[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6KGKQP0YNSMJ8G2RM5PC9ZP":3,"skill-same:01M6KGKQP0YNSMJ8G2RM5PC9ZP":57},{"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":17,"publishedAt":30,"linkOnly":31,"markdownContent":32,"markdownBody":33,"frontMatter":34,"refName":35,"commitSha":36,"skillDirectory":37,"forksCount":38,"repositoryHtmlUrl":39,"repositoryHomepageUrl":40,"ownerHtmlUrl":41,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":42,"install":43,"resources":56},"01M6KGKQP0YNSMJ8G2RM5PC9ZP","pareto-frontier-optimization",null,"Execute grid search over DBSCAN hyperparameters, evaluate each combination across all images, filter by F1 threshold, and identify Pareto-optimal solutions balancing F1 score and delta metric.","cxcscmu\u002FSkillLearnBench","cxcscmu","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F142838272?v=4","01M6KCQDFQWPDSN0MKZY96MSQP","01M6KCQDM7G70TKJ28NTSQC0RZ",93,"MIT","https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench\u002Fblob\u002Fmain\u002Fskills\u002Fb3-teacher-feedback-claude-haiku-4-5\u002Fdbscan-parameter-tuning\u002Frun1_pareto-frontier-optimization\u002FSKILL.md",[],[18,19,20,21,22,23,24,25,26,27,28,29],"Optimization","pareto frontier","pareto frontier optimization","search","execute","images","delta","across","filter","grid","evaluate","metric","2026-11-10 10:12:30",false,"---\nname: pareto-frontier-optimization\ndescription: Execute grid search over DBSCAN hyperparameters, evaluate each combination across all images, filter by F1 threshold, and identify Pareto-optimal solutions balancing F1 score and delta metric.\n---\n\n## Grid Search Space\n\nCreate all combinations of:\n\n| Parameter | Range | Values |\n|-----------|-------|--------|\n| `min_samples` | 3–9 | [3, 4, 5, 6, 7, 8, 9] |\n| `epsilon` | 4–24 (step 2) | [4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24] |\n| `shape_weight` | 0.9–1.9 (step 0.1) | [0.9, 1.0, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9] |\n\n**Total combinations:** 7 × 11 × 11 = 847\n\n## Evaluation Pipeline\n\nFor each hyperparameter combination (min_samples, epsilon, shape_weight):\n\n1. **Loop over all unique images** from expert dataset (using `file_rad`)\n2. **For each image:**\n   - Extract citizen science points for this image\n   - If no citizen science points exist:\n     - Set F1 = 0.0, delta = NaN\n     - Continue to next image\n   - Run DBSCAN with current hyperparameters on citizen science points\n   - If no clusters found (all points noise or single cluster with \u003C min_samples):\n     - Set F1 = 0.0, delta = NaN\n     - Continue to next image\n   - Compute cluster centroids\n   - Extract expert points for this image\n   - Perform greedy matching of centroids to expert points\n   - Compute F1 score and delta metric for this image\n\n3. **Aggregate across images:**\n   - Average F1: Include all F1 values (including 0.0)\n   - Average delta: Only include non-NaN values\n   - If all delta values are NaN (no matches found in any image), set average delta = NaN\n\n4. **Filter results:**\n   - Keep only results where average F1 > 0.5\n\n## Pareto Frontier\n\nIdentify Pareto-optimal solutions:\n\nA solution is **Pareto-optimal** if:\n- No other solution has **both** higher F1 **and** lower delta\n- It is not dominated on both objectives\n\n**Optimization goals:**\n- **Maximize** F1 score (higher is better)\n- **Minimize** delta (lower is better)\n\n## Parallelization\n\nRecommended approach:\n- Use `multiprocessing.Pool` or `joblib.Parallel` to evaluate hyperparameter combinations in parallel\n- Each worker processes one or more complete combinations (all images for one hyperparameter set)\n- Collect results and apply filtering\u002FPareto frontier detection on main process","## Grid Search Space\n\nCreate all combinations of:\n\n| Parameter | Range | Values |\n|-----------|-------|--------|\n| `min_samples` | 3–9 | [3, 4, 5, 6, 7, 8, 9] |\n| `epsilon` | 4–24 (step 2) | [4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24] |\n| `shape_weight` | 0.9–1.9 (step 0.1) | [0.9, 1.0, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9] |\n\n**Total combinations:** 7 × 11 × 11 = 847\n\n## Evaluation Pipeline\n\nFor each hyperparameter combination (min_samples, epsilon, shape_weight):\n\n1. **Loop over all unique images** from expert dataset (using `file_rad`)\n2. **For each image:**\n   - Extract citizen science points for this image\n   - If no citizen science points exist:\n     - Set F1 = 0.0, delta = NaN\n     - Continue to next image\n   - Run DBSCAN with current hyperparameters on citizen science points\n   - If no clusters found (all points noise or single cluster with \u003C min_samples):\n     - Set F1 = 0.0, delta = NaN\n     - Continue to next image\n   - Compute cluster centroids\n   - Extract expert points for this image\n   - Perform greedy matching of centroids to expert points\n   - Compute F1 score and delta metric for this image\n\n3. **Aggregate across images:**\n   - Average F1: Include all F1 values (including 0.0)\n   - Average delta: Only include non-NaN values\n   - If all delta values are NaN (no matches found in any image), set average delta = NaN\n\n4. **Filter results:**\n   - Keep only results where average F1 > 0.5\n\n## Pareto Frontier\n\nIdentify Pareto-optimal solutions:\n\nA solution is **Pareto-optimal** if:\n- No other solution has **both** higher F1 **and** lower delta\n- It is not dominated on both objectives\n\n**Optimization goals:**\n- **Maximize** F1 score (higher is better)\n- **Minimize** delta (lower is better)\n\n## Parallelization\n\nRecommended approach:\n- Use `multiprocessing.Pool` or `joblib.Parallel` to evaluate hyperparameter combinations in parallel\n- Each worker processes one or more complete combinations (all images for one hyperparameter set)\n- Collect results and apply filtering\u002FPareto frontier detection on main process",{"name":5,"description":7},"main","726e1f79b96b0cb3d729d0e55a1c847e0b5353ae","skills\u002Fb3-teacher-feedback-claude-haiku-4-5\u002Fdbscan-parameter-tuning\u002Frun1_pareto-frontier-optimization",8,"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":44,"prompt":54,"zipAvailable":55},[45,48,51],{"tool":46,"command":47},"npx","npx skills add cxcscmu\u002FSkillLearnBench --skill pareto-frontier-optimization",{"tool":49,"command":50},"bunx","bunx skills add cxcscmu\u002FSkillLearnBench --skill pareto-frontier-optimization",{"tool":52,"command":53},"pnpm","pnpm dlx skills add cxcscmu\u002FSkillLearnBench --skill pareto-frontier-optimization","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench\u002Fblob\u002Fmain\u002Fskills\u002Fb3-teacher-feedback-claude-haiku-4-5\u002Fdbscan-parameter-tuning\u002Frun1_pareto-frontier-optimization\u002FSKILL.md",true,[],[58,76,101,118,138,149],{"skillNo":59,"name":60,"nameZh":6,"description":61,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":62,"categories":63,"keywords":69,"publishedAt":30},"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",[64],{"categoryNo":65,"categoryName":66,"taxonomyType":67,"taxonomyTypeDesc":68},"01JBSEEDCAT000000000000016","图像与多媒体","SCENARIO","使用场景",[70,71,72,73,74,75],"object counter","object","vision","computer","Computer Vision","image",{"skillNo":77,"name":78,"nameZh":6,"description":79,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":80,"categories":81,"keywords":85,"publishedAt":30},"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",[82],{"categoryNo":83,"categoryName":84,"taxonomyType":67,"taxonomyTypeDesc":68},"01JBSEEDCAT000000000000023","Skill与智能体开发",[86,87,88,89,90,91,92,93,94,95,96,97,98,99,100],"workflow","planning","planning workflow","travel","travel planning","travel planning workflow","structured","performing","plan","for","user","it","requirements","essential","itineraries",{"skillNo":102,"name":103,"nameZh":6,"description":104,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":105,"categories":106,"keywords":107,"publishedAt":30},"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",[],[21,108,109,110,111,112,113,89,114,115,90,116,95,117,87],"tool","dataset","bundled dataset search tool","restaurants","Tools","up","look","flights","you","database",{"skillNo":119,"name":120,"nameZh":6,"description":121,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":122,"categories":123,"keywords":124,"publishedAt":30},"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",[],[125,126,127,95,128,129,130,131,132,133,134,135,116,136,137],"glm output","output","glm","tasks","metrics","json","basics","guidance","matching","verifier","processing","root","calibration",{"skillNo":139,"name":140,"nameZh":6,"description":141,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":142,"categories":143,"keywords":145,"publishedAt":30},"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",[144],{"categoryNo":83,"categoryName":84,"taxonomyType":67,"taxonomyTypeDesc":68},[146,137,127,126,132,131,128,147,148,95,29],"glm calibration","setup","companion",{"skillNo":150,"name":151,"nameZh":6,"description":152,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":153,"categories":154,"keywords":155,"publishedAt":30},"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",[],[156,127,131,157,146,137,95,126,158,159,160,161,162,163,128],"glm basics","inspect","relevant","files","good","use","how","Evaluation"]