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Found 1,440 Skills
Assess construction data quality using completeness, accuracy, consistency, timeliness, and validity metrics. Automated validation with regex patterns, thresholds, and reporting.
Day 2 end capstone move of a Foundation Sprint. Compresses the sprint's full strategic frame into a single canonical sentence (the Founding Hypothesis) plus an assumption scorecard, why-we-believe, what-could-prove-us-wrong, and recommended next validation step. Use after Magic Lenses is signed. Strict canonical template; paraphrase is not accepted in v0.1.0. The Founding Hypothesis is the spine artifact the sprint exists to produce.
Design and operate data quality programs for financial data — golden source architecture, validation rules, data lineage, exception management, profiling, and governance. Use when building validation rules for pricing or client data pipelines, designing a data quality monitoring framework, establishing golden source designations across systems, implementing data lineage for BCBS 239 or MiFID II, investigating reconciliation breaks or billing errors traced to bad data, preparing for regulatory exams on data accuracy, building data quality scorecards, or defining data stewardship roles. Trigger on: data quality, golden source, data lineage, data validation, data profiling, exception management, data governance, BCBS 239, data completeness, data accuracy, validation rules, data anomaly, data stewardship, data quality scorecard.
Build typed LLM applications with PydanticAI: schema-constrained outputs, tool integration, validation, retries, and deterministic downstream handoffs. Use when users need reliable structured outputs instead of free-form text generation.
Guides Validation by Educational Experience (VEE) for North American actuarial credential paths (SOA, CAS)—how VEE fits preliminary requirements, current topic areas (Economics, Accounting & Finance, Mathematical Statistics; subject to society updates), approved-course criteria, candidate workflow and documentation, SOA vs CAS submission timing relative to ASA/ACAS progress, international/transfer considerations, and common pitfalls. Use for VEE, validation by educational experience, VEE credit, actuarial VEE requirements, SOA VEE, CAS VEE, VEE economics, VEE statistics, VEE accounting and finance, college credit for actuarial exams, submit VEE transcript—not deep exam study (pre-actuarial-foundations, advanced-short-term-actuarial-mathematics, advanced-long-term-actuarial-mathematics), workpapers (actuarial-analyst), signing (associate-actuary, appointed-chief-actuary), official transcript qualification rulings, or generic degree planning.
Guides authoring, review, optimization, and false-positive debugging of YARA-X detection rules for malware identification across PE, script, npm, Office, Chrome extensions (crx module), and Android DEX (dex module). Covers string and atom quality, condition short-circuiting, legacy YARA migration, yarGen/FLOSS workflows, goodware validation, and production deployment—not full malware reverse engineering, network IDS (Suricata/Snort), or memory forensics (Volatility). Use when the user asks to write YARA rule, YARA-X, yr check, yr scan, false positive YARA, yarGen, malware detection rule, crx module, dex module, optimize YARA performance, or migrate legacy YARA.
Edit the Prisma Next data contract — add models, fields, relations, indexes, enums, type aliases, polymorphic types (`@@discriminator` / `@@base`), use extension namespaces (`pgvector.Vector(...)`, `cipherstash.EncryptedString(...)`), wire `prisma-next.config.ts` with `defineConfig` from the `@prisma-next/<target>/config` façade, and run `prisma-next contract emit`. Use for schema, models, fields, attributes, soft delete, paranoid, scopes, validations, callbacks, prisma schema, PSL, contract.prisma, contract.ts, contract.json, contract.d.ts, façade imports, `@prisma-next/postgres/config`, `@prisma-next/postgres/contract-builder`, `@prisma-next/postgres/control`, `@prisma-next/mongo/config`, `@prisma-next/mongo/contract-builder`, `extensions:`, `extensionPacks`, pgvector, cipherstash, postgis, paradedb, PN-CLI-4002, PN-CLI-4003, PN-CLI-4011.
Privacy review and testing: evaluate PII handling, data flows, tracking inventory, consent mechanisms, storage practices, and data leakage risks with browser-based validation against GDPR, CCPA, and industry best practices.
This skill should be activated when the user requests to "deepen a topic", "analyze a topic", "help me write an outline", "will this topic go viral", "help me diagnose a topic", "is this topic worth pursuing", or "how to improve this topic". Even if the user only shares a topic and asks for opinions, you should proactively initiate the diagnosis process instead of providing a simple response. Driven by the cognitive hijacking theory, it features four modules: Perspective Collision (challenging the topic's premise), Topic Diagnosis (graded using 🛵🚗✈️), Outline Design (emotional peak planning), and Style Validation (alignment with li-writer style). It generates a comprehensive deepening report and saves it as a file. Do NOT trigger this skill for: merely recording topics (use li-recorder), directly writing scripts (use li-writer). Use when the user wants to "develop a topic", "analyze topic potential", "write an outline", "will this topic go viral", or needs full topic diagnosis + outline design. Trigger even if the user just shares a topic and asks for opinions.
Cloudflare Zero Trust Access authentication for Workers. Use for JWT validation, service tokens, CORS, or encountering preflight blocking, cache race conditions, missing JWT headers.
Migrate users' projects from Wot UI v1 to v2. This skill is invoked when users request to upgrade wot-design-uni to @wot-ui/ui, replace old components/old APIs, migrate form validation systems, and fix compilation errors or runtime errors caused by incompatible changes in v2.
Guideline for designing, implementing, and verifying secure Python applications following OWASP Top 10 best practices. Use when the user wants to: (1) review Python code for security vulnerabilities, (2) design a secure Python application architecture, (3) implement security features (authentication, authorization, cryptography, input validation), (4) audit Python dependencies for known vulnerabilities, (5) create security checklists or verification plans, (6) fix security bugs or harden existing Python code, (7) set up security testing and static analysis (bandit, safety, semgrep), or (8) handle any Python security concern including injection prevention, secure deserialization, SSRF protection, secrets management, and secure deployment.