Create, run, and manage AgentV evaluations for AI agents and skills using the AgentV CLI and AgentEvals standard EVAL.yaml format. Use this skill whenever the user wants to write evaluation files for AI agents, run evals with agentv CLI, convert existing test cases to EVAL.yaml, set up eval targets, understand the AgentEvals specification, debug failing evaluations, integrate evals into CI/CD pipelines, or compare agent runs with `agentv compare`. Also use this when the user mentions EVAL.yaml, agentevals, agentv, or wants to evaluate skill quality with a declarative format.
Standardmäßig ist der Prompt ausgewählt, der zuerst die Quelle prüft. Sie können zu einem direkten Befehl wechseln oder eine lokale Kopie herunterladen.
Quelldateien prüfen
Lesen Sie SKILL.md und alle von SkillsMP angezeigten Begleitdateien, bevor Sie sich für eine Installation entscheiden.
Mit Codex oder Claude installieren Kopieren Sie diesen Prompt, fügen Sie ihn in Codex, Claude oder einen anderen Assistant ein und lassen Sie die Skill-Seite prüfen und installieren.
Ein direkter Befehl überspringt den Prüf-Prompt. Prüfen Sie die Quelle, bevor Sie ihn ausführen.
Create, run, and manage AgentV evaluations for AI agents and skills using the AgentV CLI and AgentEvals standard EVAL.yaml format. Use this skill whenever the user wants to write evaluation files for AI agents, run evals with agentv CLI, convert existing test cases to EVAL.yaml, set up eval targets, understand the AgentEvals specification, debug failing evaluations, integrate evals into CI/CD pipelines, or compare agent runs with `agentv compare`. Also use this when the user mentions EVAL.yaml, agentevals, agentv, or wants to evaluate skill quality with a declarative format.
license
MIT
compatibility
Requires Node.js 18+ with `agentv` globally installed (`npm install -g agentv`). Supports any agent target — Claude, Codex, Copilot, local CLI scripts, or OpenAI-compatible providers.
metadata
{"author":"Tyler Kendrick","version":"0.1.0"}
AgentV Skill
Use this skill to author, run, and manage evaluations for AI agents and skills using the AgentV CLI and the AgentEvals declarative YAML standard.
Read references/eval-yaml-schema.md for the complete EVAL.yaml schema reference before authoring new eval files.
Read references/targets.md for target configuration options.
When to use this skill
The user wants to write EVAL.yaml evaluation files for a skill or agent.
The user wants to run evals with agentv eval.
The user wants to convert existing test cases (e.g., evals.json) to EVAL.yaml format.
The user wants to understand the AgentEvals specification or schema.
The user wants to set up or update .agentv/targets.yaml.
The user wants to run agentv compare to detect regressions across runs.
The user wants to integrate AgentV into a CI/CD pipeline.
The user wants to write LLM judges, rubrics, or code graders for evaluation.
The user wants to run the same eval against two different targets or models to compare performance.
Do not use this skill for general prompt engineering unrelated to evaluations, or for creating and improving agent skill SKILL.md files and their structure.
Quickstart
# Install
npm install -g agentv
# Initialize project
agentv init
# Run an eval
agentv eval evals/my-skill.eval.yaml
# Run with a specific target
agentv eval evals/my-skill.eval.yaml --target claude
# Output formats
agentv eval evals/my.yaml -o report.html # HTML dashboard
agentv eval evals/my.yaml -o results.xml # JUnit XML for CI
agentv eval evals/my.yaml -o results.jsonl # JSONL (default)# Compare two runs
agentv compare .agentv/results/runs/<run-1>/index.jsonl .agentv/results/runs/<run-2>/index.jsonl
Core workflow
Follow this order when creating or updating evals:
Understand the skill/agent — what does it do, what inputs does it accept, what outputs should it produce?
Set up the target — configure .agentv/targets.yaml to point to the agent being evaluated.
Author the EVAL.yaml — write test cases with inputs, expected outputs, and assertions.
Add evaluators — choose deterministic assertions (contains, equals, regex, is-json) or LLM judges for subjective quality.
Run and inspect — agentv eval produces JSONL output; use --output report.html for a visual dashboard.
Iterate — improve the skill based on failing tests and re-run.
Converting evals.json to EVAL.yaml
When migrating an existing evals/evals.json file, map the fields as follows:
evals.json field
EVAL.yaml field
skill_name
name (top-level)
evals[].id
tests[].id
evals[].prompt
tests[].input
evals[].expected_output
tests[].expected_output (preserved) and informs tests[].criteria
evals[].assertions[]
tests[].rubrics[] (string) or tests[].assert[] (typed)
Note on expected_output mapping: In evals.json, expected_output is a single string used as the reference answer. In EVAL.yaml, tests[].expected_output preserves this reference answer exactly, while tests[].criteria is a separate natural-language description of what counts as success. They serve different roles — do not replace one with the other.
String-only assertions in evals.json become rubrics (string format) or llm-grader assertions in EVAL.yaml. Preserve both evals.json and EVAL.yaml — the JSON is used by the internal evaluation framework; the YAML is used by AgentV.
Writing judge prompts — use these template variables inside the markdown file:
Variable
Description
{{answer}}
The agent's actual response
{{expected_output}}
The expected output from the test case
{{input}}
The original prompt sent to the agent
{{criteria}}
The test case criteria field
Example judge prompt (graders/correctness.md):
You are evaluating whether an AI agent response meets the required criteria.
## Task criteria
{{criteria}}
## Agent response
{{answer}}
## Expected output
{{expected_output}}
Score 0.0–1.0, where 1.0 = fully meets all criteria. Reply with JSON only:
{"score": 0.9, "reasoning": "Brief explanation of the score"}
Threshold vs. --threshold: The threshold field on an llm-grader assertion is per-assertion (minimum score to pass). The --threshold CLI flag sets a minimum overall suite pass rate for CI gating. These are independent.
Rubrics
Use rubrics when you need weighted or structured grading criteria:
Use top-level assert: to apply evaluators to all test cases:
assert:-type:is-json# All responses must be valid JSON-type:llm-graderprompt:./graders/quality.mdtests:-id:test-1input:...assert:-type:contains# Per-test assertion (merged with suite-level)value:"status"
File references
Test inputs and judge prompts can reference files:
Configure .agentv/targets.yaml to point to your agent. Run agentv init to create a starter file.
# .agentv/targets.yamldefault:type:openaimodel:gpt-4oapi_key:${OPENAI_API_KEY}# Use env vars — never hardcode keysclaude:type:anthropicmodel:claude-opus-4-5api_key:${ANTHROPIC_API_KEY}
Read references/targets.md for all target types (OpenAI, Anthropic, local CLI, HTTP endpoints).
Multi-target comparison
Run the same eval suite against two targets to compare performance:
# Run against each target
agentv eval evals/my-skill.eval.yaml --target default
agentv eval evals/my-skill.eval.yaml --target claude
# Compare the two runs
agentv compare .agentv/results/runs/*/index.jsonl
CI/CD integration
Set the API key as a repository secret and inject it into the environment:
# Run with exit code on failures (for CI gates)
agentv eval evals/*.yaml --exit-on-failure
# JUnit output for GitHub Actions / Jenkins
agentv eval evals/*.yaml -o results.xml
# Threshold gate: fail if pass rate drops below 80%
agentv eval evals/*.yaml --threshold 0.8
agentv eval exits with a non-zero exit code when --exit-on-failure is set and any test fails, or when the pass rate falls below the --threshold value. This makes it suitable as a CI gate step.
Comparing runs
# After two runs, compare results
agentv compare .agentv/results/runs/run-1/index.jsonl .agentv/results/runs/run-2/index.jsonl
# Compare latest two runs automatically (ordered by run directory timestamp)
agentv compare --last 2
Improvements (↑) and regressions (↓) are highlighted. Use this to verify a skill change improves eval quality without introducing regressions.
Debugging failing evaluations
When a test case fails — especially with llm-grader giving unexpectedly low scores — follow these steps:
Inspect the JSONL results at .agentv/results/runs/<timestamp>/index.jsonl. Each test result includes:
pass — boolean pass/fail for the overall test
score — numeric score from the LLM grader (0.0–1.0)
reasoning — the judge's explanation for the score
assertions — per-assertion breakdown showing which checks passed or failed
Improve the judge prompt — if the reasoning reveals the judge is misinterpreting the criteria, edit the referenced markdown file (e.g., ./graders/correctness.md) to add clearer instructions or examples, then re-run.
Adjust the threshold — if scores are consistently in a borderline range (e.g., 0.65 when threshold is 0.7), decide whether to lower the threshold on the assertion or improve the prompt to score higher.
Distinguish per-assertion vs. suite threshold:
threshold on an llm-grader assertion: minimum score for that single assertion to pass (default: 0.5)
--threshold 0.8 CLI flag: minimum fraction of tests that must pass for the suite to exit 0
Handle non-deterministic scores — LLM graders can return different scores across runs due to temperature. If a test flips between pass and fail on re-runs, lower the judge model's temperature to 0.0 in .agentv/targets.yaml, widen the threshold margin (e.g., use 0.6 instead of 0.7), or adjust your judge prompt to produce more decisive scores.
# Re-run a single file to iterate quickly
agentv eval evals/my-skill.eval.yaml -o debug.jsonl
When evaluating skills in this repository, each skill keeps both formats:
skills/my-skill/
└── evals/
├── evals.json # Internal eval framework format
└── EVAL.yaml # AgentV-compatible format
Both files cover the same test cases. evals.json drives the internal skill-creator benchmarking workflow; EVAL.yaml enables agentv eval CLI execution and CI integration.
