agent-evaluation

name	agent-evaluation
description	Run one specified Test Agent on one specified Benchmark Case exactly once, privately score that execution, and return one protocol result.
short_description	Run and score one isolated Benchmark Case.
short_description_zh	隔离执行并评分一个 Benchmark Case。
version	5
updated	2026-07-29T17:20:58.000Z

Agent Evaluation

Handle one evaluation request from a run_subagent caller: run the specified Test Agent on one Benchmark Case once, score that execution privately, and return one protocol result.

The top-level Benchmark Designer or Optimizer owns all Case and Run loops, concurrency, and follow-up handling. This worker handles no other Case or Run, launches no evaluator or subagent, modifies no Agent or Benchmark, and never writes scoreboard.yaml. Use the Penguin CLI only to launch the specified Test Agent; do not use it to create another phase, designer, optimizer, or evaluator.

Operate silently. Call tools without progress messages. Across all streamed and final responses, the only worker-authored text must be the final plain protocol YAML. Emit no narration, headings, Markdown fences, summaries, private scoring details, or other text.

Before you start

Use this Skill only for a complete request from a run_subagent caller. If the request is incomplete or inconsistent, return invalid_request through the protocol instead of asking the user a question.

Contract

Require exactly one value for every field below:

protocol_version: 1
case_id: <case_id>
run: <1_based_run_index>
expected_version: <tested_agent_state_version>
test_agent_id: <test_agent_id>
benchmark_id: <benchmark_id>
provider: <provider>
model_id: <model_id>

One request represents one Test Agent execution. The run value identifies that execution; it is not a repeat count. provider and model_id must both be non-empty and select that exact configured model. If a required field is missing, duplicated, or conflicting, return invalid_request without creating a Workspace or launching the Test Agent.

Return a scored result when the Test Agent ran and the Rubric could be applied. Wrong, malformed, or missing Test Agent output is still a scored result. Return an evaluation failure when the request, Benchmark, launch, version check, Trace binding, or scoring process prevents a valid score.

Resolve the Project, Test Agent, Benchmark, and Case only from the explicit request and Environment App Data Dir. Reject traversal, symlink escape, or any path outside the requested Test Agent. Never read a Project configuration file, credential, or vault.

Prepare

Use the App Data Dir from the Environment:

TEST_AGENT_DIR = <app_data_dir>/agents/<test_agent_id>
BENCHMARK_DIR = <test_agent_dir>/benchmarks/<benchmark_id>

Reject path traversal, symlink escape, or any resolved path outside the requested Test Agent. Inspect only the requested Agent State, Benchmark config and Case, isolated Test Workspace, and Traces needed to verify this execution. Do not inspect another Agent, Project secrets, hidden configuration, or unrelated Workspaces or Traces.

Require agent_state/system_config.yaml, benchmark_config.toml, <case_id>/statement/README.md, and <case_id>/rubric/README.md. Treat run only as the caller-owned label for this evaluation and return it unchanged; do not read or validate the total Run count. The top-level Agent State version, defaulting to 1, must equal expected_version; otherwise return version_changed. Read and snapshot model.thinking_level from this Target Agent config, using the normal Agent-config default medium only when the field is absent. This configured value is the evaluation thinking_level; do not require or read thinking metadata from a Trace.

Before launch, snapshot every file under the Case's statement/ and rubric/ directories. Require a usable Rubric whose scoring items total exactly 100 points. Create a unique Workspace under <test_agent_dir>/workspaces/, resolve it to an absolute canonical path, and verify that the resolved path remains under that directory. Copy only statement/ into it. The Test Agent may see the Statement and its own State, but never the Rubric, Gold answers, scoring rules, or Evaluator reasoning.

Run and verify

Use an existing verified Penguin CLI or repository-local launcher. Do not install or probe a launcher. Snapshot the isolated Workspace and record the existing Trace files.

Resolve PROJECT_DIR, then derive and verify PROJECT_ID, then derive and verify PENGUIN_HOME. Perform these as separate shell statements in this order. Never compress the assignments onto one command line, derive a value before its input exists, or substitute another Penguin home. Before launch, confirm that PROJECT_ID equals the basename of PROJECT_DIR and PENGUIN_HOME equals its dirname.

Start one foreground execution with a fresh top-level Session. With an explicit pair, use:

PROJECT_DIR="<app_data_dir>"   # the App Data Dir value from your Environment section is the project root
PROJECT_ID="$(basename "$PROJECT_DIR")"
PENGUIN_HOME="$(dirname "$PROJECT_DIR")"
export PENGUIN_HOME
penguin run \
  --message "Read README.md in the current Workspace and complete the task exactly as specified there." \
  --provider "<provider>" --model-id "<model_id>" --project-id "$PROJECT_ID" \
  --agent-id "<test_agent_id>" --workspace "<absolute_unique_workspace_path>" --approve allow-all

Use the exact requested Agent, Project, absolute Workspace path, and model pair. Never omit either model flag and never fall back to a Project default. If a launch fails, retry only when unchanged Workspace and Trace evidence proves that the Test Agent did not start. Every retry must follow a new diagnosis and apply a specific correction; never repeat an unchanged launch. Do not impose a numeric retry limit while distinct safe repairs remain. Return evaluation_failed when no new repair remains, external configuration is required, or it is unclear whether the Test Agent started.

Verify after the run that the State version, configured model.thinking_level, and both directory snapshots are unchanged. Return version_changed when the State version or configured thinking level differs and benchmark_invalid when the Statement or Rubric differs.

Inspect only new or changed Traces. Bind exactly one root Test Trace whose Workspace, Agent State path, provider, and model match this request. Ignore unrelated parallel Traces and exclude the root Trace's directly referenced child Sessions. Return evaluation_failed if there is no unique match. Read the actual non-empty provider and model_id from the bound root Trace's session_meta; return evaluation_failed if either is unavailable. Use the unchanged Target Agent configuration snapshot—not Trace metadata—for thinking_level.

Score

Inspect only the isolated Workspace, the bound root Trace, its directly referenced child Traces, and the private Rubric. Apply every scoring item and allowed equivalent. Keep Rubric contents, Gold answers, per-item scoring, and scoring rationale private.

A wrong answer, missing artifact, malformed output, or task failure attributable to the Test Agent is scored behavior and returns status: ok. A launcher, Trace-binding, or Evaluator failure is not scored. Return benchmark_invalid when the Rubric cannot be applied and evaluation_failed when the score is non-finite or outside 0..100.

Set duration_ms from the root Test Session. Compute cost only from reliable final cumulative usage or cost already recorded in that Session and directly referenced child Traces found in the same bounded pass. Never browse, query a pricing service, or infer cost from external model prices. If the required data is unavailable, return cost: null. Missing cost data must not invalidate a score.

Round score to two decimal places. Preserve a non-null cost at the precision recorded in the Trace; do not round it. Write duration_ms as a non-negative integer rounded to the nearest millisecond.

Return

Return the required YAML as the only worker-authored text. Do not wrap it in backticks or a Markdown fence.

If the caller reports that your response formatting was invalid, use the scored or failed result already present in this Session and resend only the clean protocol YAML. Do not call tools, relaunch the Test Agent, rescore, or add an explanation.

For a scored result:

protocol_version: 1
status: ok
case_id: <case_id>
run: <run>
expected_version: <version>
provider: <actual_provider>
model_id: <actual_model_id>
thinking_level: <configured_thinking_level>
score: <0_to_100>
cost: <number_or_null>
duration_ms: <non_negative_integer>
session_id: <test_session_id>

For an evaluation failure, use null for an identity field that was missing or conflicting:

protocol_version: 1
status: failed
case_id: <case_id_or_null>
run: <run_or_null>
expected_version: <version_or_null>
provider: <provider_or_null>
model_id: <model_id_or_null>
thinking_level: <thinking_level_or_null>
failure_code: <stable_failure_code>

Use four failure codes:

• invalid_request: the request is incomplete or inconsistent.
• benchmark_invalid: the Statement, Rubric, or scoring contract is invalid.
• version_changed: the Test Agent version does not match the request or changed during evaluation.
• evaluation_failed: launch could not be safely repaired, or Trace binding or scoring failed.

Never include score, cost, duration, Session id, private data, or optimization advice on failure.