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 skills add Insajin/autopus-adk --skill experimentgit clone --depth 1 https://github.com/Insajin/autopus-adkWrote 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/insajin/autopus-adk/experiment)<a href="https://agentmods.dev/skills/insajin/autopus-adk/experiment"><img src="https://agentmods.dev/badge/skills/insajin/autopus-adk/experiment/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/insajin/autopus-adk/experiment"><img src="https://agentmods.dev/badge/skills/insajin/autopus-adk/experiment.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00014 | $0.01385 |
| Opus 5 | $0.00007 | $0.00692 |
| Sonnet 5 | $0.00003 | $0.00277 |
| Haiku 4.5 | $0.00001 | $0.00138 |
Grade A, and why
experiment 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 9d 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 — 219 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Experiment Loop Skill (XLOOP)
Skill for running automated iterative improvement loops that optimize a measurable metric while keeping changes simple and reversible.
Overview
The experiment loop (auto experiment) runs an agent-driven cycle:
- Measure baseline metric
- Ask an executor agent to make one focused change
- Measure the new metric
- Decide: keep (commit) or discard (reset)
- Check circuit breaker and simplicity gate
- Repeat until MaxIterations or circuit break
Setup
# Initialize experiment branch (requires clean worktree)
auto experiment init --session-id my-session
# Verify metric command works
auto experiment metric \
--metric 'go test -bench=. ./... | grep ns/op | awk "{print \"{\\\"metric\\\":\"$3\"}\"}"' \
--metric-key metric
Full Loop Configuration
auto experiment init --session-id opt-$(date +%s)
# Then invoke the loop via the agent skill below
Agent Orchestration Pattern
The experiment loop is driven by invoking an executor agent repeatedly. Pass the full history context on each call so the agent can learn from past iterations.
## Experiment Loop — Iteration {N}
### Config
- MetricCmd: {cmd}
- Direction: {minimize|maximize}
- Target: {files}
- Scope: {files or "same as target"}
- SimplicityThreshold: {threshold}
### History (last 5 results)
{JSON array of recent Result objects from `auto experiment record`}
### Baseline: {baseline_value} {unit}
### Best so far: {best_value} at iteration {best_iter}
### Your task
Make ONE focused change to the target files that should improve the metric.
Do NOT modify files outside the allowed scope.
After making your change, run:
auto experiment commit --iteration {N} --description "your change description"
Then output the description in the last line.
Keep / Discard Decision
After each executor run, measure the metric and decide:
new_value = RunMetricMedian(cfg, cmd)
simplicity = CalculateSimplicity(baseline, new_value, linesAdded, linesRemoved, direction)
if direction.IsBetter(new_value, best_value):
if simplicity >= cfg.SimplicityThreshold:
status = "keep"
best_value = new_value
circuit_breaker.Record(true)
else:
status = "discard" # improvement too small relative to code complexity
ResetToCommit(last_keep_hash)
circuit_breaker.Record(false)
else:
status = "discard"
ResetToCommit(last_keep_hash)
circuit_breaker.Record(false)
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.
- 9d ago First seen · 219 lines · 14 tokens per session scan A 311ec2ece82f
experiment is a skill published in the GitHub repository Insajin/autopus-adk (111 stars, last pushed today), licensed MIT. It adds 14 tokens to every session and 1,385 once invoked, about $0.0001 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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