claude-octopus

Multi-AI requirements scoping using available external providers (Double Diamond Define phase). Priority triggers: octo define, octo scope, co-define, co-scope. DO NOT use for implementation, research, review/validation, or built-in commands.

Multi-AI validation, scoring, and review using available external providers (Double Diamond Deliver phase)

Multi-AI implementation using available external providers (Double Diamond Develop phase). DO NOT use for simple code edits, reading/reviewing code, built-in commands, or trivial single-file changes.

Multi-AI research using available external providers (Double Diamond Discover phase)

Decompose and execute large changes, migrations, or multi-issue fixes in parallel with quality gates

NLSpec authoring — use when you need a structured specification from multi-AI research and consensus

Review system architecture, simplify boundaries, or compare interface designs from repository evidence

Quick execution for ad-hoc tasks without full workflow overhead — use for small, self-contained requests

Thorough research across multiple sources — use for complex topics needing broad synthesis

OWASP compliance, vulnerability scanning, and adversarial red team testing — use for security reviews

Design UI/UX systems with style guides, palettes, typography, and component specs for new interfaces

Audit whether a multi-agent setup earns its coordination cost — use before adding an agent, or when a workflow feels slow or agents agree without adding signal

Audit codebases for quality, consistency, and broken patterns — use for pre-release or tech debt review

Principles for writing skills that behave the same way every run — use when adding, editing, or reviewing a skill in this plugin

Expert multi-AI code review with inline PR comments — use for thorough quality and security analysis

Extract patterns and anatomy from URLs — use to reverse-engineer content strategies from live pages

Auto-detect work context (Dev vs Knowledge) — use to tailor workflows based on current task type

GitHub Copilot CLI as optional zero-cost provider via copilot -p programmatic mode

Project remaining workflow cost from per-phase averages — warns on budget ceiling overruns

Run a configurable multi-LLM council with personas, budget caps, synthesis, veto gates, and optional implementation handoff.

Trace codepaths in diffs, map against tests, auto-generate missing coverage — use before shipping PRs

Structured multi-provider AI debates between Claude and available advisors — use for critical decisions

Debug a reproducible symptom with a bounded feedback loop and original-scenario verification

Present options with trade-offs for informed decision-making — use when choosing between approaches

Generate slide deck presentations from briefs — use when you need slides, pitch decks, or visual summaries

Persist design documents with branch tracking, revision chains, and cross-session discovery

Convert markdown to DOCX, PPTX, XLSX, PDF office documents — use when you need exportable deliverables

Post-ship doc sync across project markdown. Use when: sync docs, update docs, document changes, release notes.

Environment diagnostics — check providers, auth, config, hooks, scheduler, and more

Reverse-engineer design systems, tokens, and components from live products or screenshots

Run a full build-and-ship pipeline from a spec — use for hands-off project generation

Wrap up a branch — run tests, create PR, merge or discard — use when implementation is done

Move incoming issues and pull requests through triage states until each is actionable or closed — use when the queue has piled up or a report arrives unsorted

Use when starting a complex or ambiguous task that risks scope drift

Track project blockers, bugs, and gaps across sessions — use when issues pile up or need triage

Run tasks in a loop until goals are met — use for iterative refinement, polling, or convergence

Switch to Knowledge Work mode for research and writing — use when task is non-code focused

Craft better prompts using proven optimization techniques — use when your prompt needs refinement

Use when choosing native or multi-LLM handling for init, review, or security requests

Decompose large tasks across parallel agents — use for migrations, multi-file refactors, or batch work

Write an AI-optimized PRD using multi-AI orchestration — use when scoping a new feature or product

Interrogate a plan, decision, or design one question at a time until it holds — use to stress-test your own thinking before committing to it

Prototype one risky assumption within a fixed budget, then keep or discard the result

Pick up where you left off from a previous session — use after context resets, compaction, or new conversations

Use when a reviewer, CI bot, or another AI leaves feedback to address

Roll back to a previous checkpoint via git — use when a change went wrong and you need to revert

URL validation and content sanitization for untrusted sources — use when handling external input safely

Package and finalize completed work for delivery — use when a feature is done and ready to ship

Use when a PR or feature needs both specification and code-quality review

Show where you are in the workflow and what to do next — use for progress checks and orientation