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/mitkox/skillopt/pi-skilloptnpx skills add mitkox/SkillOpt --skill pi-skilloptgit clone --depth 1 https://github.com/mitkox/SkillOptWhat 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 | $0.00059 | $0.01060 |
| Opus 5 | $0.00030 | $0.00530 |
| Sonnet 5 | $0.00012 | $0.00212 |
| Haiku 4.5 | $0.00006 | $0.00106 |
Grade A, and why
pi-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 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 — 124 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PI SkillOpt Operator
Use this skill when the user wants the agent to operate the SkillOpt repository: run experiments, evaluate skills, configure local models, inspect outputs, or extend the repo with new benchmarks/backends.
Core repo facts
- SkillOpt optimizes a skill document / system prompt, not model weights.
- The repo supports a generic local OpenAI-compatible backend named
openai_compat. - The local example model config is
configs/dotnetdebug/local_mitko.yaml. - That config uses model
mitkoathttp://localhost:8000/v1for both optimizer and target. - The runnable sample benchmark is
dotnetdebug. - The sample dataset is
data/dotnetdebug/tasks.json. - The seed skill is
skillopt/envs/dotnetdebug/skills/initial.md. - Training entry point:
scripts/train.py. - Eval-only entry point:
scripts/eval_only.py.
Default workflow
- Identify the user goal:
- run a SkillOpt training job
- evaluate an existing skill
- inspect outputs from a previous run
- add or modify a benchmark
- add or modify a backend
- Confirm the benchmark, backend, target model, and desired output directory.
- Prefer a small smoke test first before a larger run.
- Use existing configs when possible instead of inventing new ones.
- After a run, report:
- exact command used
- output directory
- best skill path
- headline metrics
- next recommended action
Local model workflow
When the user asks to use a local model or mentions mitko, localhost:8000, or an OpenAI-compatible endpoint:
- Prefer
model.backend: openai_compat. - Set both
optimizer_backendandtarget_backendtoopenai_compatunless the user explicitly wants a mixed setup. - Use
configs/dotnetdebug/local_mitko.yamlwhen the task is the built-in dotnet debugging example. - Keep smoke tests small, for example:
train.num_epochs=1train.batch_size=2gradient.analyst_workers=1gradient.minibatch_size=2env.workers=1env.limit=2
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 · 124 lines · 59 tokens per session scan A 53f525e9f9fd
pi-skillopt is a skill published in the GitHub repository mitkox/SkillOpt (83 stars, last pushed 3mo ago), licensed MIT. It adds 59 tokens to every session and 1,060 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.
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