Results for “spec”

Use the ClawHub CLI to search, install, update, and publish agent skills from clawhub.ai with advanced caching and compression. Use when you need to fetch new skills on the fly, sync installed skills to latest or a specific version, or publish new/updated skill folders with optimized performance.

Build an evidence-backed ECC install plan for a specific repo by sorting skills, commands, rules, hooks, and extras into DAILY vs LIBRARY buckets using parallel repo-aware review passes. Use when ECC should be trimmed to what a project actually needs instead of loading the full bundle.

Build an evidence-backed ECC install plan for a specific repo by sorting skills, commands, rules, hooks, and extras into DAILY vs LIBRARY buckets using parallel repo-aware review passes. Use when ECC should be trimmed to what a project actually needs instead of loading the full bundle.

Expert backend architect specializing in scalable API design, microservices architecture, and distributed systems. Masters REST/GraphQL/gRPC APIs, event-driven architectures, service mesh patterns, and modern backend frameworks. Handles service boundary definition, inter-service communication, resilience patterns, and observability. Use PROACTIVELY when creating new backend services or APIs.

Expert deployment engineer specializing in modern CI/CD pipelines, GitOps workflows, and advanced deployment automation. Masters GitHub Actions, ArgoCD/Flux, progressive delivery, container security, and platform engineering. Handles zero-downtime deployments, security scanning, and developer experience optimization. Use PROACTIVELY for CI/CD design, GitOps implementation, or deployment automation.

Expert performance engineer specializing in modern observability, application optimization, and scalable system performance. Masters OpenTelemetry, distributed tracing, load testing, multi-tier caching, Core Web Vitals, and performance monitoring. Handles end-to-end optimization, real user monitoring, and scalability patterns. Use PROACTIVELY for performance optimization, observability, or scalability challenges.

Expert technical documentation specialist for developer docs, API references, and runbooks. Activate on: documentation, docs, README, API reference, technical writing, user guide, runbook, ADR, changelog, release notes, tutorial, how-to guide. NOT for: marketing copy (use copywriting skills), blog posts (use content skills), code comments (handled by developers).

Guides choice among spironolactone, eplerenone, and esaxerenone for primary aldosteronism based on comparative efficacy, safety, and patient-specific factors. Triggers include when initiating MRA therapy and asking 'Which MRA should I prescribe?' or considering switching agents due to adverse effects, cost, or need for potassium supplementation.

Build an evidence-backed ECC install plan for a specific repo by sorting skills, commands, rules, hooks, and extras into DAILY vs LIBRARY buckets using parallel repo-aware review passes. Use when ECC should be trimmed to what a project actually needs instead of loading the full bundle.

Content Factory multi-agent orchestrator. Use when the user says @orchestrator, asks to divide work, run a swarm, coordinate specialists, or when the task is non-trivial (multi-file, audit, cleanup, feature, incident). ALWAYS launch 5–10 agents with clear scopes; merge results; block guesswork.

Comprehensive Cloudflare platform skill covering Workers, Pages, storage (KV, D1, R2), AI (Workers AI, Vectorize, Agents SDK), feature flags (Flagship), networking (Tunnel, Spectrum), security (WAF, DDoS), and infrastructure-as-code (Terraform, Pulumi). Use for any Cloudflare development task. Biases towards retrieval from Cloudflare docs over pre-trained knowledge.

Convert C code to idiomatic Rust. Use when migrating C projects to Rust, translating C patterns to idiomatic Rust, or refactoring C codebases. Extends meta-convert-dev with C-to-Rust specific patterns covering manual memory management to ownership, pointer safety, type system enhancements, and modernization strategies.

Convert Python code to idiomatic Elm. Use when migrating Python backends to Elm frontends, translating Python logic to type-safe frontend code, or refactoring Python codebases into functional-first Elm applications. Extends meta-convert-dev with Python-to-Elm specific patterns focused on The Elm Architecture (TEA).

Comprehensive blog writing skill that handles technical blog posts, personal voice writing, brain dump transformation, and category-aware AEO-optimized content. Use when: (1) writing, editing, or proofreading a blog article or post, (2) transforming unstructured brain dumps into polished posts, (3) writing in specific personal voices (Jarad, Nick Nisi), (4) creating category-aware technology/company/product posts, (5) building tutorials, deep dives, postmortems, benchmarks, or architecture posts, (6) writing engineering blogs, dev blogs, programming blogs, coding tutorials, or documentation posts. Triggers: blog post, blog writing, technical blog, dev tutorial, brain dump, article, content writing, developer article, engineering blog, programming blog, coding tutorial, documentation post, technical writing, blog editing, proofreading, developer content

Quantifies how sensitive a language model benchmark's reliability and ranking stability are to specific design choices, such as the selection of scenarios, subscenarios, examples, and few-shot prompts. Use when the user has predictions and gold and needs to compute DIoR.

Conducts Data Protection Impact Assessments for AI and ML systems per EDPB Guidelines 04/2025 on AI processing. Covers training data lawfulness evaluation, model risk assessment, automated decision triggers, and AI-specific DPIA methodology. Keywords: AI DPIA, machine learning impact assessment, EDPB AI guidelines, model risk, training data.

Guide for implementing GDPR-valid consent under Article 7 conditions and Article 4(11) definition. Covers five core requirements: freely given, specific, informed, unambiguous, and clear affirmative action. Includes pre-ticked boxes prohibition per Planet49 CJEU C-673/17, consent form audit checklist, and practical implementation patterns.

Generic webapp QA fallback — use only when no project-specific QA skill applies (project-local QA wins; native builds → mobile-emulator-test). Use when "QA the app", "test CRUD", or "smoke test". Monkey / guest vs logged-in → test-exploratory. Dead buttons → plan-stub-checker. Pixel diffs → test-visual-regression.

Audit a full-stack app against the RealWorld ("Conduit") reference — its formal API spec, shared Bruno/Hurl E2E suite, and closest-stack reference implementation — to find what is. Use when "audit against realworld", "compare my app to realworld", "conduit conformance", "is my full-stack app complete", "full-stack gap.

AI-native lead intelligence and outreach pipeline. Replaces Apollo, Clay, and ZoomInfo with agent-powered signal scoring, mutual ranking, warm path discovery, source-derived voice modeling, and channel-specific outreach across email, LinkedIn, and X. Use when the user wants to find, qualify, and reach high-value contacts.

Expert framework for assessing and achieving product-market fit. Combines PMF measurement methodologies, Sean Ellis survey, retention analysis, segment-specific PMF, and post-PMF scaling strategy. Use when determining if product has PMF, measuring user engagement, running PMF surveys, or deciding whether to scale or keep iterating.

Use when writing or modifying the pythinker-code terminal UI in apps/pythinker-code/src/tui — components, dialogs/selectors, slash commands, themes, streaming render, or the PythinkerTUI controllers. Covers the architecture, where new features go, test placement, the theme system mechanics, and the dialog interaction/visual spec (DESIGN.md).

Foundational Java patterns covering core syntax, object-oriented programming, generics, collections, streams, lambdas, and modern Java features. Use when writing Java code, understanding the type system, working with collections/streams, or needing guidance on which specialized Java skill to use. This is the entry point for Java development.

Convert Objective-C code to idiomatic Swift. Use when migrating Objective-C projects to Swift, translating Objective-C patterns to modern Swift, modernizing legacy codebases, or establishing gradual migration strategies with interop. Extends meta-convert-dev with Objective-C-to-Swift specific patterns including bridging and @objc attributes.

C++-specific library development patterns. Use when creating C++ libraries, designing header-only vs compiled libraries, configuring CMake for library targets, managing ABI stability and versioning, integrating with Conan/vcpkg, documenting with Doxygen, or testing with GoogleTest/Catch2. Extends meta-library-dev with C++ tooling and idioms.

Elm-specific library/package development patterns. Use when creating Elm packages, designing pure functional APIs, configuring elm.json for libraries, writing documentation comments, publishing to package.elm-lang.org, or managing semantic versioning in pure functional context. Extends meta-library-dev with Elm tooling and ecosystem patterns.

Performs deep architectural analysis of a specified module, directory, or feature area by examining structural coupling, data flow, concurrency patterns, risk, and SOLID alignment. Use when the user wants to assess, evaluate, or review the architecture, design quality, dependency structure, coupling, cohesion, or technical debt of an existing part of the codebase. Not for investigating specific bugs, runtime errors, or failures — use investigate. Not for test planning — use automated-test-planning. Not for file-level code review — use code-review. Not for researching open-ended options, prior art, or how something works — use research. Not for designing a new interface or contract — use design-an-api. Not for writing documentation or architectural decision records.

Run structured What-If scenario analysis with 4-6 branch possibility exploration (best, likely, worst, wild card, contrarian, second-order). Use for speculative questions about uncertain futures, strategic forks, contingency planning, or stress-testing decisions.

Turns messy documents, meeting transcripts, reports, or raw data into a polished, design-conscious HTML presentation through a structured workflow that filters content, defines style specs, and generates slides, with optional theme research and cross-session resume support.

Documents MariaDB-specific DROP TABLE syntax and behavior, including RESTRICT/CASCADE no-ops, DROP TEMPORARY TABLE, partial drops, IF EXISTS warnings, foreign key references, views, triggers, implicit commit, and WAIT/NOWAIT. Use when writing, generating, or reviewing DROP TABLE statements targeting MariaDB.

Reference for MariaDB's built-in date and time functions, covering current time, arithmetic, differences, extraction, parsing, formatting, Unix time, time-zone conversion, construction, and Oracle-compatible helpers, with guidance on version-specific behavior and common pitfalls.

Generate comprehensive email marketing strategy with 90-day implementation roadmap. Analyzes business type (local-business, saas, ecommerce, creator, agency) to deliver industry-specific segmentation plans, automation sequences, content calendars, KPI targets, platform recommendations, and week-by-week rollout schedule. Use when building email program from scratch or restructuring existing strategy.

Search and deploy services from Railway's template marketplace. Use when user wants to add a service from a template, find templates for a specific use case, or deploy tools like Ghost, Strapi, n8n, Minio, Uptime Kuma, etc. For databases (Postgres, Redis, MySQL, MongoDB), prefer the railway-database skill.

Data pipeline specialist for ETL design, data quality, CDC patterns, and batch/stream processingUse when "data pipeline, etl, cdc, data quality, batch processing, stream processing, data transformation, data warehouse, data lake, data validation, data-engineering, etl, cdc, batch, streaming, data-quality, dbt, airflow, dagster, data-pipeline, ml-memory" mentioned.

End-to-end data pipeline for empirical research: fetch economic data from APIs (FRED, World Bank, IMF, BLS, OECD, Yahoo Finance), clean and transform raw data, construct strategy-specific variables, and validate panel structure. Use when asked to fetch data, download data, clean data, merge datasets, prepare analysis-ready data.

Design and operate bounded multi-agent workflows with task decomposition, dependency graphs, ownership, handoff contracts, shared-state controls, approvals, recovery, and synthesis. Use when a task contains genuinely independent workstreams, specialized roles, parallel research or implementation, reviewer-worker loops, or coordination problems that one agent should not execute sequentially.