What is agentv-eval-migrations?

A guide for updating AgentV evaluation files when their YAML format changes. YAML is a human-readable way to write configuration files.

Use it to migrate evaluation prompts, tests, targets, environment variables, and grading rules across AgentV schema changes.

It helps bring older evaluations into the current format and avoids errors caused by removed or relocated fields.

agentv-eval-migrations — Skill by EntityProcess

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/entityprocess/agentv/agentv-eval-migrations

Any agent

npx skills add EntityProcess/agentv --skill agentv-eval-migrations

Clone the repo

git clone --depth 1 https://github.com/EntityProcess/agentv

Made for: Claude Code, Codex.

Per session 31 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 557 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. 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.

Model	Per session	Once invoked
Fable 5	$0.00031	$0.00557
Opus 5	$0.00015	$0.00279
Sonnet 5	$0.00006	$0.00111
Haiku 4.5	$0.00003	$0.00056

Measured yesterday against content hash f998e656b043, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

agentv-eval-migrations scanned grade A with 0 findings 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.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills-data/agentv-eval-migrations/SKILL.md · 47 lines

What it actually says

AgentV Eval Migrations

Use this skill when updating existing AgentV eval YAML, examples, docs, or generated eval authoring guidance after a schema-breaking change.

Before editing, read references/breaking-changes.md. For stale evals from AgentV v4.42.4, use that reference as the migration map: it lists the v4.42.4-era shape, current shape, migration steps, verification commands, and compatibility notes for each major breaking authoring change. Then compare the eval file against the current portable contract:

Treat migrations as rule-based rewrites first. Do not rely on a manual "looks current" pass when the old field has a direct current replacement.
Rewrite authored prompt data into top-level prompts plus tests[].vars; keep input only for documented raw-case import compatibility.
Move sibling expected_output to vars.expected_output, then add or keep an explicit assertion that consumes it, usually llm-rubric with {{ expected_output }}.
Rewrite target identity to id, keep provider for the backend/adapter kind, put provider settings under config, and rewrite env references to {{ env.NAME }}.
Remove use_target, eval_cases, evalcases, providerPromptMap, and provider_prompt_map from authored eval/config YAML. Current AgentV rejects these fields, so do not preserve them as compatibility aliases.
Keep committed eval YAML portable: prompts, cases, assertions, workspace templates, repos, hooks, env checks, Docker preflight/container config, and workspace.scope.
Do not put machine-local existing workspace directories in eval YAML. Use --workspace-path for one-off runs or .agentv/config.local.yaml with execution.workspace_path for persistent local binding.
Use workspace.scope: suite | attempt for portable workspace lifetime. Docker config is not a replacement for workspace folder lifetime.
Keep wire-format fields in snake_case and TypeScript internals in camelCase.

After migration, validate with bun apps/cli/src/cli.ts validate <file> when working in the AgentV repo, or agentv validate <file> from an installed CLI. Run the repo's parser/schema tests for generated examples and fixtures when the change affects shared skill data or examples.

Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

references/breaking-changes.md 32 KB

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.

yesterday First seen · 47 lines · 31 tokens per session scan A f998e656b043

Subscribe to this mod's changes

agentv-eval-migrations is a skill published in the GitHub repository EntityProcess/agentv (15 stars, last pushed 1mo ago), licensed MIT. It adds 31 tokens to every session and 557 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Testing agent-skills agentic-ai evaluation-framework llms vscode

neuron-test-engineer

Write tests for Neuron AI agents, RAG systems, workflows, and tools using the built-in testing utilities. Use this skill when the user mentions testing agents, writing unit tests, mocking AI providers, testing tool execution, verifying RAG retrieval, testing workflow behavior, or creating test cases for Neuron AI…

neuron-core/neuron-ai · 94 tokens

neuron-tool-creator

Create custom tools, toolkits, and MCP integrations for Neuron AI agents. Use this skill when the user mentions creating tools, building toolkits, extending Tool class, defining tool properties, implementing tool execution, MCP server integration, Model Context Protocol, connecting external tools, or tool guidelines.…

neuron-core/neuron-ai · 100 tokens

neuron-evaluation-engineer

Create and run AI evaluations with datasets, assertions, and output drivers in Neuron AI. Use this skill whenever the user mentions evaluation, testing AI systems, creating evaluators, dataset-driven testing, assertion-based validation, or wants to measure AI system performance. Also trigger for tasks involving…

neuron-core/neuron-ai · 77 tokens

neuron-structured-output

Design and implement structured output classes for Neuron AI agents using SchemaProperty attributes and validation rules. Use this skill when the user mentions structured output, JSON schema extraction, data validation, output classes, DTOs for AI responses, extracting structured data from LLM, or configuring property…

neuron-core/neuron-ai · 104 tokens

neuron-workflow-architect

Build custom Neuron AI workflows with nodes, events, middleware, and human-in-the-loop patterns. Use this skill whenever the user mentions workflows, orchestration, event-driven systems, custom agents, complex multi-step processes, human-in-the-loop patterns, or wants to build a custom agentic system from scratch.…

neuron-core/neuron-ai · 89 tokens

neuron-agent-builder

Create and configure Neuron AI agents with providers, tools, instructions, and memory. Use this skill whenever the user mentions building agents, creating AI assistants, setting up LLM-powered chat bots, configuring chat agents, or wants to create an agent that can talk, use tools, or handle conversations. Also…