Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/dzianisv/opencode-plugins/agent-evaluationnpx skills add dzianisv/opencode-plugins --skill agent-evaluationgit clone --depth 1 https://github.com/dzianisv/opencode-pluginsWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00032 | $0.01879 |
| Opus 5 | $0.00016 | $0.00940 |
| Sonnet 5 | $0.00006 | $0.00376 |
| Haiku 4.5 | $0.00003 | $0.00188 |
Grade A, and why
agent-evaluation scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -X POST "https://api.openai.com/v1/chat/completions" \ How it starts
The opening of the file, as written. The whole thing — 282 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Evaluation Skill
Evaluate AI agent task execution using world-class LLM-as-judge patterns from DeepEval, RAGAS, and G-Eval frameworks.
Output Format
Evaluation results are saved to evals/results/eval-${yyyy-mm-dd-hh-mm}-${commit_id}.md
Results Table
| Task Input | Agent Output | Reflection Input | Reflection Output | Score | Verdict | Feedback |
|---|---|---|---|---|---|---|
| Create hello.js... | I've created hello.js with... | Task: Create hello.js Agent Output: ... | Task complete | 5/5 | COMPLETE | Agent produced output; Found completion indicators |
| Fix the bug... | I found the issue and... | Task: Fix bug Agent Output: ... | (none) | 3/5 | PARTIAL | Agent produced output; Missing reflection |
Run Evaluation
# Run E2E evaluation
npx tsx eval.ts
# Or via npm
npm run eval:e2e
# Output saved to: evals/results/eval-2026-01-28-12-30-abc1234.md
Evaluation Rubric (0-5)
| Score | Verdict | Criteria |
|---|---|---|
| 5 | COMPLETE | Task fully accomplished. All requirements met. Optimal execution. |
| 4 | MOSTLY_COMPLETE | Task done with minor issues. 1-2 suboptimal steps. |
| 3 | PARTIAL | Core objective achieved but significant gaps or errors. |
| 2 | ATTEMPTED | Progress made but failed to complete. Correct intent, wrong execution. |
| 1 | FAILED | Wrong approach or incorrect result. |
| 0 | NO_ATTEMPT | No meaningful progress. Crashed or no output. |
Pass threshold: >= 3 (development), >= 4 (production)
Evaluation Prompt Template
Use this prompt for LLM-as-judge evaluation:
You are an expert evaluator assessing AI agent task completion.
## Original Task
{{task}}
## Execution Trace
{{trace}}
## Final Output
{{output}}
## Evaluation Criteria
1. Was the core objective achieved?
2. Were appropriate tools selected?
3. Were tool arguments correct?
4. Was execution efficient (minimal steps)?
5. Is the final output accurate and complete?
## Scoring Rubric
- 5: COMPLETE - All requirements met perfectly
- 4: MOSTLY_COMPLETE - Minor issues only
- 3: PARTIAL - Core done but significant gaps
- 2: ATTEMPTED - Progress made but failed
- 1: FAILED - Wrong approach or result
- 0: NO_ATTEMPT - No meaningful progress
## Instructions
1. Analyze the execution step-by-step
2. Identify specific issues or strengths
3. Score using the rubric
4. Provide actionable recommendations
## Response Format (JSON only)
{
"reasoning": "<step-by-step analysis>",
"score": <0-5>,
"verdict": "<COMPLETE|MOSTLY_COMPLETE|PARTIAL|ATTEMPTED|FAILED|NO_ATTEMPT>",
"feedback": "<1-2 sentence summary>",
"recommendations": ["<improvement 1>", "<improvement 2>"]
}
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- yesterday First seen · 282 lines · 32 tokens per session scan A 959378a0a2c2
agent-evaluation is a skill published in the GitHub repository dzianisv/opencode-plugins (9 stars, last pushed 2mo ago), licensed MIT. It adds 32 tokens to every session and 1,879 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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