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Use for early-stage research brainstorming or prioritizing candidate directions; hand off empirical validation, study design, ethics or regulatory review, and clinical questions to appropriate experts or skills.","Tashima-Tarsh\u002FDisha6.6","Tashima-Tarsh","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F269906498?v=4","01M6PBH98X03XERH0HFT4PFB1P","NITISH KUMAR","01M6PBH9DA0M6KM3Z12P848F0K",0,"MIT","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fkal-scientific-brainstorming\u002FSKILL.md",[18,23,26,29],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000015","视觉设计","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000021","资料检索与研究",{"categoryNo":27,"categoryName":28,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",{"categoryNo":30,"categoryName":31,"taxonomyType":32,"taxonomyTypeDesc":33},"01JBSEEDCAT000000000000032","设计师","PROFESSION","适用职业",[35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54],"brainstorming","scientific","scientific brainstorming","ethics","regulatory review","adversarial","Empirical Validation","candidate","for","directions","host","use","design","Evaluation","logs","library","validation","adversarial review","research","python","2026-11-10 10:12:30",false,"---\nname: scientific-brainstorming\ndescription: Facilitates evidence-aware scientific ideation with independent generation, structured discussion, explicit assumptions, transparent evaluation, adversarial review, and decision logs. Use for early-stage research brainstorming or prioritizing candidate directions; hand off empirical validation, study design, ethics or regulatory review, and clinical questions to appropriate experts or skills.\nlicense: MIT\ncompatibility: Core guidance works in any Agent Skills-compatible host. Optional bundled CLIs require Python 3.11+ and use only the standard library; they make no network or LLM calls and require no credentials.\nmetadata:\n  version: '1.2'\n  category: ideation-and-design\n  maintainer: Kalaris Labs\n  tags: brainstorming, research ideation, research directions, scientific method\n---\n\n# Scientific Brainstorming\n\n## Purpose and boundaries\n\nUse this skill to create, organize, challenge, and transparently prioritize\ncandidate research directions. Treat every output as a **proposal**, not a\nfinding. Creativity methods can alter participation and idea yield, but no\nmethod universally improves originality, usefulness, or scientific validity.\nThe evidence base and its limits are summarized in\n`references\u002Fsources.md`.\n\nKeep these activities separate:\n\n- **Ideation** creates questions, mechanisms, alternatives, or study concepts.\n- **Evidence assessment** checks what reliable literature and data support.\n- **Hypothesis validation** requires observations, predictions, suitable\n  designs, analyses, and independent scrutiny; brainstorming cannot validate a\n  hypothesis.\n- **Ethics, biosafety, dual-use, regulatory, and institutional review** require\n  the relevant authorized reviewers. A brainstorm is never approval.\n- **Clinical advice** requires qualified clinicians and patient-specific\n  context. Do not turn research ideas into diagnosis or treatment guidance.\n\nFor an observation-led testable hypothesis, hand off to\n`hypothesis-generation`. For study architecture, use `experimental-design`;\nfor sample size, `statistical-power`; for existing evidence,\n`literature-review`; and for analysis, `statistical-analysis`.\n\n## Operating rules\n\n1. Label claims as **idea**, **assumption**, **prediction**, **located\n   evidence**, or **decision**. Never blur these categories.\n2. Generate independently before exposing participants to other people's or\n   AI-generated ideas. Face-to-face turn-taking can block production, and\n   examples can anchor later output.\n3. Preserve minority views, negative evidence, uncertainty, and abstentions.\n   Consensus is not truth and vote counts are not effect sizes.\n4. Record provenance without exposing confidential, personal, controlled, or\n   unpublished information.\n5. Define evaluation criteria and directions before scoring. Keep raw ratings,\n   reasons, ranges, and disagreement visible.\n6. Search the literature **after an initial independent round** when practical,\n   then deliberately reopen ideation. This reduces early anchoring without\n   mistaking an incomplete search for a research gap.\n7. Do not automatically select a “winner.” Scores are traceable decision aids;\n   qualitative judgment, uncertainty, feasibility, and ethics gates remain\n   controlling.\n\n## Reproducible workflow\n\n### 1. Scope the session\n\nWrite one focal question and record:\n\n- purpose, audience, decision owner, and time horizon;\n- in-scope and out-of-scope topics;\n- constraints that are real, assumed, negotiable, or unknown;\n- current knowledge, unresolved observations, and prohibited outputs;\n- whether human participants, animals, clinical care, sensitive data,\n  pathogens, controlled technologies, or environmental release could be\n  implicated.\n\nIf the request seeks patient-specific care, evasion of oversight, harmful\noptimization, or operationally enabling dual-use details, stop ideation and\nroute to the appropriate professional or institutional process.\n\n### 2. Diversify perspectives deliberately\n\nInvite relevant methodological, domain, implementation, statistical, safety,\nethics, stakeholder, and lived-experience perspectives. Diversity is not a\nguarantee of creativity: explain whose perspective is represented, missing, or\nstructurally disadvantaged. Use accessible participation modes and\npseudonymous participant IDs where appropriate.\n\nThe facilitator should disclose conflicts, avoid offering a preferred answer\nfirst, prevent senior members from dominating, and ask leaders to contribute\nafter the independent round.\n\n### 3. Generate independently\n\nGive everyone the same neutral prompt, constraints, and fixed time window.\nParticipants write ideas privately and in parallel before discussion. For each\nidea, capture:\n\n- a stable ID and one-sentence statement;\n- contributor ID(s) and stage (`independent`, `discussion`, or `post-check`);\n- origin (`human`, `AI-assisted`, `literature-inspired`, `mixed`, or `other`);\n- assumptions, predicted observations, uncertainties, and possible\n  disconfirming evidence;\n- source identifiers for literature-inspired ideas and tool\u002Fpurpose disclosure\n  for AI assistance.\n\nDo not show example solutions before this round unless examples are necessary;\nif they are, record them as potential anchors.\n\n### 4. Share without immediate evaluation\n\nUse round-robin or pooled silent sharing. Clarify wording without advocacy.\nPermit a private or anonymous channel. Ask each participant what is missing,\nwhat contradicts the dominant framing, and which idea became less obvious\nafter hearing the group.\n\n### 5. Cluster structurally\n\nGroup ideas by an explicit relation such as shared outcome, mechanism,\npopulation, scale, or method. Keep original IDs and text. Record merges and\nsplits. Similar wording is not proof of semantic equivalence; retain distinct\nideas when their assumptions, intervention, population, or predictions differ.\nSee `references\u002Ffacilitation_workflows.md`.\n\n### 6. Define transparent criteria\n\nBefore rating, define each criterion, direction, scale anchors, evidence\nneeded, conflicts, and explicit weights. Common dimensions include:\n\n- potential information gain and discriminating predictions;\n- relevance to the scoped question;\n- originality relative to the checked literature, not merely to the room;\n- feasibility, resources, and reversibility;\n- methodological rigor and vulnerability to bias;\n- ethics, safety, equity, dual-use, and regulatory burden;\n- value if the result is null or contradicts the favored mechanism.\n\nUse ranges or confidence labels where assessors are uncertain. Do not hide\nvetoes inside an averaged score. See `references\u002Fidea_evaluation.md`.\n\n### 7. Run adversarial review\n\nAssign a reviewer who did not originate each shortlisted idea. Ask:\n\n- What observation would make this idea wrong or uninformative?\n- Which alternative explanation fits the same predicted result?\n- What hidden dependency, measurement failure, confounder, or selection effect\n  could dominate?\n- Are authority, anchoring, group loyalty, publication incentives, or an\n  attractive technology driving preference?\n- Could this cause harm, worsen inequity, expose sensitive information, or\n  enable misuse?\n\nRecord the response, mitigation, residual uncertainty, and whether the idea was\nrevised—not just pass\u002Ffail.\n\n### 8. Check literature and evidence\n\nSearch authoritative databases, primary studies, methods guidance, negative\nresults, and adjacent fields. Verify every citation at its source. For each\nidea, record query\u002Fdate, sources screened, evidence for and against, and search\nlimits. Use statuses such as `not-checked`, `search-incomplete`,\n`support-located`, `challenge-located`, or `mixed`.\n\nAbsence from a bounded search does not establish novelty, and supportive\nliterature does not validate a new mechanism. Reopen one short independent\ngeneration round after the evidence check.\n\n### 9. Apply feasibility, rigor, and ethics gates\n\nBefore advancing an idea, identify the appropriate domain review:\n\n- For biomedical work, consider rigor of prior research, robust design,\n  relevant biological variables, and resource authentication. When NIH policy\n  applies, sex as a biological variable should be considered from the research\n  question through design, analysis, and reporting; justify a single-sex scope\n  with relevant evidence.\n- Route human-subjects, animal, biosafety, data-governance, export-control,\n  clinical, environmental, and other regulated work to the relevant office.\n- Screen life-science and enabling-technology ideas for dual-use or misuse\n  potential early. Current U.S. oversight is evolving; consult the institution\n  and current agency policy rather than relying on a static checklist.\n- Do not upload sensitive, unpublished, proprietary, controlled, or personal\n  information to an external AI service.\n\nAn ethics or feasibility concern may require redesign, controlled handling, or\nstopping. A high creativity score never overrides a gate.\n\n### 10. Decide and log\n\nThe accountable human decision owner records:\n\n- candidates considered and criteria\u002Fweights used;\n- raw ratings, uncertainty ranges, dissent, abstentions, and sensitivity\n  results;\n- literature and review dates;\n- gate outcomes and required approvals;\n- decision, rationale, rejected alternatives, unresolved risks, owner, and\n  revisit trigger.\n\nLabel the next action correctly: further search, consultation, simulation,\npilot design, protocol development, preregistration, or no action. If a\nconfirmatory study is planned, preregister hypotheses and analysis decisions\nbefore outcomes are known; report later deviations and exploratory work\ntransparently. Preregistration improves transparency but is not peer review,\nethical approval, or proof of validity.\n\n## Bias and failure controls\n\n- **Production blocking:** private parallel generation before oral discussion.\n- **Anchoring and design fixation:** no leader answer or AI examples until the\n  independent round; reopen generation after evidence review.\n- **Authority and status effects:** leader-last sharing, anonymous input,\n  independent ratings, and visible dissent.\n- **Groupthink:** assign a genuine alternative-generation role, invite outside\n  review, and document rejected options. Treat “groupthink” as a family of\n  risks, not a single universally established diagnosis.\n- **Evaluation apprehension:** separate contribution from attribution where\n  possible; critique ideas, not contributors.\n- **Premature convergence:** fixed divergence window followed by an explicit\n  transition and predeclared criteria.\n- **False precision:** use anchored scales, uncertainty ranges, sensitivity\n  analysis, and narrative review.\n- **Research-gap inflation:** record search boundaries and use “no direct\n  evidence located,” not “never studied.”\n- **AI hallucination or homogenization:** human-first ideation, provenance,\n  independent verification, multiple non-AI perspectives, and comparison for\n  suspiciously repeated frames. See `references\u002Fresponsible_ai.md`.\n\n## Optional local CLIs\n\nThe scripts are deterministic, standard-library utilities. They do not call a\nnetwork service, LLM, or scientific database and do not make scientific\nconclusions.\n\n```bash\npython scripts\u002Fsession_scaffold.py --help\npython scripts\u002Fvalidate_register.py --help\npython scripts\u002Fevaluate_matrix.py --help\n```\n\nCreate a session register:\n\n```bash\npython scripts\u002Fsession_scaffold.py \\\n  --session-id \"microbiome-01\" \\\n  --title \"Microbiome mechanism ideation\" \\\n  --question \"Which mechanisms could explain the scoped observation?\" \\\n  --participant P01 --participant P02 \\\n  --output session.json\n```\n\nValidate structure and provenance:\n\n```bash\npython scripts\u002Fvalidate_register.py session.json --output validation.json\n```\n\nCalculate a fully disclosed weighted matrix from CSV, including score intervals\nand one-at-a-time weight sensitivity:\n\n```bash\npython scripts\u002Fevaluate_matrix.py scores.csv \\\n  --config criteria.json \\\n  --weight-delta 0.10 \\\n  --output matrix.json\n```\n\nOutputs refuse symlinks and existing files unless `--force` is explicit; inputs\nand collection sizes are bounded. The validator checks structure, not truth.\nThe matrix preserves qualitative review and uncertainty and leaves\n`decision` null. Input formats and interpretation are documented in\n`references\u002Fidea_evaluation.md`.\n\n## Reference index\n\n- `references\u002Fbrainstorming_methods.md` — evidence-calibrated method selection,\n  nominal groups, Delphi, structured elicitation, and creative prompts.\n- `references\u002Ffacilitation_workflows.md` — ready-to-run individual, group, and\n  asynchronous session protocols plus provenance templates.\n- `references\u002Fidea_evaluation.md` — criteria, scoring formula, uncertainty,\n  sensitivity analysis, gates, and decision logs.\n- `references\u002Fresponsible_ai.md` — accountable AI assistance, confidentiality,\n  hallucination, homogenization, disclosure, dual-use, and integrity.\n- `references\u002Fsources.md` — dated primary studies and official guidance\n  consulted for this version.\n\n## Agent operating procedure\n\n1. **Check the environment.** Clarify the research goal, constraints (budget, time, ethics) and the decision the user needs to make.\n2. **Pin down the inputs.** Confirm formats, identifiers and parameters from the data or the user. Ask rather than guess any value that changes the result.\n3. **Run a small version first.** Propose a short shortlist of options with explicit assumptions before expanding any of them.\n4. **Execute the full task** using the instructions and references above.\n5. **Validate the result.** Assumptions are stated, alternatives are compared on explicit criteria, and statistical plans include power and analysis choices.\n6. **Report.** State what was run (versions, commands, parameters), what was checked, and what is still uncertain.\n\n| If this happens | Do this |\n|---|---|\n| A design choice depends on unknown effect sizes or costs | Present scenarios (optimistic, expected, pessimistic) instead of a single guess. |\n| A function, flag or endpoint in these instructions is missing in the installed version | Check the installed version's own documentation (`help()`, `--help`, official docs), adapt, and tell the user. Never invent an API. |\n| A required input, identifier or parameter is ambiguous | Ask the user, or state the assumption explicitly before running. |\n\n**Integrity rules**\n\n- Never fabricate results, parameters, identifiers, citations or statistics. If something cannot be run or verified, say so plainly.\n- Label speculation as speculation; separate established evidence from new hypotheses.\n- Treat version-specific details here as possibly outdated: confirm them against the official documentation for the installed version.\n- Ask before actions that cost money, consume shared GPUs or cloud quota, touch personal or patient data, or cannot be undone.\n\n## Related skills\n\n- `brainstorming-research-ideas`: Guides researchers through structured ideation frameworks to discover high-impact research directions.\n- `hypothesis-generation`: Formulate evidence-bounded scientific questions, candidate hypotheses, rival explanations, causal or associational claims, discriminating p…\n- `peer-review`: Prepare evidence-bounded, constructive peer-review drafts and structured manuscript assessments.\n","# Scientific Brainstorming\n\n## Purpose and boundaries\n\nUse this skill to create, organize, challenge, and transparently prioritize\ncandidate research directions. Treat every output as a **proposal**, not a\nfinding. Creativity methods can alter participation and idea yield, but no\nmethod universally improves originality, usefulness, or scientific validity.\nThe evidence base and its limits are summarized in\n`references\u002Fsources.md`.\n\nKeep these activities separate:\n\n- **Ideation** creates questions, mechanisms, alternatives, or study concepts.\n- **Evidence assessment** checks what reliable literature and data support.\n- **Hypothesis validation** requires observations, predictions, suitable\n  designs, analyses, and independent scrutiny; brainstorming cannot validate a\n  hypothesis.\n- **Ethics, biosafety, dual-use, regulatory, and institutional review** require\n  the relevant authorized reviewers. A brainstorm is never approval.\n- **Clinical advice** requires qualified clinicians and patient-specific\n  context. Do not turn research ideas into diagnosis or treatment guidance.\n\nFor an observation-led testable hypothesis, hand off to\n`hypothesis-generation`. For study architecture, use `experimental-design`;\nfor sample size, `statistical-power`; for existing evidence,\n`literature-review`; and for analysis, `statistical-analysis`.\n\n## Operating rules\n\n1. Label claims as **idea**, **assumption**, **prediction**, **located\n   evidence**, or **decision**. Never blur these categories.\n2. Generate independently before exposing participants to other people's or\n   AI-generated ideas. Face-to-face turn-taking can block production, and\n   examples can anchor later output.\n3. Preserve minority views, negative evidence, uncertainty, and abstentions.\n   Consensus is not truth and vote counts are not effect sizes.\n4. Record provenance without exposing confidential, personal, controlled, or\n   unpublished information.\n5. Define evaluation criteria and directions before scoring. Keep raw ratings,\n   reasons, ranges, and disagreement visible.\n6. Search the literature **after an initial independent round** when practical,\n   then deliberately reopen ideation. This reduces early anchoring without\n   mistaking an incomplete search for a research gap.\n7. Do not automatically select a “winner.” Scores are traceable decision aids;\n   qualitative judgment, uncertainty, feasibility, and ethics gates remain\n   controlling.\n\n## Reproducible workflow\n\n### 1. Scope the session\n\nWrite one focal question and record:\n\n- purpose, audience, decision owner, and time horizon;\n- in-scope and out-of-scope topics;\n- constraints that are real, assumed, negotiable, or unknown;\n- current knowledge, unresolved observations, and prohibited outputs;\n- whether human participants, animals, clinical care, sensitive data,\n  pathogens, controlled technologies, or environmental release could be\n  implicated.\n\nIf the request seeks patient-specific care, evasion of oversight, harmful\noptimization, or operationally enabling dual-use details, stop ideation and\nroute to the appropriate professional or institutional process.\n\n### 2. Diversify perspectives deliberately\n\nInvite relevant methodological, domain, implementation, statistical, safety,\nethics, stakeholder, and lived-experience perspectives. Diversity is not a\nguarantee of creativity: explain whose perspective is represented, missing, or\nstructurally disadvantaged. Use accessible participation modes and\npseudonymous participant IDs where appropriate.\n\nThe facilitator should disclose conflicts, avoid offering a preferred answer\nfirst, prevent senior members from dominating, and ask leaders to contribute\nafter the independent round.\n\n### 3. Generate independently\n\nGive everyone the same neutral prompt, constraints, and fixed time window.\nParticipants write ideas privately and in parallel before discussion. For each\nidea, capture:\n\n- a stable ID and one-sentence statement;\n- contributor ID(s) and stage (`independent`, `discussion`, or `post-check`);\n- origin (`human`, `AI-assisted`, `literature-inspired`, `mixed`, or `other`);\n- assumptions, predicted observations, uncertainties, and possible\n  disconfirming evidence;\n- source identifiers for literature-inspired ideas and tool\u002Fpurpose disclosure\n  for AI assistance.\n\nDo not show example solutions before this round unless examples are necessary;\nif they are, record them as potential anchors.\n\n### 4. Share without immediate evaluation\n\nUse round-robin or pooled silent sharing. Clarify wording without advocacy.\nPermit a private or anonymous channel. Ask each participant what is missing,\nwhat contradicts the dominant framing, and which idea became less obvious\nafter hearing the group.\n\n### 5. Cluster structurally\n\nGroup ideas by an explicit relation such as shared outcome, mechanism,\npopulation, scale, or method. Keep original IDs and text. Record merges and\nsplits. Similar wording is not proof of semantic equivalence; retain distinct\nideas when their assumptions, intervention, population, or predictions differ.\nSee `references\u002Ffacilitation_workflows.md`.\n\n### 6. Define transparent criteria\n\nBefore rating, define each criterion, direction, scale anchors, evidence\nneeded, conflicts, and explicit weights. Common dimensions include:\n\n- potential information gain and discriminating predictions;\n- relevance to the scoped question;\n- originality relative to the checked literature, not merely to the room;\n- feasibility, resources, and reversibility;\n- methodological rigor and vulnerability to bias;\n- ethics, safety, equity, dual-use, and regulatory burden;\n- value if the result is null or contradicts the favored mechanism.\n\nUse ranges or confidence labels where assessors are uncertain. Do not hide\nvetoes inside an averaged score. See `references\u002Fidea_evaluation.md`.\n\n### 7. Run adversarial review\n\nAssign a reviewer who did not originate each shortlisted idea. Ask:\n\n- What observation would make this idea wrong or uninformative?\n- Which alternative explanation fits the same predicted result?\n- What hidden dependency, measurement failure, confounder, or selection effect\n  could dominate?\n- Are authority, anchoring, group loyalty, publication incentives, or an\n  attractive technology driving preference?\n- Could this cause harm, worsen inequity, expose sensitive information, or\n  enable misuse?\n\nRecord the response, mitigation, residual uncertainty, and whether the idea was\nrevised—not just pass\u002Ffail.\n\n### 8. Check literature and evidence\n\nSearch authoritative databases, primary studies, methods guidance, negative\nresults, and adjacent fields. Verify every citation at its source. For each\nidea, record query\u002Fdate, sources screened, evidence for and against, and search\nlimits. Use statuses such as `not-checked`, `search-incomplete`,\n`support-located`, `challenge-located`, or `mixed`.\n\nAbsence from a bounded search does not establish novelty, and supportive\nliterature does not validate a new mechanism. Reopen one short independent\ngeneration round after the evidence check.\n\n### 9. Apply feasibility, rigor, and ethics gates\n\nBefore advancing an idea, identify the appropriate domain review:\n\n- For biomedical work, consider rigor of prior research, robust design,\n  relevant biological variables, and resource authentication. When NIH policy\n  applies, sex as a biological variable should be considered from the research\n  question through design, analysis, and reporting; justify a single-sex scope\n  with relevant evidence.\n- Route human-subjects, animal, biosafety, data-governance, export-control,\n  clinical, environmental, and other regulated work to the relevant office.\n- Screen life-science and enabling-technology ideas for dual-use or misuse\n  potential early. Current U.S. oversight is evolving; consult the institution\n  and current agency policy rather than relying on a static checklist.\n- Do not upload sensitive, unpublished, proprietary, controlled, or personal\n  information to an external AI service.\n\nAn ethics or feasibility concern may require redesign, controlled handling, or\nstopping. A high creativity score never overrides a gate.\n\n### 10. Decide and log\n\nThe accountable human decision owner records:\n\n- candidates considered and criteria\u002Fweights used;\n- raw ratings, uncertainty ranges, dissent, abstentions, and sensitivity\n  results;\n- literature and review dates;\n- gate outcomes and required approvals;\n- decision, rationale, rejected alternatives, unresolved risks, owner, and\n  revisit trigger.\n\nLabel the next action correctly: further search, consultation, simulation,\npilot design, protocol development, preregistration, or no action. If a\nconfirmatory study is planned, preregister hypotheses and analysis decisions\nbefore outcomes are known; report later deviations and exploratory work\ntransparently. Preregistration improves transparency but is not peer review,\nethical approval, or proof of validity.\n\n## Bias and failure controls\n\n- **Production blocking:** private parallel generation before oral discussion.\n- **Anchoring and design fixation:** no leader answer or AI examples until the\n  independent round; reopen generation after evidence review.\n- **Authority and status effects:** leader-last sharing, anonymous input,\n  independent ratings, and visible dissent.\n- **Groupthink:** assign a genuine alternative-generation role, invite outside\n  review, and document rejected options. Treat “groupthink” as a family of\n  risks, not a single universally established diagnosis.\n- **Evaluation apprehension:** separate contribution from attribution where\n  possible; critique ideas, not contributors.\n- **Premature convergence:** fixed divergence window followed by an explicit\n  transition and predeclared criteria.\n- **False precision:** use anchored scales, uncertainty ranges, sensitivity\n  analysis, and narrative review.\n- **Research-gap inflation:** record search boundaries and use “no direct\n  evidence located,” not “never studied.”\n- **AI hallucination or homogenization:** human-first ideation, provenance,\n  independent verification, multiple non-AI perspectives, and comparison for\n  suspiciously repeated frames. See `references\u002Fresponsible_ai.md`.\n\n## Optional local CLIs\n\nThe scripts are deterministic, standard-library utilities. They do not call a\nnetwork service, LLM, or scientific database and do not make scientific\nconclusions.\n\n```bash\npython scripts\u002Fsession_scaffold.py --help\npython scripts\u002Fvalidate_register.py --help\npython scripts\u002Fevaluate_matrix.py --help\n```\n\nCreate a session register:\n\n```bash\npython scripts\u002Fsession_scaffold.py \\\n  --session-id \"microbiome-01\" \\\n  --title \"Microbiome mechanism ideation\" \\\n  --question \"Which mechanisms could explain the scoped observation?\" \\\n  --participant P01 --participant P02 \\\n  --output session.json\n```\n\nValidate structure and provenance:\n\n```bash\npython scripts\u002Fvalidate_register.py session.json --output validation.json\n```\n\nCalculate a fully disclosed weighted matrix from CSV, including score intervals\nand one-at-a-time weight sensitivity:\n\n```bash\npython scripts\u002Fevaluate_matrix.py scores.csv \\\n  --config criteria.json \\\n  --weight-delta 0.10 \\\n  --output matrix.json\n```\n\nOutputs refuse symlinks and existing files unless `--force` is explicit; inputs\nand collection sizes are bounded. The validator checks structure, not truth.\nThe matrix preserves qualitative review and uncertainty and leaves\n`decision` null. Input formats and interpretation are documented in\n`references\u002Fidea_evaluation.md`.\n\n## Reference index\n\n- `references\u002Fbrainstorming_methods.md` — evidence-calibrated method selection,\n  nominal groups, Delphi, structured elicitation, and creative prompts.\n- `references\u002Ffacilitation_workflows.md` — ready-to-run individual, group, and\n  asynchronous session protocols plus provenance templates.\n- `references\u002Fidea_evaluation.md` — criteria, scoring formula, uncertainty,\n  sensitivity analysis, gates, and decision logs.\n- `references\u002Fresponsible_ai.md` — accountable AI assistance, confidentiality,\n  hallucination, homogenization, disclosure, dual-use, and integrity.\n- `references\u002Fsources.md` — dated primary studies and official guidance\n  consulted for this version.\n\n## Agent operating procedure\n\n1. **Check the environment.** Clarify the research goal, constraints (budget, time, ethics) and the decision the user needs to make.\n2. **Pin down the inputs.** Confirm formats, identifiers and parameters from the data or the user. Ask rather than guess any value that changes the result.\n3. **Run a small version first.** Propose a short shortlist of options with explicit assumptions before expanding any of them.\n4. **Execute the full task** using the instructions and references above.\n5. **Validate the result.** Assumptions are stated, alternatives are compared on explicit criteria, and statistical plans include power and analysis choices.\n6. **Report.** State what was run (versions, commands, parameters), what was checked, and what is still uncertain.\n\n| If this happens | Do this |\n|---|---|\n| A design choice depends on unknown effect sizes or costs | Present scenarios (optimistic, expected, pessimistic) instead of a single guess. |\n| A function, flag or endpoint in these instructions is missing in the installed version | Check the installed version's own documentation (`help()`, `--help`, official docs), adapt, and tell the user. Never invent an API. |\n| A required input, identifier or parameter is ambiguous | Ask the user, or state the assumption explicitly before running. |\n\n**Integrity rules**\n\n- Never fabricate results, parameters, identifiers, citations or statistics. If something cannot be run or verified, say so plainly.\n- Label speculation as speculation; separate established evidence from new hypotheses.\n- Treat version-specific details here as possibly outdated: confirm them against the official documentation for the installed version.\n- Ask before actions that cost money, consume shared GPUs or cloud quota, touch personal or patient data, or cannot be undone.\n\n## Related skills\n\n- `brainstorming-research-ideas`: Guides researchers through structured ideation frameworks to discover high-impact research directions.\n- `hypothesis-generation`: Formulate evidence-bounded scientific questions, candidate hypotheses, rival explanations, causal or associational claims, discriminating p…\n- `peer-review`: Prepare evidence-bounded, constructive peer-review drafts and structured manuscript assessments.\n",{"name":5,"license":15,"metadata":60,"description":7,"compatibility":65},{"tags":61,"version":62,"category":63,"maintainer":64},"brainstorming, research ideation, research directions, scientific method","1.2","ideation-and-design","Kalaris Labs","Core guidance works in any Agent Skills-compatible host. Optional bundled CLIs require Python 3.11+ and use only the standard library; they make no network or LLM calls and require no credentials.","main","0da4485785ee599388900be50db1635489d53056",".agents\u002Fskills\u002Fkal-scientific-brainstorming","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6","https:\u002F\u002Fdisha6.6.thenitishkr.in\u002F","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh","www.thenitishkr","thenitishkr | AI • Cyber-security • Research\r\n","2026-11-08 03:33:25",{"source":8,"skillName":5,"commands":76,"prompt":86,"zipAvailable":87},[77,80,83],{"tool":78,"command":79},"npx","npx skills add Tashima-Tarsh\u002FDisha6.6 --skill scientific-brainstorming",{"tool":81,"command":82},"bunx","bunx skills add Tashima-Tarsh\u002FDisha6.6 --skill scientific-brainstorming",{"tool":84,"command":85},"pnpm","pnpm dlx skills add Tashima-Tarsh\u002FDisha6.6 --skill scientific-brainstorming","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fkal-scientific-brainstorming\u002FSKILL.md",true,[89,95,99,103,107,111,117,121,125],{"path":90,"resourceType":91,"resourceTypeDesc":92,"sizeBytes":93,"sourceHtmlUrl":94},"references\u002Fbrainstorming_methods.md","REFERENCE","参考文档",12128,"https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fkal-scientific-brainstorming\u002Freferences\u002Fbrainstorming_methods.md",{"path":96,"resourceType":91,"resourceTypeDesc":92,"sizeBytes":97,"sourceHtmlUrl":98},"references\u002Ffacilitation_workflows.md",10016,"https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fkal-scientific-brainstorming\u002Freferences\u002Ffacilitation_workflows.md",{"path":100,"resourceType":91,"resourceTypeDesc":92,"sizeBytes":101,"sourceHtmlUrl":102},"references\u002Fidea_evaluation.md",9995,"https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fkal-scientific-brainstorming\u002Freferences\u002Fidea_evaluation.md",{"path":104,"resourceType":91,"resourceTypeDesc":92,"sizeBytes":105,"sourceHtmlUrl":106},"references\u002Fresponsible_ai.md",9721,"https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fkal-scientific-brainstorming\u002Freferences\u002Fresponsible_ai.md",{"path":108,"resourceType":91,"resourceTypeDesc":92,"sizeBytes":109,"sourceHtmlUrl":110},"references\u002Fsources.md",16783,"https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fkal-scientific-brainstorming\u002Freferences\u002Fsources.md",{"path":112,"resourceType":113,"resourceTypeDesc":114,"sizeBytes":115,"sourceHtmlUrl":116},"scripts\u002F_common.py","SCRIPT","脚本",9560,"https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fkal-scientific-brainstorming\u002Fscripts\u002F_common.py",{"path":118,"resourceType":113,"resourceTypeDesc":114,"sizeBytes":119,"sourceHtmlUrl":120},"scripts\u002Fevaluate_matrix.py",18463,"https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fkal-scientific-brainstorming\u002Fscripts\u002Fevaluate_matrix.py",{"path":122,"resourceType":113,"resourceTypeDesc":114,"sizeBytes":123,"sourceHtmlUrl":124},"scripts\u002Fsession_scaffold.py",7930,"https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fkal-scientific-brainstorming\u002Fscripts\u002Fsession_scaffold.py",{"path":126,"resourceType":113,"resourceTypeDesc":114,"sizeBytes":127,"sourceHtmlUrl":128},"scripts\u002Fvalidate_register.py",21169,"https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fkal-scientific-brainstorming\u002Fscripts\u002Fvalidate_register.py",[130,159,182,207,231,254],{"skillNo":131,"name":132,"nameZh":6,"description":133,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":134,"categories":135,"keywords":139,"publishedAt":55},"01M6QRD07D4TGNDK6VJ4EHF8BV","youtube-full","Use when the user needs YouTube transcripts, video search, channel browsing, playlist extraction, or content monitoring. Trigger phrases: 'get the transcript for', 'search YouTube for', 'what are the latest videos on', 'list this playlist', 'monitor this channel', or any request involving a YouTube URL, video ID, or @handle. Do NOT use for downloading video or audio files, YouTube engagement data (likes, comments), or private\u002Fage-restricted videos.","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fyoutube-full\u002FSKILL.md",[136],{"categoryNo":137,"categoryName":138,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000016","图像与多媒体",[140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,43,156,157,158],"youtube full","youtube","full","transcript","search","monitoring","data","Video","Audio","list","files","do","get","age","trigger","URL","browsing","request","youtube transcripts",{"skillNo":160,"name":161,"nameZh":6,"description":162,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":163,"categories":164,"keywords":165,"publishedAt":55},"01M6QRCJ3112VYPA97YG7KGMC2","x-twitter-growth","X\u002FTwitter growth engine for building audience, crafting viral content, and analyzing engagement. Use when the user wants to grow on X\u002FTwitter, write tweets or threads, analyze their X profile, research competitors on X, plan a posting strategy, or optimize engagement. Complements social-content (generic multi-platform) with X-specific depth: algorithm mechanics, thread engineering, reply strategy, profile optimization, and competitive intelligence via web search.","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fx-twitter-growth\u002FSKILL.md",[],[166,167,168,53,144,169,170,171,172,173,174,175,176,177,46,178,179,180,181,43],"twitter","x twitter growth","growth","content","engineering","plan","web search","competitors","analyze","engine","write","strategy","platform","grow","posting","competitive",{"skillNo":183,"name":184,"nameZh":6,"description":185,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":186,"categories":187,"keywords":189,"publishedAt":55},"01M6QRBN3EHQNJY42RERP7A149","write-a-skill","Create new agent skills with proper structure, progressive disclosure, and bundled resources. Use when user wants to create, write, build, or author a new skill.","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fwrite-a-skill\u002FSKILL.md",[188],{"categoryNo":27,"categoryName":28,"taxonomyType":21,"taxonomyTypeDesc":22},[190,176,191,192,193,194,195,196,46,197,198,66,199,200,201,202,203,204,205,206],"write a skill","github","create","https","productivity","matt pocock","direct","example","build","new skill","structure","new agent","resources","with","tree","progressive disclosure","author",{"skillNo":208,"name":209,"nameZh":6,"description":210,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":211,"categories":212,"keywords":213,"publishedAt":55},"01M6QRB6G8P33PNGAF9Y69EJ9T","wrap-up","Close out a launched Claude Managed Agent — recap every primitive the founder now owns, regenerate the single-file overview page, and suggest the next 1-2 upgrades. Use when the user says \"wrap up\", \"close this out\", \"what do I own now\", \"give me the summary\", \"recap the agent\", or when the orchestrator routes phase=wrap-up. primitives_inventory.py tables everything owned (agent, environment, session, memory, outcome, deployment); overview_page.py regenerates a self-contained .\u002Fmy-agent\u002Fagent-overview.html; upgrade_suggester.py ranks the next moves from recorded deferrals plus standing hardening steps. Companion to run-without-you; the last stop before phase=done.","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fwrap-up\u002FSKILL.md",[],[214,215,216,217,218,219,220,221,222,46,223,151,224,225,226,227,228,229,44,230],"wrap up","up","wrap","gemini","deployment","run","file","hardening","closeout","stop","upgrade","founder","close","companion","you","tables","phase",{"skillNo":232,"name":233,"nameZh":6,"description":234,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":235,"categories":236,"keywords":238,"publishedAt":55},"01M6QRA5AYC1RBFZ39GTWGS3N8","workflow-builder","Design and write deterministic multi-agent workflow scripts (.js files in .claude\u002Fworkflows\u002F) for Claude Code's Workflow tool. Use when a user wants to build, create, author, scaffold, or run a custom Claude Code workflow, orchestrate sub-agents (fan-out, pipeline, loop, judge-panel), or automate a repeatable multi-step task across fresh-context agents.","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fworkflow-builder\u002FSKILL.md",[237],{"categoryNo":27,"categoryName":28,"taxonomyType":21,"taxonomyTypeDesc":22},[239,240,241,47,242,192,219,243,244,245,246,247,176,248,150,249,250,251,252,253],"workflow","workflow builder","builder","workflows","code","orchestrate","pipeline","loop","multi-agent","context","across","fresh","scripts","judge","claude code workflow",{"skillNo":255,"name":256,"nameZh":6,"description":257,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":258,"categories":259,"keywords":260,"publishedAt":55},"01M6QR7QMCZESKQA0ERW1E657M","weekly-review","Use when someone wants to run a weekly review, close open loops, audit stalled projects and commitments, get their system back to trusted, restart a lapsed review habit, or says \"\u002Fcs:weekly-review\". Walks David Allen's three-phase loop — GET CLEAR, GET CURRENT, GET CREATIVE — with deterministic scripts that inventory open loops, gate the checklist with named gaps, and score commitment health 0-100.","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fweekly-review\u002FSKILL.md",[],[261,262,263,264,265,266,267,268,269,246,270,271,272,273,274,275,152,276,277,230],"review","weekly review","weekly","deterministic scripts","reflect","feeds","inventory","open","team","projects","project-management","scanner","conversation","back","project","sprint","cadence"]