Cavekit

Plugin ck in hashgraph-online/awesome-codex-plugins. Commands read from the repo's manifest — today.

Skills (23)

• autonomous-loop

  How Cavekit's autonomous execution loop works — state machine, stop hook, completion sentinel, lock, budgets, iteration cap. Read this skill any time you are about to run /ck:make, debug a stuck session, or write a new command that needs the loop. Trigger phrases: "how does the loop work", "stop hoo

• blueprint-writing

  How to write Blueprint-quality blueprints that AI agents can consume effectively. Covers implementation-agnostic blueprint design, testable acceptance criteria, hierarchical structure, cross-referencing, blueprint templates, greenfield and rewrite patterns, blueprint compaction, and gap analysis. Tr

• brownfield-adoption

  Step-by-step process for adopting Cavekit on an existing codebase. Covers the 6-step brownfield process, bootstrap prompt design, spec validation against existing behavior, and the decision between brownfield adoption vs deliberate rewrite. Trigger phrases: "brownfield", "existing codebase", "add Ca

• capability-discovery

  Detect which MCP servers, Claude Code plugins, and CLI tools are available in the current environment, so kits and build sites can bind to real capabilities instead of imagined ones. Runs via /ck:init --tools-only or `cavekit-tools.cjs discover`. Writes .cavekit/capabilities.json. Trigger phrases: "

• cavekit-writing

  How to write Cavekit-quality kits that AI agents can consume effectively. Covers implementation-agnostic cavekit design, testable acceptance criteria, hierarchical structure, cross-referencing, cavekit templates, greenfield and rewrite patterns, cavekit compaction, and gap analysis. Trigger phrases:

• caveman-internal

  Internal token-compression protocol for Cavekit agent artifacts — handoff notes, artifact summaries, context bundles, review notes, status lines. NOT the user-facing /caveman skill; that one compresses assistant replies to the user. This skill compresses machine-to-machine prose so the next agent in

• caveman

  Ultra-compressed communication mode. Cuts token usage ~75% by speaking like caveman while keeping full technical accuracy. Supports intensity levels: lite, full (default), ultra. Use when user says "caveman mode", "talk like caveman", "use caveman", "less tokens", "be brief", or invokes /caveman. Al

• complexity-detection

  Classify a task or feature as quick, standard, or thorough so the rest of the pipeline (budget sizing, model routing, review depth) can right-size itself. Used by /ck:sketch to default the kit's complexity, by /ck:map to assign task depth, and by /ck:make for per-task budgets. Also invoked by the ck

• context-architecture

  Progressive disclosure architecture for organizing project context as a DAG (directed acyclic graph). Agents enter at the root and traverse only the subgraph relevant to their task. Covers the 4-tier information flow (refs → kits → plans → impl), CLAUDE.md hierarchy across context/ and source tree,

• convergence-monitoring

  Detecting whether agent iterations are converging toward a stable solution or hitting a ceiling. Covers convergence signals, ceiling detection, non-convergence diagnosis, test pass rate as a convergence metric, and forward progress tracking for large projects. Trigger phrases: "convergence", "is the

• design-system

  How to write and maintain a DESIGN.md in the 9-section Google Stitch format. Covers the 9-section structure, design token conventions, quality standards, integration with kits and build tasks, revision patterns, and collection import. Trigger phrases: "design system", "DESIGN.md", "visual design spe

• documentation-inversion

  Inverts the traditional documentation flow from code-to-wiki-for-humans (which rots) into code-to-CLAUDE.md-to-skills-for-agents (which stays current). Each module gets a machine-readable CLAUDE.md, navigation skills teach agents how to explore libraries, and plugins package skills for on-demand loa

• graphify-integration

  Optional knowledge-graph integration. When `graphify-out/graph.json` is present, architect/researcher/reviewer/task-builder agents can query symbol-level dependencies (IMPORTS, CALLS, EXTENDS, IMPLEMENTS, DEPENDS_ON) instead of grepping. Degrades gracefully when the graph is missing. Trigger phrases

• impl-tracking

  Implementation tracking documents for maintaining living records of what was built, what is pending, what failed, and what dead ends were explored. Covers the full tracking document template, dead ends prevention, cross-iteration continuity, spec compaction, and inter-session feedback protocol. Trig

• karpathy-guardrails

  Behavioral guardrails for Cavekit agents. Four principles — think before coding, simplicity first, surgical changes, goal-driven execution — that prevent over-engineering, silent assumptions, scope creep, and unfocused work. Every task-builder, reviewer, planner, and inspector must internalize these

• methodology

  Core Cavekit methodology — the master skill that teaches the Hunt lifecycle and routes to all sub-skills. Covers the Specify Before Building principle, the scientific method analogy, the four-phase Hunt lifecycle, decision matrix for when to use Cavekit, and build pipeline analogy. Trigger phrases:

• peer-review-loop

  Peer Review Ralph Loop — combines Cavekit kits with a Ralph Loop and true cross-model peer review using Codex (OpenAI). Claude builds from specs; Codex reviews adversarially. Primary path: Codex CLI delegation via codex-review.sh (fast, no MCP overhead). Legacy fallback: Codex as MCP server when CLI

• peer-review

  Patterns for using a second AI agent or model to challenge the primary builder agent's work. Covers six review modes (Diff Critique, Design Challenge, Threaded Debate, Delegated Scrutiny, Deciding Vote, Coverage Audit), how to set up peer review with any model via MCP server, peer review iteration l

• prompt-pipeline

  How to design the numbered prompt pipeline that drives Hunt phases in Cavekit. Covers greenfield 3-prompt patterns, rewrite 6-9 prompt patterns, shared principles, prompt engineering best practices, task templates, and time guards. Trigger phrases: "prompt pipeline", "design prompts for SDD", "creat

• revision

  Trace bugs and manual fixes back to kits and prompts, then fix at the source so the iteration loop can reproduce the fix autonomously. Covers the 6-step revision process for commit sweeps AND the single-failure backpropagation protocol (six steps: TRACE, ANALYZE, PROPOSE, GENERATE, VERIFY, LOG) that

• speculative-pipeline

  A pipeline execution strategy where downstream stages start before upstream stages finish, using staggered timing with configurable delays. The leader begins first, and followers start after a delay, building from whatever partial output exists. Combined with convergence loops, early follower output

• ui-craft

  Authoritative guide for implementing stunning, accessible, performant UI. Synthesizes design engineering philosophy, accessibility standards, animation principles, spatial design, typography, color systems, and component craft into a single actionable reference. Complements the design-system skill (

• validation-first

  Validation-first design for AI agent output — every spec requirement must be automatically verifiable. Covers the 6-gate validation pipeline, phase gates between Hunt phases, merge protocol, completion signals, and acceptance criteria design patterns. Trigger phrases: "validation gates", "quality ga

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