agent-evaluation

An evaluation workflow for scoring how well a generative AI agent completes tasks. It uses an LLM as a judge and saves structured results with scores, feedback, and recommendations.

In plain words
What is it for?
Use it for end-to-end evaluations of coding or other agent tasks, including scoring outcomes and reviewing saved evaluation reports.
Why use it?
It provides a consistent way to identify whether an agent fully completed a task and where its execution could improve.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/dzianisv/opencode-plugins/agent-evaluation
Any agent
npx skills add dzianisv/opencode-plugins --skill agent-evaluation
Clone the repo
git clone --depth 1 https://github.com/dzianisv/opencode-plugins

Made for: Claude Code, Codex.

Per session 32 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,879 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 1 finding. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What 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.

ModelPer sessionOnce 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

Measured yesterday against content hash 959378a0a2c2, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

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" \
skills/agent-evaluation/SKILL.md · 282 lines

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>"]
}

Read the full file on GitHub · 282 lines

Changes

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.

  1. yesterday First seen · 282 lines · 32 tokens per session scan A 959378a0a2c2

Subscribe to this mod's changes

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.

Related

Other skills, from other repositories