Harness transforms Claude Code from a stateless tool into an intelligent, context-aware collaborator that understands your project, respects your conventions, and improves over time.
Experience the leap from raw automation to engineered precision.
Harness organizes every aspect of Claude Code configuration into seven foundational pillars. Each is independently auditable and improvable.
Define discrete atomic capabilities. Chain complex behaviors through modular skill blocks that can be versioned and tested independently.
Precision knowledge injection. Manage how data is chunked, prioritized, and served to the model within token constraints.
Dynamic intent detection and multi-agent delegation. Route queries to the most efficient model or specialized skill.
Long-term memory management. Store user preferences, past interactions, and operational state across sessions.
Automated evaluation layers. Run validation scripts, schema checks, and filters on every output before it ships.
RBAC for AI agents. Define what actions are permitted and which require human-in-the-loop authorization.
Tone management and interaction tuning. Control how the model expresses uncertainty and presents responses.
Each skill is a focused, composable unit that implements one or more pillars. Call them individually or let the router dispatch automatically.
Master router that classifies intent and dispatches to the right specialist skill. Handles simple lookups directly and coordinates multi-pillar work by sequencing atomic skills. The single entry point for all harness engineering.
Bootstraps a complete harness by orchestrating a five-phase sequence. Analyzes the project, generates CLAUDE.md, configures permissions, initializes memory, installs hooks, and validates the context budget — all in one pass.
Evaluates an existing harness against all 7 pillars, scoring each from 0 (absent) to 3 (advanced). Produces a structured report with the total maturity score, identifies the weakest pillar, and recommends prioritized improvements with direct skill routing.
Standalone — evaluates existing stateContinuous improvement engine that cycles: audit the harness, identify the weakest pillar, route to the atomic skill that fixes it, validate the improvement didn't regress others, log what changed, and repeat.
Analyzes codebase tech stack, git history, engineering patterns, and architecture to generate a project-tailored CLAUDE.md and .claude/ directory structure. The foundational artifact that all other skills build upon.
Optimizes what information Claude loads and when. Analyzes token budget, eliminates wasted context, restructures CLAUDE.md for progressive disclosure, and ensures the most critical instructions are front-loaded.
→ Context EngineeringDesigns and maintains the cross-session memory system — MEMORY.md index, typed memory files (user, feedback, project, reference), staleness auditing, and proper organization so future conversations have complete context.
→ Persistence & StateConfigures permission boundaries in settings.json using the blast radius principle: auto-allow reads and local builds, prompt for shared-state mutations. Detects tooling and tunes specificity to eliminate unnecessary prompts.
→ Permissions & SafetyDesigns and implements automated hooks that fire before/after tool use or at conversation end. Provides patterns for auto-formatting, linting, secret scanning, command gating, test-on-change, and system health checks.
→ Quality GatesAnalyzes project workflows and decomposes them into a well-composed skill system. Discovers workflow candidates, classifies by complexity, designs atomic and composed skills, checks for overlaps and gaps, and recommends priorities.
→ Skill CompositionDesigns agent orchestration strategies including subagent delegation, parallelization patterns, build-loop workflows, worktree isolation, and mega-skill routing. Produces concrete orchestration plans tied to project constraints.
→ Orchestration & RoutingDesigns quality gates and feedback loops at multiple stages: post-edit, pre-commit, pre-push, pre-merge, and conversation-end. Implements self-evaluation patterns, regression detection, and automated feedback loop closure.
→ Quality GatesTunes human-AI interaction by configuring trust levels, output style, status reporting, decision surfacing, error communication, and personality. Captures feedback memories to improve interaction patterns across sessions.
→ Ergonomics & TrustThree primary workflows cover the full lifecycle — from initial setup to ongoing optimization.
Analyze project structure, frameworks, and patterns. Generate a tailored CLAUDE.md and settings.json that aligns with your architectural requirements.
Set up tool-specific permission boundaries. Auto-allow safe operations, prompt for destructive ones.
Initialize the persistent memory system. Seed with user profile, project context, and key decisions.
Add automated quality gates: code formatting, linting, secret scanning, and test triggers.
Fine-tune what Claude loads. Trim waste, front-load critical info, reference files instead of inlining.
Scan CLAUDE.md, settings.json, memory system, hooks, and skill definitions.
Rate each of the 7 pillars from 0 (not configured) to 3 (advanced). Total maturity out of 21.
Generate a structured report with pillar scores, top 3 recommendations, and direct routing to the skill that fixes each gap.
Run a full 7-pillar audit to identify the current maturity score.
Find the pillar with the lowest score. This is where improvement has the highest impact.
Route to the atomic skill for that pillar. Make targeted, validated improvements.
Re-score to confirm improvement. Check for regressions. Continue to the next weakest pillar.
Every harness is evaluated across seven core dimensions. Scores range from 0 (not configured) to 3 (advanced), providing a clear roadmap for improvement.
Begin your journey with these foundational steps.
Copy the skills/ directory into your project.
Call harness-engineer to start the analysis.
Run harness-init for fresh projects to set defaults.
Run harness-audit for existing setups to check compliance.
Use harness-loop for continuous improvement. Establish a recurring cycle of refinement to maintain top-tier maturity scores across all pillars.