Harness

Harness -- Agent Team & Skill Architect

A meta-skill for designing domain/project-specific harnesses: defining each agent's role and generating the skills each agent will use.

Core principles:

1. Generate agent definitions (.claude/agents/) and skills (.claude/skills/).
2. Use agent teams as the default execution mode.

Workflow

Phase 1: Domain Analysis

1. Identify the domain/project from the user's request.
2. Identify core task types (creation, validation, editing, analysis, etc.).
3. Check existing agents/skills to prevent conflicts or duplication.
4. Explore the project codebase -- tech stack, data models, key modules.
5. Detect user proficiency -- gauge technical level from conversational cues (terminology, question depth) and adjust communication tone accordingly. Avoid using terms like "assertion" or "JSON schema" without explanation for users with limited coding experience.

Phase 2: Team Architecture Design

2-1. Execution Mode: Agent Teams vs Sub-agents

The default is agent teams. When two or more agents collaborate, prefer agent teams. Team members coordinate via direct communication (SendMessage) and shared task lists (TaskCreate), enabling discovery sharing, conflict discussion, and gap coverage that improve result quality.

Use sub-agents only when there is a single agent, or when inter-agent communication is unnecessary (only result passing is needed).

See reference/agent-design-patterns.md for the comparison table and decision tree.

2-2. Architecture Pattern Selection

1. Decompose the work into specialized domains.
2. Choose a team structure (see reference/agent-design-patterns.md for details):
   • Pipeline: Sequential dependent tasks
   • Fan-out/Fan-in: Parallel independent tasks
   • Expert Pool: Situational selective invocation
   • Producer-Reviewer: Generation followed by quality review
   • Supervisor: Central agent manages state and distributes work dynamically
   • Hierarchical Delegation: Upper agents recursively delegate to lower agents
   • Generator-Evaluator: Strict role separation between generation and evaluation to prevent self-assessment bias. Uses calibrated scoring rubrics and few-shot examples for evaluation.
   • Long-Running Session: Initializer + incremental worker pattern for multi-session projects that span multiple context windows. Uses JSON feature tracking and merge-ready state quality gates.

2-3. Agent Separation Criteria

Evaluate along 4 axes: specialization, parallelism, context, and reusability. See reference/agent-design-patterns.md for the detailed criteria table.

Phase 3: Agent Definition Generation

All agents must be defined as project/.claude/agents/{name}.md files. Never put the role directly into the Agent tool's prompt parameter without an agent definition file. Reasons:

• Agent definitions in files are reusable across sessions.
• Explicit team communication protocols ensure collaboration quality.
• The core value of a harness is separating agents (who) from skills (how).

Even when using built-in types (general-purpose, Explore, Plan), create an agent definition file. Specify the built-in type via the Agent tool's subagent_type parameter, and include role, principles, and protocols in the definition file.

Model configuration: All agents use model: "opus" by default. Always include model: "opus" when invoking the Agent tool. Harness quality depends directly on agent reasoning capability, and opus provides the highest quality. This is configurable -- substitute a different model if cost or speed constraints require it.

Team reconstitution: Only one agent team can be active per session, but you can disband a team between phases and create a new one. For pipeline patterns requiring different specialist combinations per phase, save previous team outputs to files, clean up the team, then create a new team.

Define each agent in project/.claude/agents/{name}.md. Required sections: core role, working principles, input/output protocol, error handling, collaboration. In agent team mode, add a ## Team Communication Protocol section specifying message send/receive targets and task request scope.

Agent frontmatter fields: Beyond name and description, agent definitions support 13+ optional fields including tools, model, permissionMode, memory, maxTurns, skills, mcpServers, hooks, background, effort, isolation, color, and initialPrompt. Use these fields to precisely control each agent's capabilities, security posture, and execution behavior. See reference/agent-frontmatter-spec.md for the complete specification and field-by-field guidance.

Agent memory: For agents that build knowledge across sessions, specify the memory field in frontmatter. Three scopes are available:

• project: Version-controlled and team-shareable (.claude/agent-memory/<name>/). Best for patterns the whole team benefits from.
• user: Cross-project and private (~/.claude/agent-memory/<name>/). Best for personal workflow preferences.
• local: Project-specific, not version-controlled (.claude/agent-memory-local/<name>/). Best for machine-specific environment details.

Memory is curated automatically (200 lines / 25KB limit in context). Include initialPrompt: "Check your memory for established patterns before starting." to ensure agents consult prior knowledge.

See reference/agent-design-patterns.md for agent definition structure, reference/agent-frontmatter-spec.md for the complete frontmatter specification, and reference/team-examples.md for full file examples.

When including a QA agent:

• Use the general-purpose type (Explore is read-only and cannot run verification scripts).
• The essence of QA is cross-boundary comparison, not mere existence checking -- read the API response and front-end hook simultaneously and compare shapes.
• Run QA incrementally after each module completes, not just once after everything is done.
• See reference/qa-agent-guide.md for the detailed guide.

Phase 4: Skill Generation

Generate skills for each agent at project/.claude/skills/{name}/SKILL.md. See reference/skill-writing-guide.md for detailed writing guidance.

4-1. Skill Structure

skill-name/
  SKILL.md          (required: YAML frontmatter with name + description, then markdown body)
  scripts/          (optional: executable code for repetitive/deterministic tasks)
  references/       (optional: conditionally loaded reference documents)
  assets/           (optional: output files such as templates, images)

4-2. Description Writing -- Aggressive Trigger Induction

The description is the skill's only trigger mechanism. Claude tends to be conservative about triggering, so write descriptions aggressively ("pushy").

Bad: "A skill that processes PDF documents" Good: "Read PDF files, extract text/tables, merge, split, rotate, watermark, encrypt, OCR, and all other PDF operations. Use this skill whenever a .pdf file is mentioned or a PDF output is requested."

Key: describe what the skill does + specific trigger situations, and distinguish from similar but non-triggering cases.

4-3. Body Writing Principles

4-4. Progressive Disclosure (Staged Information Loading)

Skills manage context through a 3-tier loading system:

Size management rules:

• When SKILL.md approaches 500 lines, split detailed content into references/ and leave a pointer in the body stating when to read that file.
• Reference files over 300 lines should include a table of contents at the top.
• For domain/framework variations, separate into per-domain files under references/ so only the relevant file is loaded.

4-5. Skill-Agent Connection Principles

• One agent can use 1-to-N skills.
• Multiple agents can share a single skill.
• Skills define "how to do it"; agents define "who does it".

Phase 5: Integration and Orchestration

The orchestrator is a specialized skill that weaves individual agents and skills into a single workflow, coordinating the entire team. While Phase 4 skills define "what each agent does and how", the orchestrator defines "who collaborates in what order and when". See reference/orchestrator-template.md for concrete templates.

5-0. Orchestrator Patterns by Mode

Agent team mode (default): The orchestrator creates a team with TeamCreate, assigns tasks via TaskCreate. Members coordinate via SendMessage. The leader monitors progress and synthesizes results.

Sub-agent mode: The orchestrator calls sub-agents directly via the Agent tool. Sub-agents return results only to the main agent.

5-1. Data Passing Protocol

Recommended combination for agent teams: task-based (coordination) + file-based (artifacts) + message-based (real-time communication).

File-based passing rules:

• Create a _workspace/ folder under the working directory for intermediate artifacts.
• File naming convention: {phase}_{agent}_{artifact}.{ext} (e.g., 01_analyst_requirements.md).
• Output only final artifacts to user-specified paths; preserve intermediate files in _workspace/ for auditing.

5-2. Error Handling

Include error handling policies in the orchestrator. Core principle: retry once, then proceed without that result on re-failure (note the gap in the report); for conflicting data, keep both with source attribution.

See reference/orchestrator-template.md for error type strategy tables and implementation details.

5-4. Context Management

For long-running harnesses that span multiple context windows, prefer context reset (clear window + hand off structured state via files) over automatic compaction. Compaction is lossy -- each summarization round loses detail. Context reset preserves full fidelity by writing all intermediate state to _workspace/ files before clearing the window.

Reset protocol: When approaching context limits, write intermediate state to _workspace/ (progress.txt, features.json, current_state.md), commit current work, then start a fresh session that reads state files to resume.

Session continuity: Each session must end in a "merge-ready state" -- no major bugs in completed features, all tests passing, clear documentation of the next action. This ensures any session can be the last without leaving broken code.

See reference/long-running-harness-guide.md for the full context reset protocol, session continuity patterns, sprint contracts, and the Generator-Evaluator architecture.

5-5. Team Mode: Team Size Guidelines

More members means more coordination overhead. Three focused members outperform five unfocused ones.

Phase 6: Verification and Testing

Validate the generated harness. See reference/skill-testing-guide.md for the detailed testing methodology.

6-1. Structural Verification

• Confirm all agent files are in the correct locations.
• Validate skill frontmatter (name, description).
• Verify cross-reference consistency between agents.
• Confirm no commands were generated (commands are not created).

6-2. Execution Mode Verification

• Agent team mode: verify inter-member communication paths, task dependencies, and team size appropriateness.
• Sub-agent mode: verify each agent's I/O connections and run_in_background settings.

6-3. Skill Execution Testing

1. Write test prompts -- 2-3 realistic prompts per skill, as a real user would phrase them.
2. With-skill vs without-skill comparison -- spawn paired sub-agents (one using the skill, one as baseline) to confirm the skill's added value.
3. Evaluate results -- qualitative (user review) + quantitative (assertion-based) grading. Use assertions for objectively verifiable outputs; rely on user feedback for subjective outputs.
4. Iterative improvement loop -- if issues are found, generalize the feedback into skill fixes (avoid narrow overfitting), re-test, and repeat until satisfactory.
5. Bundle repetitive patterns -- if agents consistently generate the same helper code across tests, pre-bundle it in scripts/.

6-4. Trigger Verification

Validate that each skill's description triggers correctly:

1. Should-trigger queries (8-10) -- diverse phrasings (formal/casual, explicit/implicit).
2. Should-NOT-trigger queries (8-10) -- near-miss queries with similar keywords but requiring a different tool/skill.

Near-miss key insight: obviously unrelated queries ("write a Fibonacci function") have no test value. Good test cases are ambiguous boundary queries like "extract charts from this Excel file as PNG" (spreadsheet skill vs image conversion).

Also check for trigger collisions with existing skills.

6-5. Dry-run Testing

• Review whether the orchestrator skill's phase order is logical.
• Confirm no dead links in data passing paths.
• Verify all agent inputs match the preceding phase's outputs.
• Verify fallback paths for error scenarios are executable.

6-6. Test Scenario Authoring

• Add a ## Test Scenarios section to the orchestrator skill.
• Include at least 1 normal flow + 1 error flow.

Output Checklist

After generation, confirm:

• project/.claude/agents/ -- agent definition files created (even for built-in types)
• project/.claude/skills/ -- skill files (SKILL.md + references/)
• One orchestrator skill (with data flow + error handling + test scenarios)
• Execution mode specified (agent team or sub-agent)
• All Agent calls include model: "opus" parameter
• .claude/commands/ -- nothing created
• No conflicts with existing agents/skills
• Skill descriptions are written aggressively ("pushy")
• SKILL.md bodies are under 500 lines; excess moved to references/
• Execution verified with 2-3 test prompts
• Trigger verification complete (should-trigger + should-NOT-trigger)

References

• Architecture patterns (8 types): reference/agent-design-patterns.md
• Agent frontmatter specification: reference/agent-frontmatter-spec.md -- complete 13+ field reference for .claude/agents/ definitions, including tools, model, permissionMode, memory, and more
• Long-running harness patterns: reference/long-running-harness-guide.md -- Generator-Evaluator architecture, session continuity, context reset strategy, sprint contracts, harness evolution
• Real-world team examples (full file contents): reference/team-examples.md
• Orchestrator templates: reference/orchestrator-template.md
• Skill writing guide: reference/skill-writing-guide.md -- writing patterns, examples, data schema standards
• Skill testing guide: reference/skill-testing-guide.md -- testing/evaluation/iterative improvement methodology
• QA agent guide: reference/qa-agent-guide.md -- for including a QA agent in build harnesses; covers integration coherence verification, boundary bug patterns, and QA agent definition templates based on 7 real-world bug cases

Side Effects and Loop-Safety

This skill is loop_safe: false. It scaffolds skills/agents and may spawn agent teams. Re-running for the same target would overwrite generated artifacts or spawn duplicate teams. Treat each invocation as a single design/build pass, not an idempotent retry.