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disease-market-sizing-orchestration

Cross-disease market sizing entry skill. Triggered by user one-liner like "做 X 市场调研" or "Run market sizing for X". Auto-runs 5 phase pipeline: contract → stratify → recall → compose-audit → bundle. Hard-gates on Cite-or-Block: every fact claim must carry citation anchor verifiable against sources/. Resumable mid-run.

インストール方法を見る

含まれるファイル(2)

  • SKILL.md7.7 KB
  • scripts/orchestrator.py43.2 KB

SKILL.md(原文)

インストールする前に、エージェントに与えられる指示の中身を確認できます。

Disease Market Sizing Orchestration

Phase Sequence

  1. contract-elicitor → .cache/<slug>/contract.json
  2. disease-stratifier → .cache/<slug>/staging.json
  3. Evidence Recall (inline 7 retrieval + A1' guidelines) → .cache/<slug>/sources/
  4. Compose-then-Audit double-turn (≤ 3 turns) → output/<slug>/report_raw.html draft + verification_report.json
  5. Bundle + Design + TOC → output/<slug>/report_standalone.html + report_data.xlsx + delivery_manifest.json (PDF only when explicitly requested)

Production Non-Negotiables

  • Production smoke must start from plugin-session bootstrap evidence: .cache/<slug>/agent_run_context.json with mode=agent-plugin-bootstrap, agent_runtime=codex_or_claude_code_plugin_session, and requires_user_api_key=false.
  • Production smoke must continue through the reusable plugin continuation entrypoint and write .cache/<slug>/plugin_session_invocation.json; direct temporary callback scripts are not valid production-pass evidence.
  • Disease-specific callbacks may adapt inputs or act as deterministic test doubles, but a production pass is invalid if a callback directly writes the final report or market model in place of reusable skills.
  • Do not use one-off scripts/generate_*smoke_report.py generators or handwritten skill_run_log.json entries as production evidence.
  • Every production run must write deliverables under a dedicated output/<slug>/ directory; do not overwrite root-level or previous-run report files.
  • The orchestrator must write .cache/<slug>/orchestration_trace.json with entrypoint=disease-market-sizing-orchestration, agent_runtime=codex_or_claude_code_plugin_session, a non-empty run_id, status=ok, and ok phases for disease-stratifier, evidence-recall, cite-bound-content-generator, content-verification-layer, mermaid-renderer, and report-bundle-builder.
  • Downstream skill_run_log.json entries must be emitted by the same orchestrator run with generated_by=disease-market-sizing-orchestration and matching run_id; logs without this trace are invalid for production smoke.
  • Evidence recall must use multiple retrieval channels and must not stop at the >50 literature hard floor when the disease area is source-rich; respiratory IFI-like topics should normally exceed 100 saved literature sources.
  • Retrieval channel entries must carry provenance. pubmed-search, europepmc-search, clinical-trials-search, bioc-fulltext-fetch, pubtator-entity-search, medical-evidence-grading, and market recall entries in skill_run_log.json need generated_by / run_id plus a link to the orchestration run that consumed them.
  • Fetched required channels must enter the body analysis. If ClinicalTrials.gov/AACT or PubTator/entity sources are saved under .cache/<slug>/sources/, production verification must require matching [nct:...] / [aact:...] / [pubtator:...] anchors or a recorded pre-verification exclusion reason.
  • Evidence grading must affect source utilization. D-grade PubMed records may stay in background/reference appendices, but production smoke must block D-grade PMIDs used as analytical body anchors. Grading may use publication types and conservative study-design terms found in the saved PubMed title/abstract, but must not use external disease/drug dictionaries.
  • Production compose must call cite-bound-content-generator.compose(..., return_report=True) and pass ComposedReport.sub_pages / toc_anchors to report-bundle-builder.
  • Production compose must be agent-authored or reusable-skill-authored. The preferred plugin contract is .cache/<slug>/agent_authored_compose.json read by an adapter-only callback. compose_authoring_provenance.json must prove authoring_mode=agent_authored_compose_artifact and generated_by=codex_or_claude_code_plugin_executor; callback-authored HTML/formula/Mermaid is invalid.
  • The agent-authored compose artifact must be a formal plugin phase, not an unexplained prewritten JSON. Pair it with .cache/<slug>/agent_authored_compose_manifest.json containing prompt digest, source tree digest, artifact checksum, current source slug, and validation time; production smoke must block missing or checksum-mismatched manifests.
  • Production continuation accepts only final agent-authored compose artifacts: provenance.phase=final, current source_run_slug, reader_layer.html, audit_layer.evidence_support_table, audit_layer.reader_to_audit_mapping, substantive claims, current-run source_usage, formula_dag_ref, and mermaid_sources. Precompose artifacts are checkpoint evidence only and must block before smoke.
  • Production verification must fail closed with semantic verification, source manifest provenance when available, and persisted anchor_audit metrics in verification_report.json.
  • When semantic verification writes .cache/<slug>/semantic_audit_log.jsonl, verification_report.json must link that log and record matching non-empty entry counts. Otherwise the audit is not explainable and production smoke must fail.
  • Production verification must block formula/model evidence citation dominance and evidence-digest-matrix body dominance. These are signs that the pipeline emitted a calculation dump or source-processing log instead of a V25-level report.
  • Drug citation verification must scan the reader HTML plus audit-layer claims together. Audit claims alone are not enough because a reader-layer report can contain drug names that the audit layer omits; production smoke blocks zero drug mentions when antifungal drug names are visible.
  • Production verification must require core evidence synthesis in the body when recall is broad. A V25-level report needs thematic literature digestion across cohorts/pathways/LP decisions plus growth drivers, payer/access impact, LP ranking methodology, and methods/sensitivity depth.
  • Mermaid rendering must write .cache/<slug>/mermaid_render_manifest.json; decision-tree PNGs without this manifest are invalid. The extractor must match any <div> whose class list contains mermaid, including tags with attributes such as data-chart; HTML attributes cannot cause the renderer to skip flowchart blocks.
  • Each Mermaid manifest block must self-describe graph quality with edge_count, decision_node_count, numeric_evidence_count, and cross_point_count. A manifest that only says render ok/path/source is insufficient for V25-level auditability.
  • A compose artifact that fails verification is a failed draft only. It must not be treated as a latest deliverable candidate until verification_report.verdict=ok, rendered Mermaid assets exist, and bundle/smoke gates pass.
  • Before production smoke audit, standalone HTML should embed the current source/citation/verification corpus as an audit package when the project provides scripts/embed_source_corpus.py; size parity must come from useful provenance, not fake padding images.
  • Production smoke audit is mandatory before reporting a pass against V25. Count/size/image metrics are insufficient without sub-page depth, meaningful body source utilization, numeric evidence support, and Mermaid cross-point source checks.

Functions

  • run(user_one_liner, project_root, callbacks, *, report_output_dir=None, v25_reference_html=None, require_production_smoke_audit=None) -> manifest dict

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

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日本語の概要は準備中です。原文の説明を表示しています。

EthanYoQ/Skill-hub112026年10月5日 更新

使用 AACT (Aggregate Analysis of ClinicalTrials.gov) PostgreSQL 数据库进行大批量临床试验历史分析与数据挖掘。触发场景包括:AACT 查询、临床试验批量分析、PostgreSQL 试验数据、全量 NCT 检索、试验数据挖掘、clinical trials data warehouse、疾病领域全景分析、设计相似试验匹配、跨年度试验趋势聚合。本 skill 通过 SQL 接口处理百万级试验记录,支持云端公共 PostgreSQL 服务(aact-db.ctti-clinicaltrials.org)和每日 dump 本地还原两种连接方式,自动检测并优先使用本地高性能模式。

日本語の概要は準備中です。原文の説明を表示しています。

EthanYoQ/Skill-hub112026年10月5日 更新

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日本語の概要は準備中です。原文の説明を表示しています。

EthanYoQ/Skill-hub112026年10月5日 更新

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日本語の概要は準備中です。原文の説明を表示しています。

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