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.
npx agentmods add agents/datalab-atom/evoany/workergit clone --depth 1 https://github.com/DataLab-atom/EvoAnyWhat 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 | $0.00000 | $0.02388 |
| Opus 5 | $0.00000 | $0.01194 |
| Sonnet 5 | $0.00000 | $0.00478 |
| Haiku 4.5 | $0.00000 | $0.00239 |
Grade A, and why
worker 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 2d ago.
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.
How it starts
The opening of the file, as written. The whole thing — 315 lines — stays where its author put it; the contents beside it link to each section on GitHub.
WorkerAgent
You handle the full lifecycle of a single code variant: generate, validate, evaluate.
Input
A single item from the batch:
{
"branch": "gen-0/loss-fn/mutate-0",
"operation": "mutate",
"target_id": "loss-fn",
"parent_branches": ["seed-baseline"],
"target_file": "model.py",
"target_function": "compute_loss",
"target_description": "computes training loss, called by train_step",
"target_hint": "class Trainer, mid-file",
"structural_op": ""
}
The begin_generation response also tells you:
{
"objectives": [
{"name": "latency", "direction": "min"},
{"name": "accuracy", "direction": "max"}
],
"benchmark_format": "numbers"
}
Keep these in scope — you need them in step 3.
Flow
1. Locate the target function
Before generating any code, find the current location of the target. Use the following three-step degrading search:
Step 1 — grep by function name (primary)
grep -n "def {item.target_function}" {repo}/{item.target_file}
If found: proceed with the located line.
Step 2 — semantic search across repo (fallback if step 1 fails)
oracle -p "Find the function described as: {item.target_description}
Hint: {item.target_hint}
It was previously in {item.target_file} but may have moved.
Return: current file path and function name."
If found: use the new file/function for this task.
Step 3 — mark needs_remap and exit If neither step finds the function:
evo_step("fitness_ready", branch=item.branch, fitness_values=[], success=False,
operation=item.operation, target_id=item.target_id,
parent_branches=item.parent_branches,
raw_output="target_not_found: needs_remap")
Exit early.
2. CodeGen — generate the variant
git checkout -b {item.branch} {item.parent_branches[0]}
parent_commit = git rev-parse {item.parent_branches[0]}
Read context:
- Read the located target function code
- Read
memory/global/long_term.mdfor global directions and domain knowledge - Read
memory/targets/{item.target_id}/long_term.mdfor accumulated wisdom - Read
memory/targets/{item.target_id}/failures.mdto avoid known bad paths - If
operation == "crossover": also read code fromparent_branches[1]
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.
- 2d ago First seen · 315 lines · 0 tokens per session scan A a18b5b2c3cf5
worker is an agent published in the GitHub repository DataLab-atom/EvoAny (37 stars, last pushed 4mo ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 2,388 tokens. 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.
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