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 skills/skillberry-ai/cap-evolve/skilloptnpx skills add skillberry-ai/cap-evolve --skill skilloptgit clone --depth 1 https://github.com/skillberry-ai/cap-evolveWhat 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 | $0.00118 | $0.02480 |
| Opus 5 | $0.00059 | $0.01240 |
| Sonnet 5 | $0.00024 | $0.00496 |
| Haiku 4.5 | $0.00012 | $0.00248 |
Grade A, and why
skillopt 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 3d 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 — 152 lines — stays where its author put it; the contents beside it link to each section on GitHub.
skillopt — annealed single-lineage climb (epochs × mini-batches)
SkillOpt (arXiv:2605.23904, Executive Strategy for Self-Evolving Agent Skills) organizes a hill-climb into epochs × mini-batches under a decaying integer edit budget. The name is the paper's; the algorithm edits whatever the selected capability owns — a prompt, a tool surface, a skill package — and never assumes which.
Read the shared step first, then this file. Parent materialization, the
optimizer call, the val evaluation, the significance gate, accept/reject,
snapshot/best, the memory and handover files: all of that is
harness.run_step, documented once in algorithms/hill-climb/SKILL.md
§ "One iteration, end to end" and algorithms/hill-climb/references/run-step.md.
This file states only what SkillOpt does differently.
Know the bound before reaching for this algorithm: run_step lets a caller
vary exactly two things — parent_dir and instructions. SkillOpt pins
parent_dir to the current best, identical to hill-climb, so everything novel
lives in the instructions string plus the choice to run one extra step per
epoch. It is prompt shaping and step scheduling, not a different search.
What SkillOpt does differently
- A decaying integer edit budget
L.lr_schedule.build_scheduleemits one integer per step overconstant | linear | cosine, clamped to[--min-edit-budget, --edit-budget](core/cap_evolve/lr_schedule.py:42-55).Lis stated to the optimizer in prose — "at most L bounded edits" — and is never mechanically enforced. See the next section before you tune it. - A per-epoch rejected-edit list. Each reject appends its candidate id and
val Δ, and the next step's prompt asks the optimizer to avoid them
(
skillopt.py:120-125,:330-335). It carries no description of what the rejected edit changed, so treat it as a weak signal — the run-globalLEDGER.mdthatrun_stepalready injects names the tasks each prior edit broke and fixed, which is strictly more useful. - One extra gated step per epoch boundary (from epoch 2). It compares the
epoch-start candidate against the current best, buckets tasks as
regressed / persistent-failure / stable-success, and asks for a consolidating
edit that fixes regressions without breaking the stable passes. It goes
through the same
run_stepand the same val gate — it is never force-accepted (skillopt.py:493-499). Disable with--no-slow-update. A fourth bucket,improved, is computed and logged but is not exclusive with the others and never reaches the prompt (skillopt.py:184-191,:139-166).
What ships with it
6 files 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.
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.
- 3d ago First seen · 152 lines · 118 tokens per session scan A cb8a56890c34
skillopt is a skill published in the GitHub repository skillberry-ai/cap-evolve (47 stars, last pushed 3d ago), licensed Apache-2.0. It adds 118 tokens to every session and 2,480 once invoked, about $0.0006 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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