By radicaldo
Claude Code-specific fork of Superpowers with native task management, CC-specific enhancements, and extended session tracking
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores requirements and design before implementation.
Execute plan in batches with review checkpoints
Create detailed implementation plan with bite-sized tasks
Create detailed implementation plan with bite-sized tasks
Use this agent when a major project step has been completed and needs to be reviewed against the original plan and coding standards. Examples: <example>Context: The user is creating a code-review agent that should be called after a logical chunk of code is written. user: "I've finished implementing the user authentication system as outlined in step 3 of our plan" assistant: "Great work! Now let me use the code-reviewer agent to review the implementation against our plan and coding standards" <commentary>Since a major project step has been completed, use the code-reviewer agent to validate the work against the plan and identify any issues.</commentary></example> <example>Context: User has completed a significant feature implementation. user: "The API endpoints for the task management system are now complete - that covers step 2 from our architecture document" assistant: "Excellent! Let me have the code-reviewer agent examine this implementation to ensure it aligns with our plan and follows best practices" <commentary>A numbered step from the planning document has been completed, so the code-reviewer agent should review the work.</commentary></example>
Worktree-isolated implementation agent for PARALLEL task execution. Use when subagent-driven-development dispatches a wave of parallel-eligible tasks (same `wave`, disjoint file ownership, no cross-dependencies). Each dispatch runs in its own temporary git worktree via `isolation: worktree`, so concurrent edits never collide. For sequential single-task execution in the shared worktree, use the inline implementer-prompt.md template instead — this agent exists specifically for the parallel-wave path.
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
Use when facing 2+ independent tasks that can be worked on without shared state or sequential dependencies
Use when you have a written implementation plan to execute in a separate session with review checkpoints
Use ONLY at the end of a full plan/sprint when EVERY task is complete and tests pass — invoked once by subagent-driven-development or executing-plans after the final task, never after an individual task, mid-session, or whenever implementation "feels done". Guides merge/PR/keep/discard with archive-tagging so the feature branch is preserved for rewind/history.
Use when your human partner asks to execute an implementation plan while spending local GPU/Ollama compute instead of Claude API tokens — triggers: "offload", "local model", "ollama", "use my GPU", "save tokens", "spend local compute instead of API".
Executes bash commands
Hook triggers when Bash tool is used
Modifies files
Hook triggers on file write and edit operations
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Uses power tools
Uses Bash, Write, or Edit tools
Uses power tools
Uses Bash, Write, or Edit tools
A Claude Code skills plugin — forked and extended to stop agents from wasting tokens in messy local environments, and to stop them from shipping features that were never actually wired up.
A focused fork of obra/superpowers, tuned for Windows + VS Code + subagent-heavy workflows — where the same environment facts get re-discovered every session and where "all tests pass" too often hides a feature that nobody ever connected to its real entry point.
| End-to-end wiring gate | Plans now end with a wiring task, and the merge gate refuses features that were built + unit-tested but never connected to a real entry point. |
| Persistent lesson tracker | Environment lessons are injected into every agent and subagent at start. Learn once, stay learned — across sessions. |
| Preflight (flight check) | An executable environment contract agents prove on demand, instead of caching probe results that go stale. |
| Parallel by default | A worktree-isolated implementer agent plus planning nudges that fan independent work out into safe parallel waves. |
| Leaner plans | Tighter, more actionable plans instead of 2000-line auto-transcription. |
The core problem: agents on Windows repeatedly re-discover the same environment facts session after session. Where Python lives, which version py points to, what paths work. Every subagent starts fresh and burns 3-5 commands re-learning what the previous one already figured out. In VS Code this also means repeated permission prompts as each subagent starts without knowing what the previous task went through.
The second problem, just as expensive: a plan finishes with every task green, every unit test passing — and the feature still does nothing, because the pieces were built but never attached to the UI action, route, or caller a user actually hits. This fork treats that unwired state as a first-class failure and gates against it.
A plan is wired when the feature it builds is reachable and exercised through its real entry point — a user action flows UI → backend → response, or a caller actually invokes the new capability in production code. Plans that build and unit-test components in isolation but never connect them are unwired: every task is green, yet the feature does nothing for its intended purpose.
Three constructs now run through the planning and review skills, using one shared vocabulary:
blockedBy every task that feeds the feature) whose verifyCommand exercises the feature through its real entry point — an integration / e2e / smoke test, not a unit test re-checking one component.Where it's enforced:
| Skill | What changed |
|---|---|
writing-plans / writing-plans-lite | Require a terminal end-to-end wiring task and a reachability self-review before a plan is finalized. |
subagent-driven-development / requesting-code-review | Final review adds a whole-implementation reachability check across the finished work. |
finishing-a-development-branch | New Step 1.5 wiring gate before offering merge options; records wiring status in the PR body and merge commit; refuses to silently merge an unwired feature. |
shared/task-format-reference | Canonical wiring vocabulary all the skills draw from. |
npx claudepluginhub radicaldo/super-radical-powers --plugin super-radical-powersHarness-native ECC plugin for engineering teams - 67 agents, 278 skills, 94 legacy command shims, reusable hooks, rules, MCP conventions, and operator workflows for Claude Code plus adjacent agent harnesses
Core skills library for Claude Code: TDD, debugging, collaboration patterns, and proven techniques
Consult multiple AI coding agents (Gemini, OpenAI, Grok, Perplexity, plus codex, antigravity, and grok CLIs when installed) to get diverse perspectives on coding problems
Comprehensive skill pack with 66 specialized skills for full-stack developers: 12 language experts (Python, TypeScript, Go, Rust, C++, Swift, Kotlin, C#, PHP, Java, SQL, JavaScript), 10 backend frameworks, 6 frontend/mobile, plus infrastructure, DevOps, security, and testing. Features progressive disclosure architecture for 50% faster loading.
Upstash Context7 MCP server for up-to-date documentation lookup. Pull version-specific documentation and code examples directly from source repositories into your LLM context.
A growing collection of Claude-compatible academic workflow bundles. Covers scientific figures, manuscript writing and polishing, reviewer assessment, citation retrieval, data availability, paper reading, literature search, response letters, paper-to-PPTX conversion, and evidence-grounded Chinese invention patent drafting. Rules are organized as reusable skill folders with explicit workflows and quality checks.