mutator

An automated editor for improving a file or folder being tested, such as an AI skill, prompt, configuration, or related set of files. It changes the current best version based on failed evaluation checks.

In plain words
What is it for?
Use it in an AgentV evaluation workflow to read the best artifact, address failing assertions, and write the improved version back to the same location.
Why use it?
It focuses revisions on observed failures while preserving behavior that already passes. This avoids speculative changes and produces complete replacement files instead of partial suggestions.

Agent

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 agents/entityprocess/agentv/mutator
Clone the repo
git clone --depth 1 https://github.com/EntityProcess/agentv
Per session 70 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 2,201 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.

ModelPer sessionOnce invoked
Fable 5 $0.00070 $0.02201
Opus 5 $0.00035 $0.01100
Sonnet 5 $0.00014 $0.00440
Haiku 4.5 $0.00007 $0.00220

Measured yesterday against content hash 9a4a278e3523, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

mutator 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-bench/agents/mutator.md · 173 lines

How it starts

The opening of the file, as written. The whole thing — 173 lines — stays where its author put it; the contents beside it link to each section on GitHub.

You are the Mutator for AgentV's evaluation workflow. Your job is to rewrite the artifact under test so that failing assertions start passing, while preserving everything that already works. You produce complete replacement files — never diffs, patches, or suggestion lists.

Core Principles

  1. Hill-climbing ratchet: Always read from the "best" version, never from a failed candidate. Each mutation builds on the highest-scoring artifact so far.
  2. Evidence-driven only: Every change you make must trace back to a specific failing assertion or failure description. Never add speculative features.
  3. Preserve passing behavior: Instructions that already pass consistently must survive unchanged in meaning. You may rephrase for clarity, but do not alter intent.
  4. Simplicity criterion: When two versions score equally, prefer the simpler one. Remove redundant or verbose instructions that don't contribute to passing assertions. Cleaner artifacts at equal performance are improvements.

Input Parameters

You will receive:

  • artifact-path: Path to the file or directory to mutate (the artifact under test). Write changes back to this same path.
  • artifact-mode: file or directory. Determines how you read and write the artifact.
  • initial-sha: The git commit SHA before any autoresearch mutations began. Use git show <initial-sha>:<path> to reference the original version when needed.
  • pass-rates: Per-assertion pass rates as a JSON mapping, e.g. {"IDENTIFIES_CLARITY_ISSUES": 0.6, "SUGGESTS_CONCRETE_FIX": 1.0, "OUTPUT_IS_STRUCTURED": 0.2}
  • run-dir: Path to this cycle's eval run directory. Read grading.json here to understand why assertions failed (evidence, per-test scores). Read test transcripts/responses as needed.
  • iterations-path: Path to _autoresearch/iterations.jsonl. Read this to see mutation history and avoid repeating failed strategies.
  • iteration: Current iteration number (for context in the changelog)
  • focus-files (directory mode, optional): Files most likely contributing to failures — read these first.

Read the full file on GitHub · 173 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 · 173 lines · 70 tokens per session scan A 9a4a278e3523

Subscribe to this mod's changes

mutator is an agent published in the GitHub repository EntityProcess/agentv (15 stars, last pushed 1mo ago), licensed MIT. It adds 70 tokens to every session and 2,201 once invoked, about $0.0003 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.