ACP Harness

Purpose

This skill provides a unified toolkit for ACP client usage and agent evaluation, optimized for TypeScript/JavaScript projects using Bun.

1. ACP Client API - Headless programmatic access to ACP-compatible agents
2. Evaluation Harness - Run prompts against agents and capture full trajectories

Use this when:

• Comparing skills across different agents (Claude Code, Cursor, OpenCode, Amp, Goose, Factory)
• Evaluating built-in tools vs MCP servers vs skills for the same task
• Generating training data with full trajectory capture
• Running regression tests in CI/CD pipelines
• Building multi-agent applications on a headless transport layer

Foundation Use Cases

The harness is a foundation layer - it captures trajectories; scoring happens downstream.

flowchart LR
    Harness["ACP Harness"] -->|"trajectories"| Scoring["Braintrust / Custom Script"]
    Scoring -->|"scores"| Decision["Informed Choices"]

Use Case	Harness Provides	You Build
Cross-agent skill eval	Same prompts → multiple agents → trajectories	Scoring pipeline (Braintrust, custom)
Tool comparison	Trajectory with tool/skill attribution	Diff analysis, preference data
Training data	Structured I/O with tool calls, plans, thoughts	SFT/DPO formatting
Regression testing	Deterministic prompt → trajectory capture	CI integration, golden file comparison
Multi-agent apps	createACPClient transport layer	Session management, UI, agent switching

Agents Supporting Skills

Skills can be installed across multiple agents, enabling cross-agent comparison:

Agent	Skills Directory	Install Command
Claude Code	.claude/skills/	./install.sh --agent claude
Cursor	.claude/skills/	./install.sh --agent cursor
OpenCode	.opencode/skill/	./install.sh --agent opencode
Amp	.agents/skills/	./install.sh --agent amp
Goose	.claude/skills/	./install.sh --agent goose
Factory	.factory/skills/	./install.sh --agent factory

Example: Comparing Built-in vs Skill

# Run same prompt with built-in tool
bun scripts/run-harness.ts prompts.jsonl \
  --agent claude-code-acp \
  -o results-builtin.jsonl

# Run same prompt with custom skill installed
bun scripts/run-harness.ts prompts.jsonl \
  --agent claude-code-acp \
  --cwd /project/with/typescript-lsp-skill \
  -o results-skill.jsonl

# Compare trajectories - which used better tools? faster? more accurate?
diff <(jq '.toolCalls' results-builtin.jsonl) <(jq '.toolCalls' results-skill.jsonl)

Execution Environment

Recommendation: Run evaluations in Docker containers for consistent, isolated execution.

# Build and run with Docker Compose
docker compose -f docker-compose.acp.yml run --rm acp-harness

# Or build directly
docker build -f Dockerfile.acp -t acp-harness .
docker run --rm -e ANTHROPIC_API_KEY acp-harness

Docker provides:

• Consistent environment across local and CI
• Filesystem isolation without app-level sandboxing
• Reproducible results for training data generation

See assets/ for example container configurations:

• Dockerfile.acp - Base container with Bun and git
• docker-compose.acp.yml - Compose file with volume mounts for results

Non-Goals

This harness is optimized for TypeScript/JavaScript projects using Bun. It is not designed for:

• Python projects - Use SWE-bench, Braintrust Python SDK
• Academic model benchmarking - Use EleutherAI lm-evaluation-harness
• IDE integrations - Use Copilot Evaluation Harness
• SaaS observability - Use Braintrust, Langfuse platforms directly

Quick Reference

Resource	Description
scripts/run-harness.ts	Execute prompts against agent, capture trajectories
client-api.md	createACPClient configuration, helpers
output-formats.md	JSONL schemas, format options
downstream.md	Integration patterns (Braintrust, jq, LLM-as-judge)
llm-judge-templates.md	Evaluation prompt templates

Evaluation Workflow

flowchart LR
    Prompts["prompts.jsonl"] --> Harness["run-harness.ts"]
    Agent["ACP Agent"] --> Harness
    Harness --> Summary["summary.jsonl"]
    Harness --> Judge["results.md + results.full.jsonl"]
    Summary --> JQ["jq analysis"]
    Judge --> LLM["LLM-as-judge"]

1. Prepare - Create prompts.jsonl with evaluation cases
2. Execute - Run harness against target agent
3. Capture - Trajectories streamed to output files
4. Analyze - Pipe to downstream tools for scoring

Harness Script

Basic Usage

bun scripts/run-harness.ts <prompts.jsonl> --agent <command> [options]

Arguments

Flag	Description	Default
prompts.jsonl	Input file with evaluation prompts	Required
-a, --agent	ACP agent command	"claude-code-acp"
-o, --output	Output file/path	stdout
-c, --cwd	Working directory for agent	current
-t, --timeout	Request timeout in ms	60000
-f, --format	Output format: summary, judge	summary
--progress	Show progress to stderr	false
--append	Append to output file	false
--mcp-server	MCP server config JSON (repeatable)	none

Examples

# Summary format (default) - minimal JSONL
bun scripts/run-harness.ts prompts.jsonl -o results.jsonl

# Judge format - creates two files for two-tier evaluation
bun scripts/run-harness.ts prompts.jsonl --format judge -o results
# Creates: results.md (summary with step IDs) + results.full.jsonl (complete trajectory)

# With MCP server (stdio transport)
bun scripts/run-harness.ts prompts.jsonl \
  --mcp-server '{"type":"stdio","name":"fs","command":["mcp-filesystem","/data"]}'

# With MCP server (HTTP transport)
bun scripts/run-harness.ts prompts.jsonl \
  --mcp-server '{"type":"http","name":"api","url":"http://localhost:3000"}'

# Different agent (Droid ACP adapter)
bun scripts/run-harness.ts prompts.jsonl --agent droid-acp -o results.jsonl

# Stream with progress
bun scripts/run-harness.ts prompts.jsonl --progress -o results.jsonl

Input Format

Each line in prompts.jsonl:

{"id":"test-001","input":"Create a primary button","expected":"should contain <button>","metadata":{"category":"ui"}}
{"id":"test-002","input":"Write a function for form validation","metadata":{"category":"logic"}}

Field	Required	Description
id	Yes	Unique identifier
input	Yes	Prompt text for the agent
expected	No	Expected output (for downstream scoring)
metadata	No	Tags, category, difficulty for filtering
timeout	No	Override default timeout for this prompt

Output Formats

Summary Format (default)

Minimal JSONL for quick metrics and analysis:

{"id":"test-001","input":"Create a button","output":"I created...","toolCalls":["Write"],"status":"passed","duration":1234}

Judge Format (two-tier)

Creates two files for LLM-as-judge evaluation:

<output>.md - Markdown summary with step IDs and code previews:

## Evaluation Record: test-001

**Input:** Create a primary button

**Trajectory:**
1. [THOUGHT] I'll create a styled button... [->test-001-step-1]
2. [TOOL:Write] -> completed (234ms) [->test-001-step-2]
   File: src/button.tsx (847 chars)
   ```tsx
   import { createStyles } from '@plaited/acp'

   type ButtonProps = {
     label: string

   // ... 30 lines omitted ...

   export const Button = ({ label }: ButtonProps) => (
     <button {...styles.btn}>{label}</button>
   )

3. [MESSAGE] I created the button... [->test-001-step-3]

Output: I created the button with primary styling. Metadata: category=ui, agent=claude-code-acp Status: passed Duration: 1234ms

---

**`<output>.full.jsonl`** - Complete trajectory with step IDs for correlation:

```jsonl
{"id":"test-001","input":"...","output":"...","trajectory":[{"type":"thought","content":"...","timestamp":100,"stepId":"test-001-step-1"},{"type":"tool_call","name":"Write","status":"completed","input":{...},"output":{...},"duration":234,"stepId":"test-001-step-2"}],...}

Usage patterns by judge context window:

Judge Model	Strategy
Gemini (1M+ tokens)	Feed results.full.jsonl directly
Claude/GPT-4 (128-200k)	Use results.full.jsonl for most runs
Smaller models	Use results.md, retrieve specific steps by ID as needed

Programmatic Usage

import { createACPClient, createPrompt, summarizeResponse } from '@plaited/acp'

// Requires: npm install -g @zed-industries/claude-code-acp
const client = createACPClient({
  command: ['claude-code-acp'],
  cwd: '/path/to/project',
})

await client.connect()
const session = await client.createSession()

const { updates } = await client.promptSync(
  session.id,
  createPrompt('Create a button with hover state')
)

// Full trajectory is in updates
const summary = summarizeResponse(updates)
console.log({
  text: summary.text,
  toolCalls: summary.completedToolCalls,
  hasErrors: summary.hasErrors
})

await client.disconnect()

See client-api.md for complete API documentation.

Downstream Integration

The harness outputs standard JSONL that pipes to any tool:

# Filter with jq
cat results.jsonl | jq 'select(.metadata.category == "ui")'

# Count tool usage
cat results.jsonl | jq -s 'map(.toolCalls | length) | add'

# Feed full trajectory to Gemini (large context)
cat results.full.jsonl | your-gemini-judge.ts

See downstream.md for integration patterns with Braintrust, Gemini, and custom scorers.

Evaluation Targets

Target	How to Evaluate
Agent capability	Direct prompts, analyze trajectory quality
Skills	Set --cwd to project with skill, test skill-specific prompts
MCP Servers	Use --mcp-server flag, verify tool usage in trajectory

Skill Evaluation

bun scripts/run-harness.ts skill-prompts.jsonl \
  --cwd /project/with/skill \
  -o results.jsonl

MCP Server Evaluation

bun scripts/run-harness.ts mcp-prompts.jsonl \
  --mcp-server '{"type":"stdio","name":"fs","command":["mcp-filesystem"]}' \
  -o results.jsonl

Related

• @plaited/acp - Core ACP client module