using-superpowers

name	using-superpowers
description	Comprehensive meta-skill for discovering and orchestrating ALL available tools, MCP servers, skills, plugins, agents, and CLI capabilities to solve any problem optimally. Invoke this skill for ANY non-trivial task — building software, researching topics, analyzing data, debugging systems, deploying code, creating reports, automating workflows, or solving complex multi-step problems. This skill teaches runtime capability discovery via ToolSearch, parallel agent dispatch, and multi-tool orchestration across Firecrawl (web research), Playwright (browser automation), Pinecone (vector storage/RAG), Vercel (deployment), Cloudflare (edge infrastructure), Mermaid Chart (diagrams), Context7 (library docs), Greptile (codebase search), HuggingFace (ML/AI), CodeRabbit (code review), GerdsenAI Document Builder (PDF reports), language servers (TypeScript, Python, C#, C++), and the superpowers workflow skills (TDD, debugging, planning, code review, worktrees). Even for seemingly simple tasks, run capability discovery first — a specialized tool often produces dramatically better results than a manual approach. When in doubt, use this skill. Use when this capability is needed.
metadata	{"author":"GerdsenAI"}

Orchestrating All Capabilities

This skill turns you from a single-tool operator into a full-stack orchestrator. The environment you're running in typically has dozens of specialized tools — MCP servers, CLI utilities, language servers, plugins, and skills — but most go unused because they aren't top-of-mind. This skill fixes that by teaching a systematic discovery-first approach.

The core insight: the cost of discovering a better tool is seconds; the cost of missing one is minutes or hours of manual work. Always discover first.

Phase 0: Capability Discovery

Before doing anything substantive, survey what's available. Tools change between sessions (MCP servers added/removed, plugins installed/uninstalled), so discovery is essential even if you think you know what's available.

0a: Check for Applicable Skills

Before responding to any user message — even clarifying questions — check whether a skill applies. This incorporates the superpowers skill-routing discipline:

1. Scan the available skills list in your system context
2. If there's even a small chance a skill applies, invoke it via the Skill tool
3. Process skills first (brainstorming, debugging, planning), then implementation skills

Rationalizations to watch for:

• "This is just a simple question" — Questions are tasks. Check for skills.
• "I need more context first" — Skill check comes before exploration.
• "The skill is overkill" — Simple things become complex. Use it.
• "I'll just do this one thing first" — Check BEFORE doing anything.

0b: Probe for Deferred MCP Tools

Many powerful tools are "deferred" — they exist but aren't loaded until you discover them via ToolSearch. Run these probes in parallel (they are independent):

Probe Keyword	What You Find	Use When
"firecrawl"	Web search, scrape, crawl, extract, browser	Research, data gathering, live web content
"playwright"	Full browser automation (click, fill, screenshot)	Interactive testing, form filling, visual verification
"pinecone"	Vector DB: indexes, search, upsert, rerank, assistants	Knowledge storage, RAG, cross-session memory
"cloudflare"	Workers, D1, KV, R2, Hyperdrive	Edge deployment, databases, object storage
"vercel"	Deploy, projects, logs, domains	Frontend deployment, monitoring
"hugging face"	Model search, paper search, datasets, spaces	ML/AI research, model discovery
"mermaid"	Diagram rendering and validation	Architecture diagrams, flowcharts, visualizations
"context7"	Library documentation lookup	API references, framework-specific questions
"greptile"	Codebase search, code review, PR analysis	Understanding unfamiliar codebases
"ollama"	Local LLM inference (OpenAI-compatible API)	Pre-screening, counter-arguments, offline drafts
"github"	Issues, PRs, repos, actions via MCP	GitHub operations beyond the gh CLI

Not every probe will find tools — that's fine. The ones that do will dramatically expand your capabilities. See references/tool-discovery-probes.md for the full catalog with expected tool names and detailed capabilities.

0c: Check CLI Tools

These are always available via the Bash tool:

which gh python node claude 2>/dev/null

• gh — GitHub CLI for repos, PRs, issues, releases, actions
• python — Scripting, data processing, automation
• node/npm — JavaScript execution, package management
• claude — Claude CLI for spawning subprocesses

0d: Scan Available Skills

Review your system context for loaded skills. Key families to look for:

Superpowers workflow skills (prefix superpowers:):

• brainstorming — Socratic design exploration before implementation
• writing-plans — Detailed bite-sized implementation plans
• executing-plans — Execute plans in batches with review checkpoints
• subagent-driven-development — Dispatch one subagent per task with two-stage review
• systematic-debugging — 4-phase root cause investigation
• test-driven-development — RED-GREEN-REFACTOR cycle
• dispatching-parallel-agents — Concurrent problem solving
• requesting-code-review / receiving-code-review — Quality gates
• using-git-worktrees — Isolated development branches
• verification-before-completion — Evidence-based success claims
• finishing-a-development-branch — Merge/PR/cleanup flow

GerdsenAI skills (prefix gerdsenai:):

• pdf-document-authoring — Professional PDF output with Mermaid diagrams
• build — PDF build command (supports --recursive)
• research-report — Full research pipeline with parallel sub-agents
• setup — Document Builder installation, configuration, and updates

Other skills (check system context for availability):

• firecrawl:firecrawl-cli — Web operations
• coderabbit:code-review — AI-powered code review
• frontend-design — UI/UX patterns
• feature-dev — Feature development workflows
• code-simplifier — Code simplification
• security-guidance — Security best practices
• skill-creator — Creating new skills
• pinecone:* — Pinecone assistant and query operations
• huggingface-skills:* — ML training, datasets, evaluation, CLI

0e: Build a Capability Manifest

Organize everything you discovered into a mental manifest. This becomes your decision-making reference for the rest of the task:

Category	Available Tools	Fallback Chain
Web Research	Firecrawl > WebSearch > WebFetch	If Firecrawl unavailable, WebSearch still works
Browser Automation	Playwright > Firecrawl browser	Firecrawl has a browser mode as backup
Vector Storage / RAG	Pinecone > local JSON files > in-context	Degrade gracefully if Pinecone unavailable
Cloud / Deploy	Vercel (frontend) + Cloudflare (edge/API)	Manual deploy instructions as last resort
Code Intelligence	Language servers (TS/Py/C#/C++) > Grep + Glob	LSPs give precise types; Grep is a fallback
Local AI	Ollama > (not available)	Local inference for pre-screening, counter-arguments
Diagrams	Mermaid Chart MCP > inline Mermaid code blocks	Inline Mermaid always works
Documentation	Context7 > Firecrawl doc sites > WebFetch	Multiple paths to get docs
Code Review	CodeRabbit > Greptile > superpowers code review	At least one is usually available
ML / AI	HuggingFace MCP > WebSearch for papers	HF tools are specialized but optional
PDF / Reports	GerdsenAI Document Builder	The skill for formatted output
Git / GitHub	gh CLI + GitHub MCP	gh CLI is always available

Phase 1: Task Decomposition

With your capability manifest in hand, break the user's request into discrete subtasks.

1a: Identify Independent Subtasks

Two subtasks are independent if neither needs the other's output to begin. Look for natural boundaries:

• Data gathering vs scaffolding — research and skeleton code can proceed simultaneously
• Multiple research questions — each question is its own subtask
• Build vs deploy config — application code and deployment setup are independent
• Tests vs documentation — can be written in parallel once the interface is defined

1b: Map Tools to Subtasks

For each subtask, select the optimal tool from your manifest. The right tool is not always the obvious one:

• Firecrawl's structured extraction beats WebFetch for pulling specific data from web pages
• Playwright beats Firecrawl when you need to interact (log in, fill forms, navigate SPAs)
• Pinecone beats keeping everything in context when research findings exceed ~20 pages
• Context7 beats web-searching for framework-specific API questions
• Language servers give precise type information that Grep cannot provide
• GerdsenAI PDF builder produces professional reports; raw markdown is fine for internal notes
• CodeRabbit or Greptile code review catches things a manual scan misses

1c: Design the Execution Plan

Sequence subtasks by dependency:

1. Parallel batch 1 — all independent subtasks
2. Parallel batch 2 — subtasks that depend only on batch 1 outputs
3. Continue until all subtasks are sequenced

Use TaskCreate to register subtasks. This creates visible progress for the user and ensures nothing is forgotten.

Phase 2: Parallel Execution

2a: Dispatch Independent Work

When multiple subtasks can proceed simultaneously, use the Task tool with subagents. Each subagent should receive:

• A clear, self-contained objective
• The specific tools it should use (include ToolSearch probe instructions if it needs deferred tools)
• Where to save outputs
• What format to return results in

Launch all independent subagents in a single message — this maximizes parallelism.

2b: When to Parallelize vs Serialize

Parallel when:

• Subtasks are truly independent (no shared state or outputs needed between them)
• Each subtask is substantial (>30 seconds of work)
• You want to minimize wall-clock time

Sequential when:

• Tasks have tight dependencies (output of one feeds into the next)
• You need to inspect intermediate results before continuing
• The task is small enough that dispatch overhead exceeds the parallelism benefit

2c: Fallback Chains

Every tool can fail. When one does, move to the next in the chain:

• Firecrawl fails → WebSearch → manual URL + WebFetch
• Playwright fails → Firecrawl browser mode → Bash + curl
• Pinecone fails → keep findings in local files → accumulate in context
• Vercel deploy fails → check logs, fix, retry → suggest manual deploy
• Language server unresponsive → fall back to Grep + Glob for code intelligence
• Context7 fails → Firecrawl the documentation site → WebSearch for docs

Report what failed and why before trying the fallback. Never silently skip a subtask.

Phase 3: Domain-Specific Orchestration Patterns

These are reusable multi-tool recipes. Match the user's task to the closest pattern, then adapt.

Research Pattern

When the task involves gathering information from external sources:

1. Discover search tools (Phase 0b: firecrawl, brave, WebSearch)
2. Cast a wide net with broad searches first
3. Use Firecrawl crawl or map for deep site analysis
4. Use Context7 for library/framework documentation
5. Use HuggingFace paper_search for academic/ML research
6. Store extensive findings in Pinecone (if available) to manage context window
7. Synthesize findings — resolve conflicting sources, identify gaps, follow up

Build + Deploy Pattern

When the task involves creating and shipping code:

1. Check for relevant language server (TypeScript, Python, C#, C++)
2. Invoke brainstorming skill if requirements are ambiguous
3. Use frontend-design skill for UI work
4. Use feature-dev or code-simplifier for implementation
5. Write tests first — invoke test-driven-development skill
6. Request code review via CodeRabbit, Greptile, or requesting-code-review
7. Deploy via Vercel (frontend) or Cloudflare Workers (edge/API)
8. Verify with Playwright browser automation (screenshot, interaction test)

Analysis + Report Pattern

When the task involves analyzing data and producing a deliverable:

1. Gather data using the Research Pattern
2. Process and analyze with Python or Node scripts
3. Create visualizations with Mermaid Chart (or inline Mermaid)
4. Generate PDF via GerdsenAI Document Builder — invoke pdf-document-authoring skill
5. For full research reports, use /gerdsenai:research-report for the complete pipeline
6. Store analysis in Pinecone for future reference (if available)

Debug + Fix Pattern

When the task involves finding and fixing a problem:

1. Invoke systematic-debugging skill — 4-phase root cause investigation
2. Use language servers for precise type errors, references, and call hierarchies
3. Use Greptile for codebase-wide pattern search
4. Use Playwright for reproducing browser-based bugs
5. Invoke test-driven-development — write a failing test for the bug, then fix
6. Request code review to verify the fix won't introduce regressions

Security Audit Pattern

When the task involves security assessment:

1. Invoke security-guidance skill for best practices framework
2. Use Greptile to search for common vulnerability patterns (SQL injection, XSS, etc.)
3. Use language servers to trace data flow from user input to sensitive operations
4. Use Firecrawl to check for known CVEs in dependencies
5. Generate findings report via GerdsenAI PDF builder

Phase 4: Output Format Selection

Match the deliverable to what the user actually needs. Don't default to one format — choose deliberately:

User Needs	Format	Tool
Quick answer or explanation	Inline text	Direct response
Code changes	File edits	Write / Edit tools
Professional report or document	PDF	GerdsenAI Document Builder (pdf-document-authoring skill)
Architecture or process visualization	Diagram	Mermaid Chart MCP or inline Mermaid
Live demo or application	Deployed app	Vercel or Cloudflare
Data analysis with charts	Charts + narrative	Python scripts + Mermaid + optional PDF
Persistent knowledge base	Vector embeddings	Pinecone
Visual verification	Screenshots	Playwright
Code quality assessment	Review report	CodeRabbit or Greptile

When the deliverable is a report or formal document, activate the pdf-document-authoring skill for proper formatting (front matter, heading hierarchy, Mermaid diagrams, citations). For full research reports, use the research-report agent which handles the entire pipeline.

Phase 5: Verification and Completion

5a: Cross-check Results

Before presenting final results:

• Did every subtask complete successfully?
• Were fallback chains triggered? Note any quality degradation.
• Are external data sources cited?
• Did code changes pass language server checks?
• Were tests written and passing?
• If using verification-before-completion skill, provide fresh evidence for every success claim

5b: Present Results with Context

Tell the user what happened:

• Which tools and capabilities you discovered and used
• Which you chose NOT to use and why
• What fallbacks were triggered
• How subtasks were parallelized

This transparency builds trust and helps the user understand the value of the orchestration.

5c: Offer Next Steps

Based on the capability manifest, suggest what else is possible. Examples:

• "Pinecone is available — want me to store these research findings for future sessions?"
• "I can deploy this to Vercel if you want a live version."
• "I can generate a PDF report of these findings using the GerdsenAI Document Builder."
• "CodeRabbit could do a deeper review of this code."
• "I can run the superpowers TDD workflow to add test coverage."

Anti-Patterns to Avoid

The "Obvious Tool" Trap

Don't reach for the first tool that comes to mind. A 30-second ToolSearch probe might reveal a specialized tool that saves 10 minutes of manual work. Discovery cost is low; missing a better tool is expensive.

The "One Tool" Trap

Complex problems rarely have single-tool solutions. If you're doing everything with Bash scripts and file reads, step back and check whether specialized MCP tools exist for what you're doing.

The "Context Window" Trap

When accumulating large amounts of research or data, use Pinecone to offload findings instead of keeping everything in the conversation. Context windows have limits; vector databases don't.

The "Sequential by Default" Trap

When you have 4 independent research questions, don't answer them one by one. Dispatch 4 parallel subagents. The wall-clock time improvement is dramatic and the user experience is fundamentally better.

The "Skip Discovery" Trap

Tool availability changes between sessions. Run Phase 0 discovery at the start of every complex task, even if you "remember" what was available last time. It takes seconds and prevents costly mistakes.

The "Gold Plating" Trap

Not every task needs every tool. A simple file edit doesn't need Playwright verification, Pinecone storage, and a PDF report. Match the response to the complexity of the request. Use the minimum tool set that solves the problem well.

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Source: GerdsenAI/GerdsenAI-Agentic-Devtools — distributed by TomeVault.