{"id":"claude-code","label":"Claude Code","kind":"agent-prompt","value":"Add \"academic-plotting\" as a Claude Code skill from https://github.com/OpenRaiser/NanoResearch/tree/main/skills/vendor-ai-research/academic-plotting. Inspect the skill instructions, place the reusable skill files in the appropriate local skills location for this project, and report the activation steps. Skill purpose: Generates publication-quality figures for ML papers from research context. Given a paper section or description, extracts system components and relationships to generate architecture diagrams via Gemini. Given experiment results or data, auto-selects chart type and generates data-driven figures via matplotlib/seaborn. Use when creating any figure for a conference paper. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_<unique-id>\",\"skill_slug\":\"openraiser-academic-plotting\",\"task\":\"Install academic-plotting\",\"agent\":\"claude-code\",\"outcome\":\"success\",\"install_used\":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes."},{"id":"cursor","label":"Cursor","kind":"agent-prompt","value":"Turn \"academic-plotting\" from https://github.com/OpenRaiser/NanoResearch/tree/main/skills/vendor-ai-research/academic-plotting into a reusable Cursor project rule or agent instruction. Preserve the core workflow, adapt paths to this repo, and keep the rule scoped to tasks where it is relevant. Skill purpose: Generates publication-quality figures for ML papers from research context. Given a paper section or description, extracts system components and relationships to generate architecture diagrams via Gemini. Given experiment results or data, auto-selects chart type and generates data-driven figures via matplotlib/seaborn. Use when creating any figure for a conference paper. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_<unique-id>\",\"skill_slug\":\"openraiser-academic-plotting\",\"task\":\"Install academic-plotting\",\"agent\":\"cursor\",\"outcome\":\"success\",\"install_used\":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes."}],"handoff_url":"https://www.openagentskill.com/api/skills/openraiser-academic-plotting/install","manifest_url":"https://www.openagentskill.com/api/registry/manifest/openraiser-academic-plotting"},"trust":{"score":79,"label":"Strong shortlist","version":"trust-score-v4","install_policy":"human_review_before_install","evidence":{"stars":"1.4K GitHub stars","repoActivity":"1.4K stars, 96 forks","lastPushed":"13d since push","license":"MIT","repository":"https://github.com/OpenRaiser/NanoResearch/tree/main/skills/vendor-ai-research/academic-plotting","install":"npx skills add OpenRaiser/NanoResearch --skill academic-plotting","installSafety":"standard package or runtime install path","permissionSurface":"secrets or environment access, filesystem or document access","documentation":"Strong README/SKILL.md context","agentOutcomes":"No agent outcome data yet"},"outcome_evidence":{"total":0,"successes":0,"failures":0,"not_relevant":0,"success_rate":null,"recent_success_rate":null,"recent_failure_rate":null,"install_attempts":0,"install_success_rate":null,"risk_blocked":0,"setup_required":0,"avg_output_quality":null,"production_outcomes":0,"last_outcome_at":null,"label":"No agent outcome data yet"},"auto_install":{"allowed":false,"sandbox_required":true,"reason":"Human review or sandbox validation is required before automatic installation."},"best_for":["research","academic-writing","visualization","matplotlib","seaborn","plotting"],"known_risks":["Financial research output is not financial advice; require human review before any live investment decision.","Quality score needs review","Permission surface needs review: secrets or environment access, filesystem or document access","Dependency/runtime risk: credential or environment access, network or browser surface","Permission surface: secrets or environment access, filesystem or document access"]},"agent_proven":{"version":"agent-proven-v1","score":0,"tier":"unproven","label":"Needs first agent run","summary":"No agent outcome reports yet. Use Resolve, run one narrow sandbox task, then report the result.","metrics":{"totalOutcomes":0,"successfulOutcomes":0,"failedOutcomes":0,"installAttempts":0,"installSuccessRate":null,"successRate":null,"recentSuccessRate":null,"recentFailureRate":null,"riskBlocked":0,"setupRequired":0,"notRelevant":0,"avgOutputQuality":null,"avgTimeToUsefulMs":null,"productionOutcomes":0,"humanReviewRequired":0,"uniqueAgents":0,"lastOutcomeAt":null},"signals":[],"penalties":["No real agent outcome evidence yet"]},"audit":{"score":84,"risk_level":"needs_review","risk_label":"Needs review","warnings":["Dependency or permission surface needs review","Permission surface may require sandboxing","Financial research output is not financial advice; require human review before any live investment decision","Financial research output is not financial advice; require human review before any live investment decision.","Quality score needs review","Permission surface needs review: secrets or environment access, filesystem or document access","Dependency/runtime risk: credential or environment access, network or browser surface","Permission surface: secrets or environment access, filesystem or document access"]},"safety_gate":{"tier":"experimental","label":"Experimental","auto_install_policy":"review","auto_install_allowed":false,"human_review_required":true,"blocked":false,"recommended_action":"Test manually in an isolated workspace and compare against safer alternatives."},"quality":{"score":82,"label":"Strong"},"supply":{"track":"Research and knowledge work","scenario":"Research agents","maintenance":"13d since push","risk":"Needs review"},"alternative_skills":[],"do_not_use_when":["teams that need a vendor-supported SLA","high-compliance environments without internal security review","No major risk signals from current metadata","High-risk permission hints: Secrets or environment access","Dependency or permission surface needs review","Permission surface may require sandboxing","Financial research output is not financial advice; require human review before any live investment decision","Financial research output is not financial advice; require human review before any live investment decision."],"agent_contract":{"task_input":"Use academic-plotting in an agent workflow","recommended_action":"Test manually in an isolated workspace and compare against safer alternatives.","install_policy":"review","minimum_review_before_use":["Trust: 79/100 Strong shortlist","Audit: 84/100 Needs review","Safety: 52/100 Avoid automatic install","Review repository, license, install command, and permission surface before production use."],"expected_agent_output":{"selected_skill":"openraiser-academic-plotting (academic-plotting)","install_command":"npx skills add OpenRaiser/NanoResearch --skill academic-plotting","risk_summary":"Needs review; Experimental; Review before production","verification_result":"Report the smallest successful task, files touched, warnings, and any missing setup."}},"outcome_feedback":{"endpoint":"https://www.openagentskill.com/api/agent/outcome","method":"POST","requires_resolve_event_id":true,"event_id_source":"Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.","expected_outcomes":["success","failed","not_relevant","blocked_by_risk","setup_required"],"payload_template":{"event_id":"<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>","skill_slug":"openraiser-academic-plotting","task":"Use academic-plotting in an agent workflow","agent":"codex","outcome":"success","install_used":true,"risk_blocked":false,"setup_required":false,"task_success":true,"output_quality":4,"error_type":null,"human_review_required":false,"workspace":"sandbox","time_to_useful_ms":120000,"notes":"Report the smallest successful task, setup friction, files touched, and risk notes."}},"endpoints":{"web":"https://www.openagentskill.com/skills/openraiser-academic-plotting","api":"https://www.openagentskill.com/api/agent/skills/openraiser-academic-plotting","audit":"https://www.openagentskill.com/skills/openraiser-academic-plotting/audit","eval":"https://www.openagentskill.com/api/agent/evals?slug=openraiser-academic-plotting&task=Use%20academic-plotting%20in%20an%20agent%20workflow&max_risk=medium","resolve":"https://www.openagentskill.com/api/agent/resolve?task=Use%20academic-plotting%20in%20an%20agent%20workflow&agent=codex&max_risk=medium","receipt":"https://www.openagentskill.com/api/agent/receipt?task=Use%20academic-plotting%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text","install":"https://www.openagentskill.com/api/skills/openraiser-academic-plotting/install","manifest":"https://www.openagentskill.com/api/registry/manifest/openraiser-academic-plotting"}},"supply_profile":{"track":{"slug":"research","label":"Research and knowledge work","shortLabel":"Research","description":"Deep research, source comparison, literature review, RAG, knowledge search, and reports."},"scenario":{"label":"Research agents","description":"I need my agent to research a topic, compare sources, and produce a concise report.","useCases":[{"slug":"research-agents","title":"Research agents"},{"slug":"data-analysis","title":"Data analysis"},{"slug":"rag-knowledge","title":"RAG and knowledge"}]},"applicableAgents":["OpenAI Agents","CLI","Codex","Claude Code","Cursor"],"install":{"ready":true,"command":"npx skills add OpenRaiser/NanoResearch --skill academic-plotting","primaryTarget":"CLI","targetCount":4},"githubQuality":{"stars":1362,"starsLabel":"1.4K","forks":96,"license":"MIT","qualityScore":82,"trustScore":79,"auditScore":84},"maintenance":{"status":"fresh","label":"13d since push","daysSincePush":13,"lastPushedAt":"2026-08-25T09:28:09+00:00"},"risk":{"level":"needs_review","label":"Needs review","requiresReview":true,"notes":["Dependency or permission surface needs review","Permission surface may require sandboxing","Financial research output is not financial advice; require human review before any live investment decision","Financial research output is not financial advice; require human review before any live investment decision.","Quality score needs review"]},"coverageTags":["Research","Research agents","academic-writing","visualization","matplotlib","seaborn","plotting","figures"]},"audit":{"audit_score":84,"risk_level":"needs_review","risk_label":"Needs review","quality_score":82,"trust_score":79,"maintenance_score":100,"security_score":80,"install_score":92,"warnings":["Dependency or permission surface needs review","Permission surface may require sandboxing","Financial research output is not financial advice; require human review before any live investment decision","Financial research output is not financial advice; require human review before any live investment decision.","Quality score needs review","Permission surface needs review: secrets or environment access, filesystem or document access","Dependency/runtime risk: credential or environment access, network or browser surface","Permission surface: secrets or environment access, filesystem or document access"]},"quality_signals":{"model":"v2","star_score":21.94,"usage_score":0,"review_score":5.1,"metadata_score":7,"freshness_score":15},"platforms":["OpenAI Agents"],"use_cases":[{"slug":"research-agents","title":"Research agents","url":"https://www.openagentskill.com/use-cases/research-agents"},{"slug":"data-analysis","title":"Data analysis","url":"https://www.openagentskill.com/use-cases/data-analysis"},{"slug":"rag-knowledge","title":"RAG and knowledge","url":"https://www.openagentskill.com/use-cases/rag-knowledge"},{"slug":"workflow-automation","title":"Workflow automation","url":"https://www.openagentskill.com/use-cases/workflow-automation"}],"stacks":[{"slug":"research-report-agent","title":"Research report agent","url":"https://www.openagentskill.com/collections/research-report-agent"},{"slug":"rag-knowledge-base","title":"RAG knowledge base","url":"https://www.openagentskill.com/collections/rag-knowledge-base"},{"slug":"content-growth-agent","title":"Content growth agent","url":"https://www.openagentskill.com/collections/content-growth-agent"}],"install":"npx skills add OpenRaiser/NanoResearch --skill academic-plotting","install_targets":[{"id":"openagentskill-cli","label":"CLI","title":"OpenAgentSkill CLI","kind":"command","value":"npx --yes https://github.com/Leon-Drq/openagentskill/releases/download/cli-v0.3.0/openagentskill-0.3.0.tgz add openraiser-academic-plotting","description":"Resolve policy, run the source installer safely, and report a verified install receipt.","copyLabel":"Copy command"},{"id":"codex","label":"Codex","title":"Codex install prompt","kind":"agent-prompt","value":"Install the \"academic-plotting\" agent skill from https://github.com/OpenRaiser/NanoResearch/tree/main/skills/vendor-ai-research/academic-plotting. Read its SKILL.md or equivalent instructions first, install only the files needed for this workspace, and summarize any required setup before using it. Skill purpose: Generates publication-quality figures for ML papers from research context. Given a paper section or description, extracts system components and relationships to generate architecture diagrams via Gemini. Given experiment results or data, auto-selects chart type and generates data-driven figures via matplotlib/seaborn. Use when creating any figure for a conference paper. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_<unique-id>\",\"skill_slug\":\"openraiser-academic-plotting\",\"task\":\"Install academic-plotting\",\"agent\":\"codex\",\"outcome\":\"success\",\"install_used\":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes.","description":"Give Codex a repo-aware install prompt when the skill is not available through a local CLI.","copyLabel":"Copy prompt"},{"id":"claude-code","label":"Claude Code","title":"Claude Code skill prompt","kind":"agent-prompt","value":"Add \"academic-plotting\" as a Claude Code skill from https://github.com/OpenRaiser/NanoResearch/tree/main/skills/vendor-ai-research/academic-plotting. Inspect the skill instructions, place the reusable skill files in the appropriate local skills location for this project, and report the activation steps. Skill purpose: Generates publication-quality figures for ML papers from research context. Given a paper section or description, extracts system components and relationships to generate architecture diagrams via Gemini. Given experiment results or data, auto-selects chart type and generates data-driven figures via matplotlib/seaborn. Use when creating any figure for a conference paper. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_<unique-id>\",\"skill_slug\":\"openraiser-academic-plotting\",\"task\":\"Install academic-plotting\",\"agent\":\"claude-code\",\"outcome\":\"success\",\"install_used\":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes.","description":"Use this prompt to ask Claude Code to add the skill and explain the local activation steps.","copyLabel":"Copy prompt"},{"id":"cursor","label":"Cursor","title":"Cursor rule prompt","kind":"agent-prompt","value":"Turn \"academic-plotting\" from https://github.com/OpenRaiser/NanoResearch/tree/main/skills/vendor-ai-research/academic-plotting into a reusable Cursor project rule or agent instruction. Preserve the core workflow, adapt paths to this repo, and keep the rule scoped to tasks where it is relevant. Skill purpose: Generates publication-quality figures for ML papers from research context. Given a paper section or description, extracts system components and relationships to generate architecture diagrams via Gemini. Given experiment results or data, auto-selects chart type and generates data-driven figures via matplotlib/seaborn. Use when creating any figure for a conference paper. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_<unique-id>\",\"skill_slug\":\"openraiser-academic-plotting\",\"task\":\"Install academic-plotting\",\"agent\":\"cursor\",\"outcome\":\"success\",\"install_used\":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes.","description":"Use this when installing as Cursor project rules or reusable agent instructions.","copyLabel":"Copy prompt"}],"repository":"https://github.com/OpenRaiser/NanoResearch/tree/main/skills/vendor-ai-research/academic-plotting","github_repo":"OpenRaiser/NanoResearch","version":"1.0.0","license":"MIT","urls":{"web":"https://www.openagentskill.com/skills/openraiser-academic-plotting","repository":"https://github.com/OpenRaiser/NanoResearch/tree/main/skills/vendor-ai-research/academic-plotting","api":"/api/agent/skills/openraiser-academic-plotting","install_api":"/api/skills/openraiser-academic-plotting/install"},"meta":{"created_at":"2026-09-02T07:02:48.786511+00:00","updated_at":"2026-09-02T07:02:48.86618+00:00","agent_friendly":true}}