Results for “pr”

Foundational theory for process-oriented concurrency through synchronous message-passing, applicable to multi-agent coordination and parallel decomposition

Backend Node.js Specialist IA — Expert en développement backend Node.js (Express, Fastify, NestJS, Prisma, real-time, WebSockets)

Use this skill for transporting pipes (water, gas, oil, structural). Covers § 393.122 specific rules and roll-prevention.

Manage issues, projects & team workflows in Linear. Use when the user wants to read, create or updates tickets in Linear.

Validate PRD, UX, Architecture and Epics specs are complete. Use when the user says "check implementation readiness".

Rust testing patterns including unit tests, integration tests, async testing, property-based testing, mocking, and coverage. Follows TDD methodology.

Technical architecture and UX specialist who provides developers with solid foundations, CSS systems, and clear implementation guidance

Screenshot-obsessed, fantasy-allergic QA specialist - Default to finding 3-5 issues, requires visual proof for everything

Discover supply-chain manifests, run structured supply-chain audits, and export SBOMs through a dedicated MCP surface.

Use this skill to generate or audit design systems, check visual consistency, and review PRs that touch styling.

Pythonic idioms, PEP 8 standards, type hints, and best practices for building robust, efficient, and maintainable Python applications.

Laravel architecture patterns, routing/controllers, Eloquent ORM, service layers, queues, events, caching, and API resources for production apps.

Suggests manual context compaction at logical intervals to preserve context through task phases rather than arbitrary auto-compaction.

Rust testing patterns including unit tests, integration tests, async testing, property-based testing, mocking, and coverage. Follows TDD methodology.

Idiomatic C# and .NET patterns, conventions, dependency injection, async/await, and best practices for building robust, maintainable .NET applications.

SwiftUI architecture patterns, state management with @Observable, view composition, navigation, performance optimization, and modern iOS/macOS UI best practices.

Write grant reports with progress, financials, and outcomes. TRIGGERS - Use when user needs help with grant-report related tasks.

OpenFEC API skill. Use when working with OpenFEC for audit-case, audit-category, audit-primary-category. Covers 99 endpoints.

Convex Management API skill. Use when working with Convex Management for teams, projects, deployments. Covers 20 endpoints.

xurl CLI for authenticated X posts, replies, reads/search, DMs, media upload, followers, auth status, or raw v2 API calls.

Verify an MarketingClaw release is fully published across GitHub, npm, plugins, ClawHub, package smoke, and live Gateway agent turns.

Write grant reports with progress, financials, and outcomes. TRIGGERS - Use when user needs help with grant-report related tasks.

Use when you need to inspect and summarize statistics for HMM (profile hidden Markov model) files from the HMMER suite.

Use when computing RNA specific heat profiles from sequence data to analyze melting behavior and thermal stability across temperature ranges.

Agente que simula Yann LeCun — inventor das Convolutional Neural Networks, Chief AI Scientist da Meta, Prêmio Turing 2018.

CI/CD pipeline engineering agent for GitHub Actions workflows with matrix builds, caching, artifacts, and security best practices

High-fidelity ASCII/Braille rendering via the Risomorphism-1911 pipeline — edge-aware downsampling, presets, quality gates, and eikon mirror workflows

Sets up and audits the Claude Code harness for a project: CLAUDE.md, .claude/rules, skills, subagents, settings.json permissions, hooks, verification loop. Commands: kickoff (scaffold new setup), check-playbook (score an existing one), scouting-report (last scorecard), adjust (fix bloat/misplaced instructions), drill (turn advisory prose into real hooks/permissions/CI), decline (record an accepted risk), gameplan (work order with acceptance criteria before building), watch-film (check a diff against that order for scope creep/weakened tests/false claims). Use for setting up Claude Code, or on: "Claude ignores my CLAUDE.md", "it's huge and still misses things", "it said done but ran nothing", "it changed files I didn't ask about", "it weakened a test to pass", "rule, skill, or hook?", "is my setup any good". Not for code quality (code-audit), test coverage (test-assessment), one-off prompt wording (genie-proof-prompts), new skill authoring (skill-creator), or compacting a conversation (handoff).

Pick the right business-model canvas (Lean Canvas, Business Model Canvas, or Value Proposition Canvas) for the stage and fill it with specifics — one segment, one primary canvas, top-3 assumptions, no fluff in the moat or channel boxes. Load when the user asks to fill a business model canvas, lean canvas, value proposition canvas, model this business, map the business model, says "fill the BMC", "make a Lean Canvas", "Value Proposition Canvas for this", "model this idea", "what's the business model", "design the business model". Sub-skill of `venture-exploration`. Hard-bans "everyone" segments, generic channels ("SEO/social/content/ads"), and "unfair advantage = AI/data/network effects" with no concrete asset. Does NOT score viability — for that use `idea-evaluation`.

Use this skill when conditioning, loading, preparing, or labeling signal data for analysis or ML training. Covers: cleaning a single signal (fill gaps, remove drift, deoutlier, denoise, resample/align a time base) BEFORE analysis; building a `signalDatastore` pipeline; creating a `labeledSignalSet` for Signal Labeler; deriving labels (filename, folder, in-file, ROI, time-frequency ROI); stratified train/val/test splits; framing long signals; parallel processing; and shaping datastore output for `trainnet`. Triggers include "clean up this signal", "remove drift / detrend", "fill gaps", "remove spikes / outliers", "denoise", "resample to a uniform rate", "align channels", "labels from filenames", "stratified split", "prepare for Signal Labeler", and function names like `fillgaps`, `fillmissing`, `detrend`, `filloutliers`, `smoothdata`, `resample`, `synchronize`, `signalDatastore`, `labeledSignalSet`, `filenames2labels`, `folders2labels`, `splitlabels`, `framesig`, `framelbl`, `createDatastores`.

Use this skill to classify tabular data end-to-end in MATLAB — load a dataset, prepare and clean it, select promising classifiers, train them, and compare accuracies with cross-validation, holdout, or hyperparameter optimization plus statistical tests. TRIGGER when: user asks to classify tabular data, pick classifiers for a dataset, compare classifier accuracy, run cross-validation or a holdout evaluation, or find the best model with statistical uncertainty. DO NOT TRIGGER when: user has non-tabular inputs (images, sequences, time series), wants a regression model, is training a specific neural network architecture (use matlab-train-network), or wants cost-sensitive learning or an arbitrary class-prior vector (this skill only supports the built-in uniform-prior toggle for imbalanced data).

Runs NCBI BLAST+ 2.17.0 sequence searches from the command line: makeblastdb (with -parse_seqids), blastn/blastp/blastx/tblastn with tabular -outfmt 6/7 for parsing, correct -task choice (megablast vs blastn vs blastn-short), -taxids/-negative_taxids taxonomic scoping, and -mt_mode multithreading; plus a DIAMOND blastp --ultra-sensitive path for large protein searches. Warns that -max_target_seqs is a heuristic keep-count, not a top-N best-hits filter. Use when the user wants command-line BLAST, makeblastdb, a local BLAST database, blastn/blastp/blastx/tblastn searches, or DIAMOND protein search. For the Bio.Blast web NCBIWWW API prefer alterlab-biopython; for quick one-liner database lookups prefer alterlab-gget. Part of the AlterLab Academic Skills suite.

Fully autonomous QA testing agent for Modular RAG MCP Server. Reads test cases from QA_TEST_PLAN.md, executes ALL test types automatically without human intervention — CLI commands, Dashboard UI via Streamlit AppTest headless rendering, MCP protocol via subprocess JSON-RPC, provider switches, and data lifecycle checks. Diagnoses failures, applies fixes with up to 3 retry rounds, and records results in QA_TEST_PROGRESS.md. Use when user says 'run QA', 'QA test', 'QA 测试', '执行测试', '跑测试', 'test and fix', or wants to execute QA test plan.

Establish durable brand and voice context for cross-skill consumption. Generates BRAND.md (audience, positioning, do/don't editorial rules, taboo phrases, competitor differentiation) and VOICE.md (existing persona JSON re-expressed as readable prose), both written to the project root. When present, all blog sub-skills auto-load these files before writing or reviewing. Pairs with blog-persona, which manages the structured persona JSON. Use when user says "blog brand", "create brand context", "brand voice doc", "BRAND.md", "VOICE.md", "establish editorial brand", "brand guidelines for blog".

Perform vulnerability scanning in OT/ICS environments safely using passive monitoring, native protocol queries, and carefully controlled active scanning with Tenable OT Security to identify vulnerabilities without disrupting industrial processes or crashing legacy controllers.

Verifies claims, articles, screenshots, and URLs through source-grounded analysis with an evidence ledger, source credibility evaluation, and manipulation detection. Supports quick checks, full fact-check cards, two-source comparisons, and prebunking in multiple languages and policy contexts.