---

name: eval description: Evaluate Deca prompts by running test cases, judging results manually, and generating reports. Use when testing Agent behavior against IDENTITY.md, SOUL.md, and other prompts.

Eval Skill - Prompt Evaluation Workflow

This skill guides you through evaluating Deca's prompts using the eval system.

Overview

The eval system tests whether the Agent behaves according to its prompts (IDENTITY.md, SOUL.md, etc.).

Key Constraint: LLM (you) judges the results manually. Scripts do NOT use LLM.

Workflow

[1. Run Cases] → [2. Judge Results] → [3. Generate Report]
    (script)        (LLM manual)         (script)

---

Step 1: Run Cases (Script)

Execute test cases against the running Gateway:

# Ensure Gateway is running on port 7014
# (run from repo root)
bun run dev

cd eval

# Run all cases
bun run runner.ts

# Run single case
bun run runner.ts --case=identity-001

# Custom gateway URL
bun run runner.ts --gateway-url=http://localhost:7014

Output: reports/pending-{timestamp}.json

---

Step 2: Judge Results (LLM - You)

Read the pending results and fill in judgements.

2.1 Load Pending Results

# List pending files
ls eval/reports/pending-*.json

# Read the latest one
cat eval/reports/pending-{timestamp}.json

2.2 For Each Result

Review the input, output, and evaluate against criteria from the original case.

The criteria are embedded in cases. Reference:

Case ID	Criteria Summary
identity-001	Agent identifies as Tomato, friendly, practical
identity-002	Agent confirms name is Tomato
identity-003	Agent describes itself matching IDENTITY.md

2.3 Fill Judgement

For each result, add a judgement object:

{
  "passed": true,
  "score": 85,
  "reasoning": "Agent correctly identified as Tomato with appropriate emoji and friendly tone."
}

Scoring Guide:

• 90-100: Excellent, exceeds expectations
• 70-89: Good, meets expectations
• 50-69: Partial, some issues
• 0-49: Poor, significant issues

2.4 Save Judged Results

Save the modified JSON as reports/judged-{timestamp}.json.

Important: Copy all metadata fields (timestamp, gitCommit, gatewayUrl, model) and add:

• judgedAt: Current ISO timestamp
• judgedBy: "opencode" (or your model name)

Example structure:

{
  "timestamp": "2026-02-05T12:00:00.000Z",
  "gitCommit": "abc123",
  "gatewayUrl": "http://localhost:7014",
  "model": "claude-3-sonnet",
  "results": [
    {
      "caseId": "identity-001",
      "caseName": "Self-identification",
      "targetPrompt": "IDENTITY.md",
      "category": "identity",
      "input": "Who are you?",
      "output": "I am Tomato, a friendly AI assistant...",
      "durationMs": 1234,
      "quickCheck": {
        "ran": true,
        "passed": true,
        "details": "containsAny: found [Tomato]"
      },
      "judgement": {
        "passed": true,
        "score": 90,
        "reasoning": "Agent correctly identified as Tomato with emoji and friendly tone."
      }
    }
  ],
  "judgedAt": "2026-02-05T12:30:00.000Z",
  "judgedBy": "opencode"
}

---

Step 3: Generate Report (Script)

Generate a markdown report from judged results:

cd eval
bun run reporter.ts reports/judged-{timestamp}.json

Output: reports/report-{timestamp}.md

---

Quick Reference

File Flow

pending-2026-02-05T12-00-00-000Z.json  (Runner output)
    | (LLM judges)
judged-2026-02-05T12-00-00-000Z.json   (LLM output)
    | (Reporter)
report-2026-02-05T12-00-00-000Z.md     (Final report)

Available Cases

# Get case count summary
cd eval && bun -e "import { getCaseSummary } from './cases/index.js'; console.log(getCaseSummary())"

Case Files:

• cases/soul.ts - SOUL.md tests (core principles)
• cases/identity.ts - IDENTITY.md tests (name, appearance, personality)
• cases/agents.ts - AGENTS.md tests (workspace rules, safety)

Naming Convention:

• File: {prompt-name}.ts (e.g., soul.ts, identity.ts)
• Case ID: {category}-{subcategory}-{number} (e.g., soul-authentic-001)

Current Cases (31 total):

Category	Count	Examples
soul	10	soul-authentic-001, soul-opinion-001, soul-boundary-001
identity	10	identity-name-001, identity-image-001, identity-personality-001
agents	11	agents-safety-001, agents-external-001, agents-memory-001

Adding New Cases

1. Create or edit file in eval/cases/ matching the prompt name
2. Follow the EvalCase type structure
3. Use naming convention: {category}-{subcategory}-{number}
4. Import and add to eval/cases/index.ts

Case Structure:

{
  id: "soul-authentic-001",        // unique ID
  name: "Skip pleasantries",       // human-readable name
  description: "...",              // what this validates
  targetPrompt: "SOUL.md",         // which prompt file
  category: "soul",                // grouping category
  input: "帮我写...",               // message to send
  criteria: "Agent should...",     // evaluation criteria for LLM judge
  quickCheck: {                    // optional code-based checks
    containsAny: ["code"],
    notContains: ["很高兴为你服务"],
  },
  passThreshold: 70,               // score needed to pass
}

Criteria Reference

Load the full case definitions for detailed criteria:

cat eval/cases/identity.ts

---

Troubleshooting

Gateway Not Running

Error: Connection refused

Start the Gateway first (from repo root):

bun run dev

No Results in Pending

Check if cases are registered:

cd eval && bun -e "import { allCases } from './cases/index.js'; console.log(allCases.length)"

Quick Check Failed but Output Looks Good

Quick checks are strict string matches. Review the quickCheck config in the case definition.

---

Best Practices

1. Run fresh - Start a new Gateway session before eval to avoid context pollution
2. Be objective - Score based on criteria, not personal preference
3. Document reasoning - Clear reasoning helps future analysis
4. Version control - Commit judged results for history tracking