[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M7ECT0STFQK1EBEXX472X2XB":3,"skill-same:01M7ECT0STFQK1EBEXX472X2XB":129},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":18,"publishedAt":19,"linkOnly":20,"markdownContent":21,"markdownBody":22,"frontMatter":23,"refName":29,"commitSha":30,"skillDirectory":31,"forksCount":32,"repositoryHtmlUrl":33,"repositoryHomepageUrl":6,"ownerHtmlUrl":34,"ownerBlogUrl":6,"ownerDescription":35,"updatedAt":36,"install":37,"resources":50},"01M7ECT0STFQK1EBEXX472X2XB","scholar-evaluation",null,"Provide qualitative-first, evidence-traceable developmental review of scholarly works and audit low-stakes research-assessment rubrics with optional local quality controls. Never use for ranking people or consequential decisions.","sanjeevafk\u002Fagent-skills","sanjeevafk","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F230678117?v=4","01M7C540EFY46ETG1NNWKS5X3Q","Sanjeev","01M7C540F366J5C1D587BMKH9Q",0,"MIT","https:\u002F\u002Fgithub.com\u002Fsanjeevafk\u002Fagent-skills\u002Fblob\u002Fmain\u002Fcollections\u002Fscientific-skills\u002Fscholar-evaluation\u002FSKILL.md",[],[],"2026-11-15 14:19:17",false,"---\nname: scholar-evaluation\ndescription: Provide qualitative-first, evidence-traceable developmental review of scholarly works and audit low-stakes research-assessment rubrics with optional local quality controls. Never use for ranking people or consequential decisions.\nlicense: MIT\ncompatibility: Requires Python 3.11+ for optional bundled standard-library CLIs. All tooling is local JSON\u002FCSV processing with no network, credentials, external models, or subprocesses.\nallowed-tools: Read Write Bash Glob Python\nmetadata:\n  version: \"2.1\"\n  skill-author: K-Dense Inc.\n---\n\n# Scholar Evaluation\n\n## Purpose\n\nProvide developmental, evidence-traceable feedback on a **scholarly work**:\npaper, draft, protocol, literature synthesis, or research idea. Use\nqualitative judgment first. Optional scores only describe how submitted\nevidence maps to a predeclared bounded rubric.\n\nThis skill also audits whether a low-stakes assessment process documents its\nconstruct, provenance, rater quality, uncertainty, traceability, sensitivity,\nfairness, accessibility, privacy, and human governance.\n\n## Hard safety boundary\n\nNever use this skill to automate, recommend, materially influence, or score:\n\n- hiring, promotion, or tenure;\n- admissions;\n- grants or other funding;\n- prizes, honors, or awards;\n- discipline, dismissal, or sanctions; or\n- any other high-impact personnel decision.\n\nNever rank people. Never reduce a person to a composite score. Never infer\nability, character, integrity, protected traits, future performance, or worth.\nA nominal human-in-the-loop does not remove this boundary.\n\nIf asked for a prohibited use, stop. Offer developmental comments on a\nscholarly work or a process-only audit that does not process applications,\ncompare people, recommend an outcome, or advise a decision.\n\nDo not issue publication-readiness, accept\u002Freject, or “top-tier” judgments.\n\nRead `references\u002Fresponsible_assessment.md` before any organizational use.\n\n## ScholarEval status\n\nThe referenced ScholarEval project is an **experimental\nliterature-grounded research-idea evaluation framework**, not validated\npsychometrics.\n\nThe verified primary record is Moussa et al., *ScholarEval: Research Idea\nEvaluation Grounded in Literature*, arXiv:2510.16234v2, revised 2026-02-28.\nIt reports a retrieval-augmented soundness\u002Fcontribution framework, a\n117-idea four-discipline dataset, coverage experiments, and a user study.\n\nDo not generalize those results to person assessment, consequential decisions,\nall disciplines, or this skill's rubric. No peer-reviewed publication status\nwas verified during the dated review. See `references\u002Fsource_ledger.md`.\n\n## Metric and prestige policy\n\nDo not score or infer quality from:\n\n- Journal Impact Factor or other journal measures;\n- h-index, publication counts, or citation counts;\n- altmetrics or attention;\n- journal, conference, venue, institution, employer, or geographic prestige;\n- author affiliation, reputation, network, or career path.\n\nThe rubric validator rejects common proxy-measure criteria.\n\nIf a qualified reviewer mentions an indicator descriptively outside the\nscoring tools, record its exact purpose, source, coverage, field and time\neffects, uncertainty, missingness, biases, gaming risk, and why it does not\ndirectly measure quality. Never hide indicators inside an opaque composite.\n\n## Data boundary\n\nBundled scripts accept only strict local JSON\u002FCSV containing pseudonymous IDs,\nbounded ratings, statuses, uncertainty, and local references.\n\nDo not put raw private applications, CVs, letters, reviewer identities,\ncontact details, protected attributes, or source-document text in inputs,\noutputs, logs, examples, or prompts. Keep source content in the authorized\nrecords system and use opaque local references.\n\nAllowed classifications are:\n\n- `synthetic`\n- `public_scholarly_work`\n- `deidentified_low_stakes`\n\nNo script searches the web, loads environment files, reads credentials, calls a\nmodel, executes supplied text, deserializes executable objects, or launches a\nprocess.\n\nUse Bash only to invoke the documented local `python3` commands.\n\n## Workflow\n\n### 1. Confirm allowed use and authorization\n\nRecord:\n\n- developmental purpose;\n- unit of assessment: `scholarly_work`;\n- work type, stage, discipline, language, and audience;\n- authorized source location and data classification;\n- accountable committee owner;\n- conflicts and recusals;\n- accessibility and accommodation process;\n- appeal or correction route; and\n- data purpose, access, retention, and deletion.\n\nStop on a prohibited decision context or unnecessary private data.\n\n### 2. Define the construct before criteria\n\nState:\n\n- what quality or support is being examined;\n- excluded constructs;\n- intended interpretation;\n- contexts where the interpretation does not travel;\n- evidence requirements; and\n- known limitations.\n\nStart with values and disciplinary context, not available metrics.\n\n### 3. Adapt and validate the rubric\n\nBegin with `assets\u002Frubric_template.json`, then obtain qualified disciplinary,\nassessment-methods, stakeholder, accessibility, privacy, and fairness review.\n\nThe template deliberately records content validity as `not_established`.\nDo not change that status without documented evidence for the exact intended\nuse.\n\nValidate structure:\n\n```bash\nPYTHONDONTWRITEBYTECODE=1 python3 scripts\u002Fvalidate_rubric.py \\\n  --rubric assets\u002Frubric_template.json\n```\n\nRead `references\u002Fevaluation_framework.md` for construct, anchor, validity, and\nrater guidance.\n\n### 4. Build traceable evidence records\n\nReviewers may read an authorized work outside the scripts. Record only stable\nlocal locators and claim references in\n`assets\u002Fevidence_manifest_template.json`.\n\nFor every criterion, distinguish:\n\n- observed evidence from interpretation;\n- supporting from contrary evidence;\n- available from unavailable evidence;\n- `missing` from `not_applicable`; and\n- uncertainty from absence.\n\nFailure to find prior work does not prove novelty.\n\n### 5. Rate independently\n\nUse `assets\u002Fevaluation_template.json`. Each criterion must be:\n\n- `rated` with an anchor score, bounded uncertainty, evidence IDs, and a local\n  rationale reference;\n- `missing` with null score\u002Funcertainty and a rationale reference; or\n- `not_applicable` with null score\u002Funcertainty and a rationale reference.\n\nDo not encode missing or not-applicable as zero. Raters should train, calibrate,\ndisclose conflicts, rate independently, and document disagreement.\n\n### 6. Run local quality checks\n\nBounded scoring, without labels or recommendation:\n\n```bash\nPYTHONDONTWRITEBYTECODE=1 python3 scripts\u002Fcalculate_scores.py \\\n  --rubric assets\u002Frubric_template.json \\\n  --evaluation assets\u002Fevaluation_template.json\n```\n\nEvidence traceability:\n\n```bash\nPYTHONDONTWRITEBYTECODE=1 python3 scripts\u002Fcheck_traceability.py \\\n  --rubric assets\u002Frubric_template.json \\\n  --evaluation assets\u002Fevaluation_template.json \\\n  --evidence assets\u002Fevidence_manifest_template.json\n```\n\nInter-rater agreement:\n\n```bash\nPYTHONDONTWRITEBYTECODE=1 python3 scripts\u002Fsummarize_agreement.py \\\n  --rubric assets\u002Frubric_template.json \\\n  --ratings assets\u002Fratings_template.csv\n```\n\nWeight sensitivity requires two or more distinct scholarly-work evaluation\nfiles:\n\n```bash\nPYTHONDONTWRITEBYTECODE=1 python3 scripts\u002Fweight_sensitivity.py \\\n  --rubric assets\u002Frubric_template.json \\\n  --evaluation \u002Ftmp\u002Fwork-a-evaluation.json \\\n  --evaluation \u002Ftmp\u002Fwork-b-evaluation.json\n```\n\nProcess controls:\n\n```bash\nPYTHONDONTWRITEBYTECODE=1 python3 scripts\u002Fcheck_process.py \\\n  --process assets\u002Fprocess_checklist_template.json\n```\n\nThe checklist template is intentionally unconfirmed and fails closed.\nInstructions and exact schemas are in `references\u002Flocal_tooling.md`.\n\n### 7. Synthesize qualitative findings\n\nLead with criterion-level evidence, not the composite. For each criterion:\n\n1. cite evidence references;\n2. state `rated`, `missing`, or `not_applicable`;\n3. explain the anchor interpretation;\n4. report score and uncertainty only if rated;\n5. note disagreements and context;\n6. identify strengths and limitations; and\n7. offer non-prescriptive improvement options.\n\nGenerate an empty-reference scaffold if useful:\n\n```bash\nPYTHONDONTWRITEBYTECODE=1 python3 scripts\u002Fgenerate_report_scaffold.py \\\n  --rubric assets\u002Frubric_template.json \\\n  --evaluation assets\u002Fevaluation_template.json \\\n  --output \u002Ftmp\u002Fdevelopmental-report-scaffold.json\n```\n\nThe scaffold does not read source documents or draft findings.\n\n### 8. Human review and release\n\nBefore releasing an organizational report, a qualified accountable human\ncommittee must verify:\n\n- construct and rubric provenance;\n- content-validity evidence and limits;\n- rater training, agreement, inter-rater reliability evidence, and drift;\n- evidence traceability and source access;\n- missingness, not-applicable rationales, and uncertainty;\n- weight sensitivity and order instability;\n- disciplinary and subgroup bias review;\n- conflicts and recusals;\n- accessibility and accommodations;\n- privacy, minimization, retention, and output controls; and\n- correction or appeal information.\n\nDocument dissent. Do not imply consensus, validity, or precision beyond the\nevidence. Periodically evaluate the evaluation and retire harmful criteria.\n\n## Interpretation rules\n\n- A score is an ordinal rubric summary, not a natural measurement.\n- Normalization does not repair incomplete evidence.\n- The bundled uncertainty range is not a confidence interval.\n- Agreement does not establish reliability, validity, fairness, or correctness.\n- Stable results under tested weights do not establish validity.\n- The overall score never overrides criterion evidence or qualified judgment.\n- No output is a decision recommendation.\n\n## Bundled resources\n\n- `references\u002Fresponsible_assessment.md` — safety, metrics, governance,\n  accessibility, privacy, and bias.\n- `references\u002Fevaluation_framework.md` — ScholarEval boundary, construct,\n  criteria, anchors, validity, and interpretation.\n- `references\u002Flocal_tooling.md` — strict schemas, formulas, commands, and\n  output behavior.\n- `references\u002Fsource_ledger.md` — authoritative sources and publication-status\n  verification dated 2026-07-23.\n- `references\u002Fsecurity_validation.md` — baseline remediation, validation, and\n  residual security-scan record.\n- `assets\u002Frubric_template.json` — bounded rubric template.\n- `assets\u002Fevaluation_template.json` — rating template.\n- `assets\u002Fevidence_manifest_template.json` — traceability template.\n- `assets\u002Fprocess_checklist_template.json` — fail-closed process checklist.\n- `assets\u002Fratings_template.csv` — synthetic agreement data.\n","# Scholar Evaluation\n\n## Purpose\n\nProvide developmental, evidence-traceable feedback on a **scholarly work**:\npaper, draft, protocol, literature synthesis, or research idea. Use\nqualitative judgment first. Optional scores only describe how submitted\nevidence maps to a predeclared bounded rubric.\n\nThis skill also audits whether a low-stakes assessment process documents its\nconstruct, provenance, rater quality, uncertainty, traceability, sensitivity,\nfairness, accessibility, privacy, and human governance.\n\n## Hard safety boundary\n\nNever use this skill to automate, recommend, materially influence, or score:\n\n- hiring, promotion, or tenure;\n- admissions;\n- grants or other funding;\n- prizes, honors, or awards;\n- discipline, dismissal, or sanctions; or\n- any other high-impact personnel decision.\n\nNever rank people. Never reduce a person to a composite score. Never infer\nability, character, integrity, protected traits, future performance, or worth.\nA nominal human-in-the-loop does not remove this boundary.\n\nIf asked for a prohibited use, stop. Offer developmental comments on a\nscholarly work or a process-only audit that does not process applications,\ncompare people, recommend an outcome, or advise a decision.\n\nDo not issue publication-readiness, accept\u002Freject, or “top-tier” judgments.\n\nRead `references\u002Fresponsible_assessment.md` before any organizational use.\n\n## ScholarEval status\n\nThe referenced ScholarEval project is an **experimental\nliterature-grounded research-idea evaluation framework**, not validated\npsychometrics.\n\nThe verified primary record is Moussa et al., *ScholarEval: Research Idea\nEvaluation Grounded in Literature*, arXiv:2510.16234v2, revised 2026-02-28.\nIt reports a retrieval-augmented soundness\u002Fcontribution framework, a\n117-idea four-discipline dataset, coverage experiments, and a user study.\n\nDo not generalize those results to person assessment, consequential decisions,\nall disciplines, or this skill's rubric. No peer-reviewed publication status\nwas verified during the dated review. See `references\u002Fsource_ledger.md`.\n\n## Metric and prestige policy\n\nDo not score or infer quality from:\n\n- Journal Impact Factor or other journal measures;\n- h-index, publication counts, or citation counts;\n- altmetrics or attention;\n- journal, conference, venue, institution, employer, or geographic prestige;\n- author affiliation, reputation, network, or career path.\n\nThe rubric validator rejects common proxy-measure criteria.\n\nIf a qualified reviewer mentions an indicator descriptively outside the\nscoring tools, record its exact purpose, source, coverage, field and time\neffects, uncertainty, missingness, biases, gaming risk, and why it does not\ndirectly measure quality. Never hide indicators inside an opaque composite.\n\n## Data boundary\n\nBundled scripts accept only strict local JSON\u002FCSV containing pseudonymous IDs,\nbounded ratings, statuses, uncertainty, and local references.\n\nDo not put raw private applications, CVs, letters, reviewer identities,\ncontact details, protected attributes, or source-document text in inputs,\noutputs, logs, examples, or prompts. Keep source content in the authorized\nrecords system and use opaque local references.\n\nAllowed classifications are:\n\n- `synthetic`\n- `public_scholarly_work`\n- `deidentified_low_stakes`\n\nNo script searches the web, loads environment files, reads credentials, calls a\nmodel, executes supplied text, deserializes executable objects, or launches a\nprocess.\n\nUse Bash only to invoke the documented local `python3` commands.\n\n## Workflow\n\n### 1. Confirm allowed use and authorization\n\nRecord:\n\n- developmental purpose;\n- unit of assessment: `scholarly_work`;\n- work type, stage, discipline, language, and audience;\n- authorized source location and data classification;\n- accountable committee owner;\n- conflicts and recusals;\n- accessibility and accommodation process;\n- appeal or correction route; and\n- data purpose, access, retention, and deletion.\n\nStop on a prohibited decision context or unnecessary private data.\n\n### 2. Define the construct before criteria\n\nState:\n\n- what quality or support is being examined;\n- excluded constructs;\n- intended interpretation;\n- contexts where the interpretation does not travel;\n- evidence requirements; and\n- known limitations.\n\nStart with values and disciplinary context, not available metrics.\n\n### 3. Adapt and validate the rubric\n\nBegin with `assets\u002Frubric_template.json`, then obtain qualified disciplinary,\nassessment-methods, stakeholder, accessibility, privacy, and fairness review.\n\nThe template deliberately records content validity as `not_established`.\nDo not change that status without documented evidence for the exact intended\nuse.\n\nValidate structure:\n\n```bash\nPYTHONDONTWRITEBYTECODE=1 python3 scripts\u002Fvalidate_rubric.py \\\n  --rubric assets\u002Frubric_template.json\n```\n\nRead `references\u002Fevaluation_framework.md` for construct, anchor, validity, and\nrater guidance.\n\n### 4. Build traceable evidence records\n\nReviewers may read an authorized work outside the scripts. Record only stable\nlocal locators and claim references in\n`assets\u002Fevidence_manifest_template.json`.\n\nFor every criterion, distinguish:\n\n- observed evidence from interpretation;\n- supporting from contrary evidence;\n- available from unavailable evidence;\n- `missing` from `not_applicable`; and\n- uncertainty from absence.\n\nFailure to find prior work does not prove novelty.\n\n### 5. Rate independently\n\nUse `assets\u002Fevaluation_template.json`. Each criterion must be:\n\n- `rated` with an anchor score, bounded uncertainty, evidence IDs, and a local\n  rationale reference;\n- `missing` with null score\u002Funcertainty and a rationale reference; or\n- `not_applicable` with null score\u002Funcertainty and a rationale reference.\n\nDo not encode missing or not-applicable as zero. Raters should train, calibrate,\ndisclose conflicts, rate independently, and document disagreement.\n\n### 6. Run local quality checks\n\nBounded scoring, without labels or recommendation:\n\n```bash\nPYTHONDONTWRITEBYTECODE=1 python3 scripts\u002Fcalculate_scores.py \\\n  --rubric assets\u002Frubric_template.json \\\n  --evaluation assets\u002Fevaluation_template.json\n```\n\nEvidence traceability:\n\n```bash\nPYTHONDONTWRITEBYTECODE=1 python3 scripts\u002Fcheck_traceability.py \\\n  --rubric assets\u002Frubric_template.json \\\n  --evaluation assets\u002Fevaluation_template.json \\\n  --evidence assets\u002Fevidence_manifest_template.json\n```\n\nInter-rater agreement:\n\n```bash\nPYTHONDONTWRITEBYTECODE=1 python3 scripts\u002Fsummarize_agreement.py \\\n  --rubric assets\u002Frubric_template.json \\\n  --ratings assets\u002Fratings_template.csv\n```\n\nWeight sensitivity requires two or more distinct scholarly-work evaluation\nfiles:\n\n```bash\nPYTHONDONTWRITEBYTECODE=1 python3 scripts\u002Fweight_sensitivity.py \\\n  --rubric assets\u002Frubric_template.json \\\n  --evaluation \u002Ftmp\u002Fwork-a-evaluation.json \\\n  --evaluation \u002Ftmp\u002Fwork-b-evaluation.json\n```\n\nProcess controls:\n\n```bash\nPYTHONDONTWRITEBYTECODE=1 python3 scripts\u002Fcheck_process.py \\\n  --process assets\u002Fprocess_checklist_template.json\n```\n\nThe checklist template is intentionally unconfirmed and fails closed.\nInstructions and exact schemas are in `references\u002Flocal_tooling.md`.\n\n### 7. Synthesize qualitative findings\n\nLead with criterion-level evidence, not the composite. For each criterion:\n\n1. cite evidence references;\n2. state `rated`, `missing`, or `not_applicable`;\n3. explain the anchor interpretation;\n4. report score and uncertainty only if rated;\n5. note disagreements and context;\n6. identify strengths and limitations; and\n7. offer non-prescriptive improvement options.\n\nGenerate an empty-reference scaffold if useful:\n\n```bash\nPYTHONDONTWRITEBYTECODE=1 python3 scripts\u002Fgenerate_report_scaffold.py \\\n  --rubric assets\u002Frubric_template.json \\\n  --evaluation assets\u002Fevaluation_template.json \\\n  --output \u002Ftmp\u002Fdevelopmental-report-scaffold.json\n```\n\nThe scaffold does not read source documents or draft findings.\n\n### 8. Human review and release\n\nBefore releasing an organizational report, a qualified accountable human\ncommittee must verify:\n\n- construct and rubric provenance;\n- content-validity evidence and limits;\n- rater training, agreement, inter-rater reliability evidence, and drift;\n- evidence traceability and source access;\n- missingness, not-applicable rationales, and uncertainty;\n- weight sensitivity and order instability;\n- disciplinary and subgroup bias review;\n- conflicts and recusals;\n- accessibility and accommodations;\n- privacy, minimization, retention, and output controls; and\n- correction or appeal information.\n\nDocument dissent. Do not imply consensus, validity, or precision beyond the\nevidence. Periodically evaluate the evaluation and retire harmful criteria.\n\n## Interpretation rules\n\n- A score is an ordinal rubric summary, not a natural measurement.\n- Normalization does not repair incomplete evidence.\n- The bundled uncertainty range is not a confidence interval.\n- Agreement does not establish reliability, validity, fairness, or correctness.\n- Stable results under tested weights do not establish validity.\n- The overall score never overrides criterion evidence or qualified judgment.\n- No output is a decision recommendation.\n\n## Bundled resources\n\n- `references\u002Fresponsible_assessment.md` — safety, metrics, governance,\n  accessibility, privacy, and bias.\n- `references\u002Fevaluation_framework.md` — ScholarEval boundary, construct,\n  criteria, anchors, validity, and interpretation.\n- `references\u002Flocal_tooling.md` — strict schemas, formulas, commands, and\n  output behavior.\n- `references\u002Fsource_ledger.md` — authoritative sources and publication-status\n  verification dated 2026-07-23.\n- `references\u002Fsecurity_validation.md` — baseline remediation, validation, and\n  residual security-scan record.\n- `assets\u002Frubric_template.json` — bounded rubric template.\n- `assets\u002Fevaluation_template.json` — rating template.\n- `assets\u002Fevidence_manifest_template.json` — traceability template.\n- `assets\u002Fprocess_checklist_template.json` — fail-closed process checklist.\n- `assets\u002Fratings_template.csv` — synthetic agreement data.\n",{"name":5,"license":15,"metadata":24,"description":7,"allowed-tools":27,"compatibility":28},{"version":25,"skill-author":26},"2.1","K-Dense Inc.","Read Write Bash Glob Python","Requires Python 3.11+ for optional bundled standard-library CLIs. All tooling is local JSON\u002FCSV processing with no network, credentials, external models, or subprocesses.","main","4e084e84eb42fc53e144c89c0f7f48b39c8a4b0f","collections\u002Fscientific-skills\u002Fscholar-evaluation",1,"https:\u002F\u002Fgithub.com\u002Fsanjeevafk\u002Fagent-skills","https:\u002F\u002Fgithub.com\u002Fsanjeevafk","First make it work. Then make it right. Then make it fast.","2026-11-14 13:36:16",{"source":8,"skillName":5,"commands":38,"prompt":48,"zipAvailable":49},[39,42,45],{"tool":40,"command":41},"npx","npx skills add sanjeevafk\u002Fagent-skills --skill scholar-evaluation",{"tool":43,"command":44},"bunx","bunx skills add sanjeevafk\u002Fagent-skills --skill scholar-evaluation",{"tool":46,"command":47},"pnpm","pnpm dlx skills add sanjeevafk\u002Fagent-skills --skill 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Use when the user requests deployment actions like \"deploy my app\", \"deploy and give me the link\", \"push this live\", or \"create a preview deployment\".","https:\u002F\u002Fgithub.com\u002Fsanjeevafk\u002Fagent-skills\u002Fblob\u002Fmain\u002Fskills\u002Fvercel-deploy\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fsanjeevafk\u002Fagent-skills\u002Fblob\u002Fmain\u002Fskills\u002Fvercel-deploy\u002FSKILL.md",[],[]]