Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/sillyDaibo/reasflow-devnpx agentmods add skills/sillydaibo/reasflow-dev/auto-tuningWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/sillydaibo/reasflow-dev/auto-tuning)<a href="https://agentmods.dev/skills/sillydaibo/reasflow-dev/auto-tuning"><img src="https://agentmods.dev/badge/skills/sillydaibo/reasflow-dev/auto-tuning/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/sillydaibo/reasflow-dev/auto-tuning"><img src="https://agentmods.dev/badge/skills/sillydaibo/reasflow-dev/auto-tuning.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00022 | $0.00824 |
| Opus 5.5 | $0.00009 | $0.00330 |
| Sonnet 5.5 | $0.00004 | $0.00165 |
| Haiku 4.5 | $0.00002 | $0.00082 |
Grade A, and why
auto-tuning scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 95 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Installed Root
Resolve the installed reasflow-dev skills root before running packaged scripts:
REASFLOW_SKILLS_ROOT="${REASFLOW_SKILLS_ROOT:-}"
if [ -z "$REASFLOW_SKILLS_ROOT" ]; then
if [ -d ./.agents/skills ]; then
REASFLOW_SKILLS_ROOT="$(pwd)/.agents/skills"
elif [ -d "$HOME/.agents/skills" ]; then
REASFLOW_SKILLS_ROOT="$HOME/.agents/skills"
else
echo "reasflow shared skills not found in ./.agents/skills or $HOME/.agents/skills" >&2
exit 1
fi
fi
REASFLOW_PRIVATE_SKILLS_ROOT="${REASFLOW_PRIVATE_SKILLS_ROOT:-}"
if [ -z "$REASFLOW_PRIVATE_SKILLS_ROOT" ]; then
if [ -d ./.codex/reasflow-skills ]; then
REASFLOW_PRIVATE_SKILLS_ROOT="$(pwd)/.codex/reasflow-skills"
elif [ -d "$HOME/.codex/reasflow-skills" ]; then
REASFLOW_PRIVATE_SKILLS_ROOT="$HOME/.codex/reasflow-skills"
else
echo "reasflow private skills not found in ./.codex/reasflow-skills or $HOME/.codex/reasflow-skills" >&2
exit 1
fi
fi
Auto Tuning
Overview
This skill replaces the old prompt-only tuning advice with an actual Optuna-backed CLI. Use it when Experiment needs a bounded parameter search and the experiment code exposes a callable objective function.
Requirements
- A workspace-local Python environment, preferably
Alg_Exp/.venv/ optunainstalled there- An experiment module that defines a function like:
def objective_for_tuning(params: dict[str, float]) -> float:
result = run_experiment_with_params(params)
return result["validation_loss"]
If optuna is missing, create or fix the environment with uv:
uv venv Alg_Exp/.venv
Alg_Exp/.venv/bin/pip install optuna numpy scipy pandas
Helper Script
Set SKILL_ROOT="$REASFLOW_PRIVATE_SKILLS_ROOT/experiment/auto-tuning".
Run:
Alg_Exp/.venv/bin/python "$SKILL_ROOT/scripts/optuna-search.py" \
--experiment-file Alg_Exp/code/tuning_experiment.py \
--objective-function objective_for_tuning \
--param-space-file Alg_Exp/document/param_space.json \
--direction minimize \
--trials 50 \
--output Alg_Exp/data/tuning_history.json
What ships with it
1 file 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.
- 1mo ago First seen · 95 lines · 22 tokens per session scan A 52fedf8e8152
auto-tuning is a skill published in the GitHub repository sillyDaibo/reasflow-dev (2 stars, last pushed 1mo ago), licensed MIT. It adds 22 tokens to every session and 824 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-08-31.
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