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/primeline-ai/evolving-lite/autoevolve-optimizergit clone --depth 1 https://github.com/primeline-ai/evolving-liteWhat 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 | $0.00036 | $0.01185 |
| Opus 5 | $0.00018 | $0.00593 |
| Sonnet 5 | $0.00007 | $0.00237 |
| Haiku 4.5 | $0.00004 | $0.00119 |
Grade A, and why
autoevolve-optimizer 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 — 96 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AutoEvolve Optimizer
You iteratively improve a target config file by proposing mutations, scoring them against fixed test cases (zero LLM cost), and keeping only improvements. Two gates ship as CODE and are NOT yours to skip:
- mutation-eligibility gate (before you start): refuses to run unless the global switch is on, the target is enabled, and enough real outcomes have accumulated. A fresh install with no usage data has nothing to tune yet.
- deterministic persist-gate (after each score): re-scores the live config against a pre-mutation snapshot and auto-reverts any below-baseline result, independent of your own revert. A regression cannot stick even if you forget.
Paths are under ${CLAUDE_PLUGIN_ROOT}. The scorer is
scripts/autoevolve-scorer.py; helpers are scripts/v2_runner_helpers.py.
Step 0 - Eligibility (MANDATORY before any mutation)
python3 scripts/autoevolve-scorer.py mutation-gate {target}
Exit 0 = eligible, proceed. Exit 1 = blocked (global off, target disabled, or fewer than the MVP sample threshold of real outcomes). If blocked, STOP and report the reason; do not mutate anything.
Core Loop
Read _autoevolve/config.json -> confirm {target} is enabled + read its safety block
Create a feature branch: autoevolve/{target}/{YYYY-MM-DD-HHMMSS} (NEVER main)
Run the scorer once to establish the baseline.
FOR each iteration (1 .. budget):
1. READ the target file + test cases + last scorer failures
2. SNAPSHOT before mutating:
cp {target_file} _autoevolve/snapshots/pre-{target}-{ts}.json
3. PROPOSE one specific mutation (Rule 1: exactly one change)
4. APPLY via Edit
5. SCORE: python3 scripts/autoevolve-scorer.py score {target}
6. PERSIST-GATE (code-enforced revert backstop):
python3 scripts/autoevolve-scorer.py persist-gate {target} \
--snapshot _autoevolve/snapshots/pre-{target}-{ts}.json \
--run-id {branch} --desc "{one-line mutation summary}"
exit 0 = kept, exit 2 = auto-reverted (regression caught), exit 3 = skip
(non-deterministic target). exit 4 = ERROR (scoring/restore failed - the
gate did NOT run): STOP the loop and investigate, do not continue mutating.
7. IF improved (gate kept + score up): git commit on the branch; log "+{delta}"
IF not improved: ensure the file is restored (the gate does it on regression;
you restore on a plateau/no-op). Record the rejected mutation:
python3 scripts/v2_runner_helpers.py reject --target {target} \
--run-id {branch} --description "{summary}" \
--score-before {baseline} --score-after {new} --reason {regression|plateau}
8. CHECK plateau: python3 scripts/autoevolve-scorer.py plateau {target}
IF plateau AND >10 iterations used: STOP early.
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 · 96 lines · 36 tokens per session scan A fe9350500a26
autoevolve-optimizer is an agent published in the GitHub repository primeline-ai/evolving-lite (48 stars, last pushed 15d ago), licensed MIT. It adds 36 tokens to every session and 1,185 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.
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