By nmalinowski
Route research and engineering tasks to the right skills and model profile.
Cross-platform task triage plugin for Codex, Claude Code, and Copilot CLI.
It routes incoming work requests to:
primary + secondary)deep_reasoning, balanced, fast_execution)skills/project-task-router/scripts/route_task.pyalternativesmissing_capabilities)market-discovery-validationproduct-strategy-positioningarchitecture-build-planningfeature-implementationui-ux-frontendsecurity-compliance-reliabilitydata-analytics-aiplatform-ops-reliabilitynmalinowski/groundworknmalinowski/agentsnmalinowski/ui-ux-pro-max-skill21st.dev/community/componentsnano-banana-proBasic routing:
python skills/project-task-router/scripts/route_task.py --task "Implement HIPAA-compliant audit logging"
Markdown output:
python skills/project-task-router/scripts/route_task.py --task "Plan API architecture" --format markdown
Route with explicit installed-skill list:
python skills/project-task-router/scripts/route_task.py --task "Improve frontend layout" --installed-skills "ui-ux-pro-max,design-system,product-design"
Route with installed-skill file:
python skills/project-task-router/scripts/route_task.py --task "Build RAG pipeline" --installed-skills-file installed-skills.json
When --installed-skills and --installed-skills-file are not provided, discovery runs in this order:
ROUTER_INSTALLED_SKILLS, INSTALLED_SKILLS, CODEX_INSTALLED_SKILLS, COPILOT_INSTALLED_SKILLS, CLAUDE_INSTALLED_SKILLSinstalled-skills.json.codex/installed-skills.json.claude/installed-skills.jsoncopilot/installed-skills.json~/.codex/installed-skills.json~/.claude/installed-skills.json<plugin-root>/installed-skills.json<plugin-root>/.codex/installed-skills.json<plugin-root>/.claude/installed-skills.json<script-dir>/installed-skills.jsonFirst existing manifest wins.
route: selected domain or needs-clarificationconfidence: low, medium, highprimary_skills and secondary_skillsalternatives: close competing routesmissing_capabilities: required capability IDs not resolved from known inventoryevidence: domain score mapnote: routing/discovery hintsUse this sequence when executing routed work:
low, medium, highcritical findings for explicit approvalRun tests:
python -m unittest discover -s tests -v
MIT (see plugin.json).
Uses power tools
Uses Bash, Write, or Edit tools
Own this plugin?
Verify ownership to unlock analytics, metadata editing, and a verified badge. GitHub access is read-only (username + org membership).
Sign in to claimOwn this plugin?
Verify ownership to unlock analytics, metadata editing, and a verified badge. GitHub access is read-only (username + org membership).
Sign in to claimBased on adoption, maintenance, documentation, and repository signals. Not a security audit or endorsement.
npx claudepluginhub nmalinowski/project-task-routerComprehensive multi-domain plugin collection with reusable skills, agents, and commands.
Agent Teams orchestration, governance hooks, multi-AI review, memento skill intelligence, and project management skills for Claude Code
This skill should be used when the model's ROLE_TYPE is orchestrator and needs to delegate tasks to specialist sub-agents. Provides scientific delegation framework ensuring world-building context (WHERE, WHAT, WHY) while preserving agent autonomy in implementation decisions (HOW). Use when planning task delegation, structuring sub-agent prompts, or coordinating multi-agent workflows.
The team-architecture factory for Claude Code — a meta-skill that turns a domain description into an agent team and the skills they use, with six pre-defined team-architecture patterns (Pipeline, Fan-out/Fan-in, Expert Pool, Producer-Reviewer, Supervisor, Hierarchical Delegation). Claude Code용 팀 아키텍처 팩토리: 도메인 한 문장을 에이전트 팀과 스킬 세트로 변환하는 메타 스킬.
361 agentic skills across 65 domains, 72 agent personas, and 17 team compositions following the agentskills.io open standard
A collection of project-agnostic skills for common engineering tasks such as ticket refinement, planning, code reviews, agent memory hygiene, skill authoring, and more.
Ultra-compressed communication mode. Cuts 65% of output tokens (measured) while keeping full technical accuracy by speaking like a caveman.