[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6KFP7K7S3Q40MZPZT648KB7":3,"skill-same:01M6KFP7K7S3Q40MZPZT648KB7":61},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":22,"publishedAt":34,"linkOnly":35,"markdownContent":36,"markdownBody":37,"frontMatter":38,"refName":39,"commitSha":40,"skillDirectory":41,"forksCount":42,"repositoryHtmlUrl":43,"repositoryHomepageUrl":44,"ownerHtmlUrl":45,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":46,"install":47,"resources":60},"01M6KFP7K7S3Q40MZPZT648KB7","run2_matplotlib-technical-poster",null,"Complete guide to generating polished technical poster PNGs with matplotlib, including typography, pseudo-3D layers, and annotations.","cxcscmu\u002FSkillLearnBench","cxcscmu","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F142838272?v=4","01M6KCQDFQWPDSN0MKZY96MSQP","01M6KCQDM7G70TKJ28NTSQC0RZ",92,"MIT","https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench\u002Fblob\u002Fmain\u002Fskills\u002Fb2-self-feedback-claude-sonnet-4-6\u002Fanthropic-poster-design\u002Frun2_matplotlib-technical-poster\u002FSKILL.md",[17],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000015","视觉设计","SCENARIO","使用场景",[23,24,25,26,27,28,29,30,31,32,33],"matplotlib","technical","poster","run2 matplotlib technical poster","3D","annotations","guide","typography","complete","with","layers","2026-11-10 10:12:30",false,"---\nname: run2_matplotlib-technical-poster\ndescription: Complete guide to generating polished technical poster PNGs with matplotlib, including typography, pseudo-3D layers, and annotations.\n---\n\n# Matplotlib Technical Poster Generation (Refined)\n\n## Installation\n```bash\npip3 install matplotlib pillow --break-system-packages\n```\n\n## Setup (headless rendering — critical for servers)\n```python\nimport matplotlib\nmatplotlib.use('Agg')  # MUST be set before importing pyplot\nimport matplotlib.pyplot as plt\nimport matplotlib.patches as patches\nimport numpy as np\nimport json\n```\n\n## Figure Setup\n```python\nfig, ax = plt.subplots(figsize=(14, 20))   # portrait poster\nfig.patch.set_facecolor(BG_COLOR)\nax.set_facecolor(BG_COLOR)\nax.set_xlim(0, 14)\nax.set_ylim(0, 20)\nax.axis('off')\nplt.subplots_adjust(left=0, right=1, top=1, bottom=0)  # remove default margins\n```\n\n## Saving (high quality)\n```python\nplt.savefig('\u002Froot\u002Foutput.png', dpi=200, bbox_inches='tight',\n            facecolor=fig.get_facecolor(), edgecolor='none')\nplt.close()\n```\n- `dpi=200` → ~2800×4000px for 14×20in figure (better quality than dpi=150)\n\n## Known matplotlib Limitations\n| Feature | Issue | Workaround |\n|---|---|---|\n| `letter_spacing` | Not a Text property → AttributeError | Use spaced chars: `\"N O V A\"` |\n| `fontfamily='Inter'` | May not be installed | Use `'DejaVu Sans'` as fallback |\n| `alpha` on patches | Works fine | N\u002FA |\n| `linestyle='dotted'` | Use `linestyle=':'` or `(0, (1,3))` | N\u002FA |\n\n## Color Utilities\n```python\ndef hex_to_rgb(h):\n    h = h.lstrip('#')\n    return tuple(int(h[i:i+2], 16)\u002F255 for i in (0,2,4))\n\ndef lighten(h, factor=0.25):\n    r,g,b = hex_to_rgb(h)\n    return (r+(1-r)*factor, g+(1-g)*factor, b+(1-b)*factor)\n\ndef darken(h, factor=0.25):\n    r,g,b = hex_to_rgb(h)\n    return (r*(1-factor), g*(1-factor), b*(1-factor))\n```\n\n## Pseudo-3D Hardware Layer (three-face box)\n```python\ndef draw_layer(ax, x, y, w, h_front, depth, skew,\n               face_color, edge_color='#9B9B9B', z=3):\n    \"\"\"Front face + top face + right face = pseudo-3D box.\"\"\"\n    # Front face\n    front = patches.Rectangle((x, y), w, h_front,\n        facecolor=face_color, edgecolor=edge_color, linewidth=0.9, zorder=z)\n    ax.add_patch(front)\n    # Top face\n    tx = [x, x+w, x+w+skew, x+skew, x]\n    ty = [y+h_front]*2 + [y+h_front+depth]*2 + [y+h_front]\n    ax.fill(tx, ty, color=lighten(face_color, 0.28), zorder=z)\n    ax.plot(tx, ty, color=edge_color, linewidth=0.8, zorder=z+1)\n    # Right side face\n    rx = [x+w, x+w+skew, x+w+skew, x+w, x+w]\n    ry = [y, y+depth, y+h_front+depth, y+h_front, y]\n    ax.fill(rx, ry, color=darken(face_color, 0.22), zorder=z)\n    ax.plot(rx, ry, color=edge_color, linewidth=0.8, zorder=z+1)\n```\n\n## Leader Line Annotations\n```python\ndef add_leader(ax, tip_x, tip_y, label_x, label_y, label,\n               gray='#9B9B9B', text_color='#1A1816', font='DejaVu Sans'):\n    \"\"\"Diagonal leader + horizontal rule + label.\"\"\"\n    ax.plot([tip_x, label_x], [tip_y, label_y],\n            color=gray, linewidth=0.65, zorder=6)\n    ax.plot([label_x, label_x + 2.0], [label_y, label_y],\n            color=gray, linewidth=0.65, zorder=6)\n    ax.text(label_x + 2.1, label_y, label,\n            fontsize=8.5, va='center', color=text_color,\n            fontfamily=font, fontweight='light')\n```\n\n## Component Detail Overlays\n```python\n# PCB chip pads (small rectangles)\nfor cx in chip_x_positions:\n    chip = patches.Rectangle((cx-0.1, pcb_y-0.07), 0.2, 0.14,\n        facecolor=PRIMARY, edgecolor=GRAY, linewidth=0.4, zorder=7)\n    ax.add_patch(chip)\n\n# Thermal fins (vertical dashed lines within thermal layer)\nfor fx in np.arange(x_start, x_end, 0.3):\n    ax.plot([fx, fx], [y_bottom, y_top],\n            color=SECONDARY, linewidth=0.5, alpha=0.5,\n            linestyle='--', zorder=5)\n\n# Battery cells (horizontal bands)\nfor by in np.arange(y_bottom+0.1, y_top, 0.2):\n    ax.plot([x_start, x_end], [by, by],\n            color=lighten(BATTERY_COLOR, 0.1), linewidth=0.5,\n            alpha=0.6, zorder=5)\n```\n","# Matplotlib Technical Poster Generation (Refined)\n\n## Installation\n```bash\npip3 install matplotlib pillow --break-system-packages\n```\n\n## Setup (headless rendering — critical for servers)\n```python\nimport matplotlib\nmatplotlib.use('Agg')  # MUST be set before importing pyplot\nimport matplotlib.pyplot as plt\nimport matplotlib.patches as patches\nimport numpy as np\nimport json\n```\n\n## Figure Setup\n```python\nfig, ax = plt.subplots(figsize=(14, 20))   # portrait poster\nfig.patch.set_facecolor(BG_COLOR)\nax.set_facecolor(BG_COLOR)\nax.set_xlim(0, 14)\nax.set_ylim(0, 20)\nax.axis('off')\nplt.subplots_adjust(left=0, right=1, top=1, bottom=0)  # remove default margins\n```\n\n## Saving (high quality)\n```python\nplt.savefig('\u002Froot\u002Foutput.png', dpi=200, bbox_inches='tight',\n            facecolor=fig.get_facecolor(), edgecolor='none')\nplt.close()\n```\n- `dpi=200` → ~2800×4000px for 14×20in figure (better quality than dpi=150)\n\n## Known matplotlib Limitations\n| Feature | Issue | Workaround |\n|---|---|---|\n| `letter_spacing` | Not a Text property → AttributeError | Use spaced chars: `\"N O V A\"` |\n| `fontfamily='Inter'` | May not be installed | Use `'DejaVu Sans'` as fallback |\n| `alpha` on patches | Works fine | N\u002FA |\n| `linestyle='dotted'` | Use `linestyle=':'` or `(0, (1,3))` | N\u002FA |\n\n## Color Utilities\n```python\ndef hex_to_rgb(h):\n    h = h.lstrip('#')\n    return tuple(int(h[i:i+2], 16)\u002F255 for i in (0,2,4))\n\ndef lighten(h, factor=0.25):\n    r,g,b = hex_to_rgb(h)\n    return (r+(1-r)*factor, g+(1-g)*factor, b+(1-b)*factor)\n\ndef darken(h, factor=0.25):\n    r,g,b = hex_to_rgb(h)\n    return (r*(1-factor), g*(1-factor), b*(1-factor))\n```\n\n## Pseudo-3D Hardware Layer (three-face box)\n```python\ndef draw_layer(ax, x, y, w, h_front, depth, skew,\n               face_color, edge_color='#9B9B9B', z=3):\n    \"\"\"Front face + top face + right face = pseudo-3D box.\"\"\"\n    # Front face\n    front = patches.Rectangle((x, y), w, h_front,\n        facecolor=face_color, edgecolor=edge_color, linewidth=0.9, zorder=z)\n    ax.add_patch(front)\n    # Top face\n    tx = [x, x+w, x+w+skew, x+skew, x]\n    ty = [y+h_front]*2 + [y+h_front+depth]*2 + [y+h_front]\n    ax.fill(tx, ty, color=lighten(face_color, 0.28), zorder=z)\n    ax.plot(tx, ty, color=edge_color, linewidth=0.8, zorder=z+1)\n    # Right side face\n    rx = [x+w, x+w+skew, x+w+skew, x+w, x+w]\n    ry = [y, y+depth, y+h_front+depth, y+h_front, y]\n    ax.fill(rx, ry, color=darken(face_color, 0.22), zorder=z)\n    ax.plot(rx, ry, color=edge_color, linewidth=0.8, zorder=z+1)\n```\n\n## Leader Line Annotations\n```python\ndef add_leader(ax, tip_x, tip_y, label_x, label_y, label,\n               gray='#9B9B9B', text_color='#1A1816', font='DejaVu Sans'):\n    \"\"\"Diagonal leader + horizontal rule + label.\"\"\"\n    ax.plot([tip_x, label_x], [tip_y, label_y],\n            color=gray, linewidth=0.65, zorder=6)\n    ax.plot([label_x, label_x + 2.0], [label_y, label_y],\n            color=gray, linewidth=0.65, zorder=6)\n    ax.text(label_x + 2.1, label_y, label,\n            fontsize=8.5, va='center', color=text_color,\n            fontfamily=font, fontweight='light')\n```\n\n## Component Detail Overlays\n```python\n# PCB chip pads (small rectangles)\nfor cx in chip_x_positions:\n    chip = patches.Rectangle((cx-0.1, pcb_y-0.07), 0.2, 0.14,\n        facecolor=PRIMARY, edgecolor=GRAY, linewidth=0.4, zorder=7)\n    ax.add_patch(chip)\n\n# Thermal fins (vertical dashed lines within thermal layer)\nfor fx in np.arange(x_start, x_end, 0.3):\n    ax.plot([fx, fx], [y_bottom, y_top],\n            color=SECONDARY, linewidth=0.5, alpha=0.5,\n            linestyle='--', zorder=5)\n\n# Battery cells (horizontal bands)\nfor by in np.arange(y_bottom+0.1, y_top, 0.2):\n    ax.plot([x_start, x_end], [by, by],\n            color=lighten(BATTERY_COLOR, 0.1), linewidth=0.5,\n            alpha=0.6, zorder=5)\n```\n",{"name":5,"description":7},"main","726e1f79b96b0cb3d729d0e55a1c847e0b5353ae","skills\u002Fb2-self-feedback-claude-sonnet-4-6\u002Fanthropic-poster-design\u002Frun2_matplotlib-technical-poster",7,"https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench","https:\u002F\u002Fcxcscmu.github.io\u002FSkillLearnBench","https:\u002F\u002Fgithub.com\u002Fcxcscmu","2026-10-02 22:55:36",{"source":8,"skillName":5,"commands":48,"prompt":58,"zipAvailable":59},[49,52,55],{"tool":50,"command":51},"npx","npx skills add cxcscmu\u002FSkillLearnBench --skill run2_matplotlib-technical-poster",{"tool":53,"command":54},"bunx","bunx skills add cxcscmu\u002FSkillLearnBench --skill run2_matplotlib-technical-poster",{"tool":56,"command":57},"pnpm","pnpm dlx skills add cxcscmu\u002FSkillLearnBench --skill run2_matplotlib-technical-poster","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench\u002Fblob\u002Fmain\u002Fskills\u002Fb2-self-feedback-claude-sonnet-4-6\u002Fanthropic-poster-design\u002Frun2_matplotlib-technical-poster\u002FSKILL.md",true,[],[62,78,103,121,141,153],{"skillNo":63,"name":64,"nameZh":6,"description":65,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":66,"categories":67,"keywords":71,"publishedAt":34},"01M6KM5KH175P5ABP5JMX2SBDQ","object_counter","Count occurrences of an object in the image using computer vision algorithm.","https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench\u002Fblob\u002Fmain\u002Fskills\u002Fhuman_authored\u002Fvideo-object-counting\u002Fobject_counter\u002FSKILL.md",[68],{"categoryNo":69,"categoryName":70,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000016","图像与多媒体",[72,73,74,75,76,77],"object counter","object","vision","computer","Computer Vision","image",{"skillNo":79,"name":80,"nameZh":6,"description":81,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":82,"categories":83,"keywords":87,"publishedAt":34},"01M6KM51TMVXG79MMPCS6Y8G86","travel-planning-workflow","Invoke this skill when performing travel planning. It defines a structured workflow for generating itineraries. Following this workflow is essential to ensure the final plan satisfies user requirements.","https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench\u002Fblob\u002Fmain\u002Fskills\u002Fhuman_authored\u002Ftravel-planning\u002Ftravel-planning-workflow\u002FSKILL.md",[84],{"categoryNo":85,"categoryName":86,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000023","Skill与智能体开发",[88,89,90,91,92,93,94,95,96,97,98,99,100,101,102],"workflow","planning","planning workflow","travel","travel planning","travel planning workflow","structured","performing","plan","for","user","it","requirements","essential","itineraries",{"skillNo":104,"name":105,"nameZh":6,"description":106,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":107,"categories":108,"keywords":109,"publishedAt":34},"01M6KM4DY820XHS9YTSK5QP5HD","bundled-dataset-search-tool","invoke this skill when you need to perform database search for travel planning. This skill provides some useful pre-packaged tools to look up accommodations, attractions, cities, driving distance, flights, and restaurants from the bundled dataset.","https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench\u002Fblob\u002Fmain\u002Fskills\u002Fhuman_authored\u002Ftravel-planning\u002Fbundled-dataset-search-tool\u002FSKILL.md",[],[110,111,112,113,114,115,116,91,117,118,92,119,97,120,89],"search","tool","dataset","bundled dataset search tool","restaurants","Tools","up","look","flights","you","database",{"skillNo":122,"name":123,"nameZh":6,"description":124,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":125,"categories":126,"keywords":127,"publishedAt":34},"01M6KM465BB9PQ0B548HQSVAWZ","glm-output","Output-processing guidance for GLM tasks. Especially useful after glm-basics and glm-calibration when you need verifier-matching metrics from output.nc and a final \u002Froot\u002Fmetrics.json.","https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench\u002Fblob\u002Fmain\u002Fskills\u002Fhuman_authored\u002Ftemperature-simulation\u002Fglm-output\u002FSKILL.md",[],[128,129,130,97,131,132,133,134,135,136,137,138,119,139,140],"glm output","output","glm","tasks","metrics","json","basics","guidance","matching","verifier","processing","root","calibration",{"skillNo":142,"name":143,"nameZh":6,"description":144,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":145,"categories":146,"keywords":148,"publishedAt":34},"01M6KM40KY3QWDW5W97S39R2BC","glm-calibration","Calibration guidance for GLM tasks. Often most effective after glm-basics has clarified the setup; glm-output is the companion skill for exact final metric computation.","https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench\u002Fblob\u002Fmain\u002Fskills\u002Fhuman_authored\u002Ftemperature-simulation\u002Fglm-calibration\u002FSKILL.md",[147],{"categoryNo":85,"categoryName":86,"taxonomyType":20,"taxonomyTypeDesc":21},[149,140,130,129,135,134,131,150,151,97,152],"glm calibration","setup","companion","metric",{"skillNo":154,"name":155,"nameZh":6,"description":156,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":157,"categories":158,"keywords":159,"publishedAt":34},"01M6KM3VHHVSGMGHJQB3PPT6YG","glm-basics","A good starting point for GLM calibration tasks. Use to inspect glm3.nml, confirm how GLM runs, and identify the relevant files before moving on to calibration and output evaluation.","https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench\u002Fblob\u002Fmain\u002Fskills\u002Fhuman_authored\u002Ftemperature-simulation\u002Fglm-basics\u002FSKILL.md",[],[160,130,134,161,149,140,97,129,162,163,164,165,166,167,131],"glm basics","inspect","relevant","files","good","use","how","Evaluation"]