auto
97Command
AI auto strategy executor. Analyzes task and automatically selects and executes the best strategy: direct execution, hybrid-auto PRD generation, hybrid-worktree isolated development, or mega-plan multi-feature orchestration.
6,218 tagged claude-code-plugin, measured the same way as everything else here.
Browse within: design-system 123accessibility 94Multi-Agent 87code-quality 82claude-code-marketplace 70beta 69figma 66agentic-workflow 64ai-design 61claude-code-skills 57claude-plugin 52claude-ai 50claude-commands 48agent-orchestration 46
Command
AI auto strategy executor. Analyzes task and automatically selects and executes the best strategy: direct execution, hybrid-auto PRD generation, hybrid-worktree isolated development, or mega-plan multi-feature orchestration.
Command
Generate PRD from task description and enter review mode. Auto-generates user stories with priorities, dependencies, and acceptance criteria for parallel execution. Usage: /plan-cascade:hybrid-auto [--flow ] [--tdd ] [--confirm] [--no-confirm] [--spec ] [--first-principles] [--max-questions N] [--agent ]…
Command
Approve the mega-plan and start feature execution. Creates worktrees and generates PRDs for each feature. Usage: /plan-cascade:mega-approve [--flow ] [--tdd ] [--confirm] [--no-confirm] [--spec ] [--first-principles] [--max-questions N] [--auto-prd] [--agent ] [--prd-agent ] [--impl-agent ].
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A setup wizard for LockedIn, a Claude Code add-on that stores personal experience in a knowledge graph and uses it for resumes and cover letters.
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Iterative planning with human checkpoints at every phase.
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Final shipping gate. PR creation, documentation updates, and merge with human approval.
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Post-execution verification gate. Tests, lint, type-check, then see it working.
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Content quality analysis (readability, keyword density, GEO scoring).
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Static analysis for backend performance anti-patterns (N+1 queries, sync I/O, memory leaks).
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Run all auditors and produce a ship-readiness scorecard.
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Protocolo interno compartido para la composición dinámica del equipo de Alfred según tarea, stack y señales runtime.
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Asistente contextual de Alfred Dev. Enruta automáticamente al flujo o comando operativo correcto.
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Auditoría completa del proyecto con 4 agentes en paralelo.
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Full SAIL V2 gap analysis of the AI agent/system in the current repo — component inventory, all 91 catalog risks dispositioned, prioritized recommendations. Writes SAIL-assessment.md.
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SAIL V2 compliance checklist — ISO/IEC 42001, EU AI Act, OWASP LLM/Agentic, DASF, AIUC-1. All five frameworks by default, or only those named.
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Vendor RFP questionnaire from the SAIL V2 catalog — security-vendor coverage questionnaire by default, platform-vendor framing on request.
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A read-only command that reviews local code changes. It checks uncommitted work, or compares the current branch with its default base when the working tree is clean.
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A command for running one-shot or resumable tasks through DeepSeek Harness. It supports read-only runs, optional workspace edits, model and effort choices, and continuation of recorded sessions.
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A setup command that installs DeepSeek Harness and creates a ready-to-use profile named cc. DeepSeek Harness is a tool for running multi-step coding sessions.
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Mirror a just-saved native memory into the shared evolve store so it becomes shareable and auditable.
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Analyze saved trajectories and recall audit events offline to record whether recalled guidelines influenced completed sessions.
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Convert a saved trajectory into a reusable agent skill (SKILL.md + supporting scripts) that future agents can invoke to skip rediscovered work. Use when a session captured a non-trivial workflow worth promoting from a free-text guideline to an executable skill.
Command
Run an audit-and-update pass on the docs/arrows/ overlay. Dispatches on project state — audits an existing overlay, bootstraps the overlay from existing LID docs, or redirects to /map-codebase or /linked-intent-dev for projects without LID.
Command
Bootstrap LID in an existing (brownfield) codebase. Reads every file in scope, proposes lens-based clusterings, walks the user through reconciliation, generates skeleton LLDs/HLD/EARS and the arrow overlay, then prompts the user to flesh out the skeletons. Token-intensive by design.