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Implement event-driven communication using BlueprintEventBus for cross-module coordination. Use this skill when modules need to communicate without tight coupling, broadcasting domain events (task.created, member.added), subscribing to events with proper lifecycle management, and implementing event-driven workflows. Ensures events follow naming conventions ([module].[action]), include Blueprint context, and use takeUntilDestroyed() for automatic cleanup.

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Create components using ng-alain (@delon/abc) and ng-zorro-antd UI libraries. Use this skill when building enterprise UI features with ST (Simple Table), SF (Schema Form), ACL (Access Control), PageHeader, ReuseTab, and other @delon components. Ensures proper integration with ng-alain architecture, theming system, responsive layouts, and accessibility standards while following Angular 20 patterns.

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Trigger for: Labor-Hour IGCE, LH estimate, Time-and-Materials IGCE, T&M estimate, burdened hourly rate, burden multiplier, labor-category ceiling hours, materials estimate, proposed LH/T&M rate validation, price-reasonableness memo, or fair-and-reasonable analysis. Build auditable LH/T&M estimates using BLS OEWS wages, explicit burden multipliers, GSA CALC+ positioning, GSA Per Diem travel, and separately priced T&M materials. Do NOT use for FFP, cost-reimbursement, grants, or cooperative agreements. Requires the bls-oews, gsa-calc, and gsa-perdiem MCP servers.

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Trigger for: cost-reimbursement IGCE, CR cost estimate, CPFF, CPAF, CPIF, cost-plus estimate, BAA estimate, fixed-fee analysis, award-fee analysis, incentive-fee analysis, proposed CR rate validation, cost-pool buildup, share-ratio scenario, price-reasonableness memo, or fair-and-reasonable analysis. Build auditable CR estimates using BLS OEWS wages, fringe/overhead/G&A/FCCM pools, contract-type-specific fee, GSA CALC+ positioning, and GSA Per Diem travel. Do NOT use for FFP, LH/T&M, grants, or cooperative agreements. Requires the bls-oews, gsa-calc, and gsa-perdiem MCP servers.

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Browser automation for AI agents via inference.sh. Navigate web pages, interact with elements using @e refs, take screenshots, record video. Capabilities: web scraping, form filling, clicking, typing, drag-drop, file upload, JavaScript execution. Use for: web automation, data extraction, testing, agent browsing, research. Triggers: browser, web automation, scrape, navigate, click, fill form, screenshot, browse web, playwright, headless browser, web agent, surf internet, record video

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Trigger for: FFP IGCE, firm-fixed-price estimate, FFP cost model, proposed FFP rate validation, wrap-rate analysis, Agency BPA rate comparison, price-reasonableness memo, or fair-and-reasonable analysis. Build auditable Firm-Fixed-Price federal estimates using BLS OEWS wages, layered fringe/overhead/G&A/profit, GSA CALC+ positioning, and GSA Per Diem travel. Use for FFP-by-period, FFP-by-deliverable, SOW/PWS decomposition, implied-multiplier analysis, and fixed-price scenario comparisons. Do NOT use for Labor Hour, T&M, or cost-reimbursement IGCEs (use IGCE Builder LH/T&M or IGCE Builder CR). Do NOT use for grant budgets. Requires the bls-oews, gsa-calc, and gsa-perdiem MCP servers.

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Create or update a Codex skill with appropriately scoped instructions and any needed supporting resources.

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Implement SAFe methodology in Jira. Use when creating Epics, Features, Stories with proper hierarchy, acceptance criteria, and parent-child linking.

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Use this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like "the xlsx in my downloads") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.

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Re-ingest-correctness SOP for production RAG. Activate when a calling agent builds, reviews, or debugs an ingestion pipeline that runs more than once over a changing corpus — scheduled re-index, incremental updates, CI re-ingest, or a "retrieval has duplicates / shows deleted docs" bug. Encodes the rule — **ingestion must be idempotent: a document's content hash decides insert/update/skip, so re-running over unchanged docs is a no-op** — plus the docstore + doc-hash upsert machinery (LlamaIndex `IngestionPipeline` + `DocstoreStrategy`), the delete-propagation problem, and cross-framework equivalents (LangChain `index()` + `RecordManager`, manual hash table). ENHANCE overlay over [[llamaindex]]: the IngestionPipeline exists in the base skill but the re-ingest-correctness contract is not surfaced.

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Routes heterogeneous queries to the appropriate index, tool, or answering engine before retrieval. Use when one endpoint serves multiple handlers, such as summary, vector retrieval, text-to-SQL, or tools, and query types require different paths. Covers LLM, semantic, and rule-based routers, confidence thresholds, fallbacks, and framework mappings. Do not use when one handler serves all queries or branching is fixed.

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Pick an LM output format per (task x consumer x model) rather than by reflex: different formats carry different cognitive load (e.g. code-in-JSON makes the same model write worse code than plain-text+diff, while asking for prose when you need a typed object fails the other way). Use when designing or debugging an LM's output schema, choosing between plain text / diff / JSON / tool-call / grammar-constrained output, or when a model's quality drops after wrapping its output in a structured format.

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Enhancement-overlay SOP for adding sparse (BM25 / keyword) retrieval alongside dense (embedding) retrieval. Activate when a calling agent is building, reviewing, or debugging a retrieval pipeline whose corpus contains exact-match tokens — identifiers, error codes, SKUs, API/function names, proper nouns, citations, rare jargon — that pure dense embedding silently misses. Encodes the single decision rule (**hybrid is traffic-driven, not theoretical: add sparse only when the query share that depends on exact tokens is non-trivial**), the wiring of QueryFusionRetriever-style fusion (RRF vs alpha-weighted), and per-query-type alpha tuning. Frame the work as recovering lexical identity that dense pooling destroys, not as "add keyword search for completeness". Cross-links [[llamaindex]].

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Orchestrate the BZD mathematical-modeling Skills across problem reading, idea generation, model selection, paper drafting, section checks, final format/AIGC audits and judge-style scoring. Use when a user wants one entry point, an end-to-end competition workflow, the next appropriate Skill, or coordinated processing of a problem and paper.

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Review a mathematical-modeling paper's model-assumption section against the complete problem statement. Diagnose whether each assumption is necessary, evidence-based, compatible with the task, appropriately simplified, and usable by the later model. Use for assumption review, not automatic full-paper review or unsupported assumption generation.

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Drive the contracts engine's runtime surface (the stdio MCP server) to verify changes end-to-end. Scoped to public-plugins/plugins/healthcare/skills/contracts/ and ../../servers/documents/.

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Extract plain text from a document file - PDF, DOCX, XLSX, PPTX, RTF, or plain text/markdown/HTML. Use when a binary document needs to be turned into text, for example a contract PDF or an EHR DocumentReference attachment. Other skills (fhir) invoke scripts/extract.ts directly; the contracts MCP server bundles its own copy (servers/documents/src/extract.mjs) so its bundle stays self-contained — port fixes to both.

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Answer a question across a corpus of contract documents with verified citations. Use when the user asks what a contract says, which contracts have a clause, what changed between amendments, or any question that needs reading and citing across a set of contract files. The corpus must be on the local filesystem (see README).

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Screen a Medicare/Medicaid claims corpus for fraud, waste, and abuse and produce ranked, fully-cited investigation referrals for an SIU / program-integrity team. Use when asked to run a fraud sweep, screen claims for FWA, find billing anomalies, or generate investigation referrals over a claims dataset.

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Extract billable ICD-10-CM diagnosis codes from a clinical note the way a professional coder builds the claim. Use when users say "code this encounter", "assign ICD-10 codes", "what diagnosis codes apply", "code this chart", or when turning clinical documentation into claim-ready diagnosis codes.

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Automate payer review of prior authorization (PA) requests. This skill should be used when users say "Review this PA request", "Process prior authorization for [procedure]", "Assess medical necessity", "Generate PA decision", or when processing clinical documentation for coverage policy validation and authorization decisions.

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Assign CPT and HCPCS Level II procedure codes from clinical documentation the way a professional coder builds the claim. Use when users say "code this encounter for procedures", "what CPT codes apply", "assign HCPCS codes", "code this op note", or when turning visit notes or operative reports into claim-ready procedure codes.

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Generate clinical trial protocols for medical devices or drugs. This skill should be used when users say "Create a clinical trial protocol", "Generate protocol for [device/drug]", "Help me design a clinical study", "Research similar trials for [intervention]", or when developing FDA submission documentation for investigational products.

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Extract structured data from clinical notes with span-level provenance and null-safety. Use when users say "extract [variables] from this note", "abstract this chart", "pull structured data from these notes", "what does this note say about [field]", or when building a chart-abstraction, registry, or cohort dataset from unstructured clinical text.

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。