AI Agent Workflow Collection

Use when work produces a deliverable, factual claim, dataset, code change, decision, publication, deployment, automation, or irreversible action whose failure would matter; when the user asks for a quality gate, acceptance criteria, QA, completeness, validation, audit, evidence, preflight, release readiness, or a definition of done; or before claiming completion on medium- or high-risk work. Automatically decide whether a formal gate is warranted, derive task-specific pass/fail criteria, gather evidence, and block unsupported completion. Make sure to use this skill even when the user does not say "quality gate" if consequential work needs acceptance criteria or completion evidence. Skip formal gating for trivial, reversible, low-impact requests.

Use when operating Alibaba Cloud through a custom OpenAPI MCP server, especially for SWAS deployment workflows: OAuth setup, selector updates, missing-tool diagnosis, deployment permission closure, and pre-deploy verification.

Use when managing Alibaba Cloud Simple Application Server (SWAS OpenAPI 2020-06-01) resources end-to-end, including querying instances, starting/stopping/rebooting, executing commands (cloud assistant), managing disks/snapshots/images, firewall rules/templates, key pairs, tags, monitoring, and lightweight database operations.

Phase 4 Turn 1 of disease-market-sizing-orchestration. Generates report HTML with mandatory citation anchors per fact claim. Reads contract + staging + sources/. Every drug/treatment/stat/recommendation MUST carry [pmid:.../guideline:.../nct:...] anchor that resolves to an actual file in sources/. NEVER hardcode drug names or numerical claims. Use AFTER evidence recall (phase 3), BEFORE content-verification-layer (Turn 2).

Use when the user asks about clinical trial status, recruitment, trial phases (1-4), outcomes, trial registries, NCT IDs, or any query on ClinicalTrials.gov data. Real-time lookup of trial protocols, results, enrollment status, sponsors, and interventions via ClinicalTrials.gov API v2. Handles: 'What is the latest status of NCT04368728?', 'Find Phase 3 BTK inhibitor trials currently recruiting', 'Show primary outcomes for this trial', 'Which trials use Pembrolizumab?', 'Recruiting trials for Acute Myeloid Leukemia'. Real-time, single-query ≤1000 records. Delegates to: aact-bulk-trials (bulk >10k), pubmed-eutils (PubMed literature), medical-evidence-grading (GRADE rating), evidence-appendix-sync (reference formatting).

Phase 4 Turn 2 of disease-market-sizing-orchestration (A6' upgrade). Audits compose output via Cite-or-Block strict — every anchor must reverse-resolve to an actual file in sources/, every fact claim must have an anchor. Returns verdict (ok/blocked) + violations list. Use AFTER cite-bound-content-generator (Turn 1), BEFORE report-bundle-builder (phase 5).

Use FIRST when user requests any disease market sizing report (e.g. "做 X 市场调研" / "Run market sizing for X"). Elicits 4-6 startup questions (evidence window, geography, international comparison, report depth, user role) and writes .cache/ /contract.json. ALWAYS invoke before disease-market-sizing-orchestration. Validates against schemas/contract.schema.json.

Use for unattended QQ controlled frontend lockcheck runs, failure classification, bounded autofix decisions, and packaging-gate evidence in this repository. Trigger when the task is to automate full frontend batch tests, run controlled lockcheck loops, classify QQ truth-audit failures, or decide whether an autofix may continue without touching protected core rules.

Use when generating Mermaid clinical decision trees that must include Leverage Point (LP) annotations at specific stage nodes (not just text-only in summary box). Trigger on tasks like "决策树标 LP 切入点", "在流程图节点上 标注杠杆点", "决策树嵌入 LP", or any flowchart that has accompanying LP strategy. Library-agnostic — works for hematology IFI, respiratory ICU, oncology, cardiology, any disease where decision tree + leverage points must be visually integrated. Inherits from L0 market-sizing-mece-foundation §10 LP decision diagram spec. LP annotations sourced from pubtator-entity-search.entitysearch() (drug-disease relations).

Use when generating multi-page HTML market research reports with main page + N disease sub-pages structure (e.g. hematology IFI, respiratory ICU fungal, oncology immunotherapy). Provides shared CSS design system, sticky topnav, hero gradients per market tier, disease-card grid, lp-callout boxes, cross-page link integrity, and (v1.2 · 2026-04-25) standalone single-file HTML-only 交付 pipeline (buildstandalone.py base64 内嵌, v1.2 PDF 已停用, 仅交付 HTML 单文件即终态交付物). Triggers on "生成 HTML 报告主页+子页", "市场调研 HTML 模板", "决策树跳转 网格", "可分享交付物 / 双击可读 / 邮件附件", or any multi-page disease market sizing HTML output. Inherits from L0 market-sizing-mece-foundation §11 visual specs (MRR alignment). After generating HTML pages, downstream skill is report-bundle-builder for single-file delivery.

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.

Phase 2 of disease-market-sizing-orchestration. Discovers disease-specific stratification dimensions by citing guideline sections from sources/guidelines/. ALWAYS uses cite-based discovery — NEVER built-in stratification templates. Output: .cache/ /staging.json. Use AFTER contract-elicitor and AFTER A1' guidelines fetch.

Use when finalizing market research report appendix C (参考文献清单) before delivery; Use when verifying evidence/ folder PMID coverage matches the report's reference list; Use when generating 附录 C / Appendix C from a populated evidence/ directory; Use when reviewing a delivered report for PMID 漏列 / 幻觉 / 等级缺失. Trigger keywords: 市场调研报告交付前 / 附录 C 生成 / 参考文献清单 / evidence 同步 / PMID 双向校验 / 文献附录核对. Library-agnostic — works for hematology IFI, respiratory ICU 真菌, oncology, cardiology, ICU sepsis, any evidence-driven medical/pharmaceutical market research report where evidence/.md and 报告附录 C must stay bidirectionally synchronized. Inherits L0 market-sizing-mece-foundation §evidence-driven pattern. v1.1 (2026-04-25):基于血液科 v2.5.2 复盘,新增 Iron Law 5(每条 PMID 必含 5 项元数据, 零裸数字)与 Iron Law 6(PMID 主题相关性核查,防关键词污染),配套 SOP Step 6/7/8 与失败模式 9/10。 v1.2 (2026-04-25):基于血液科 v2.5.3 复盘,新增 Iron Law 7(evidence/ 内部标注 vs PubMed 真实文献冲突时,信 PubMed 删 evidence/ 错误,禁止"保留 + 警告"妥协), 确立数据源信任优先级链(PubMed > 期刊官网 > NMPA > evidence 笔记 > LLM 推断), 配套 SOP Step 9(PMID 内外冲突核查)与失败模式 11(zombie warnings)。 PMID source of truth: pubmed-eutils.efetchabstracts() cache; do NOT use pubmed-search (Valyu, deprecated).

Create or refresh screenshot-led product hero images, localized interface captures, and image-led README sections for GitHub repositories. Use whenever a user asks to make a GitHub README show a GUI product visually, add software screenshots, design a README hero image, reproduce a screenshot-collage reference, or prepare repository-bound visual documentation. For GUI applications, default to the deterministic product-proof split hero with real software screenshots unless the user explicitly requests another visual direction.

Use when auditing, planning, measuring, or implementing GitHub repository discoverability work, including README/READMEzh SEO review, Description or Topics proposals, release copy review, search ranking tracking, competitor or alternative term handling, Baidu/Bing/AI search channel assessment, site asset readiness, or portfolio rollout.

Use when running recurring GitHub repository SEO evaluation, comparing broad Chinese search visibility over time, producing metrics evidence, weekly summaries, manual Baidu/Bing/AI search tasks, or deciding whether an SEO experiment should trigger another optimization round.

Use when user requests medical/pharmaceutical market sizing, Chinese clinical pathway reconstruction, patient flow waterfall charts, drug usage composition analysis, unmet need quantification, prescription share estimation, competitive landscape for antifungals/oncology/ICU drugs, or says things like "估算某市场多大", "画个诊疗路径图", "做个 market sizing", "分析用药占比". Also triggers on disease×department×drug class combinations (血液科 IFI, 呼吸科真菌, ICU真菌, 肿瘤免疫). Works for 中立第三方 or 特定产品策略 stances. Supports Agent Teams as the foundation for market research sub-agents. [Domain reference] Generic flow now lives in disease-market-sizing-orchestration + 7 retrieval skills.

L0 foundation for all medical market-sizing tasks. Use this skill whenever a task involves MECE department/age/region/indication slicing, evidence grading, drug-target three-state activity matrix, or cross-department patient-flow sizing. Trigger phrases: "市场多大", "market sizing", "用药占比", "竞品格局", "患者流". Disease-specific L1 skills (ifi-market-sizing-skill, etc.) must reference this first.

Retrieves scientific papers from PubMed and creates plain-language research summaries. Use when users ask about medical research, scientific studies, clinical trials, disease treatments, or want to understand recent scientific literature on any biomedical topic.

Audit and safely clean local project repositories or workspaces with excessive generated files, old project residue, dirty Git status, stale worktrees, build caches, duplicate nested projects, confusing roots, or agent context/token waste. Use when the user asks to organize a repo, remove useless files, classify valid project files, clean untracked/deleted files, reduce disk usage, migrate from an old repo layout to a canonical project, or prepare a repository for future Codex/agent work.

Search PubMed and retrieve structured biomedical literature with PMID lists, abstracts, MeSH terms, publication types, and PMC IDs for full-text access. Use this skill whenever the user mentions PubMed, PMID, MeSH terms, Clinical Queries, NCBI, E-utilities, or needs evidence for systematic reviews, RCT searches, drug-disease research, mechanism mapping, market research, or clinical evidence gathering. Make sure to use at the START of any medical evidence pipeline (diagnostic queries, prognosis, therapy effectiveness, observational studies, real-world evidence, clinical guidelines). Outputs structured records (title, abstract, journal, year, MeSH, publication type, PMID↔PMCID mapping)—does NOT perform semantic reranking (delegated to medical-evidence-grading), full-text extraction (delegated to bioc-fulltext-fetch), or entity annotation (delegated to pubtator-entity-search). Free official NCBI API covering 37M+ biomedical records. Cross-platform (Claude Code / Codex / Gemini CLI) with zero external SDK dependencies.

Build, rebuild, audit, and compare QQ mailbox invoice ground truth datasets for this repository using the existing truth-building scripts and evidence artifacts. Use when the task is to generate a QQ mailbox ground truth set, validate QQ batch output against truth, review QQ mailbox invoice evidence, or investigate QQ-specific invoice extraction pitfalls in this project.

Manage Agent Skills across Codex, Claude Code, local/global/project roots, Skill Hubs, GitHub, skills.sh, and managed plugin caches. Use when searching, installing, updating, upgrading, syncing, merging, auditing, deduplicating, cleaning, packaging, publishing, validating, resolving upstream sources, choosing runtime or scope, or working with skill-upgrade, checkupstreams.ps1, Claude Code plugins, skills-lock.json, or skill-upstreams.json. Do not use for ordinary software-package upgrades or non-skill file cleanup.

Run evidence-disciplined Xiaohongshu social listening for foreign pharma employee pain points. Use when the user asks to collect and analyze notes/comments about pharma companies, aliases, roles, compliance, medical affairs, or AI topics, then produce a cited and scored sampled-insight report. Includes independent-evidence counting, MediaCrawler batch collection, and CAPTCHA/461 fallback to Agent Reach/OpenCLI.