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/binghanofuestc/open_agent_team/experiment-iteration-loopnpx skills add BingHanOfUESTC/open_agent_team --skill experiment-iteration-loopgit clone --depth 1 https://github.com/BingHanOfUESTC/open_agent_teamWrote 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/binghanofuestc/open_agent_team/experiment-iteration-loop)<a href="https://agentmods.dev/skills/binghanofuestc/open_agent_team/experiment-iteration-loop"><img src="https://agentmods.dev/badge/skills/binghanofuestc/open_agent_team/experiment-iteration-loop.svg" alt="Measured on agentmods" 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 | $0.00027 | $0.00416 |
| Opus 5 | $0.00014 | $0.00208 |
| Sonnet 5 | $0.00005 | $0.00083 |
| Haiku 4.5 | $0.00003 | $0.00042 |
Grade A, and why
experiment-iteration-loop 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 5d 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.
What it actually says
Experiment Iteration Loop
This skill ensures experiments are decisive, logged, and honest about failures.
1. Experiment Queue
Maintain:
research_workspace/experiments/experiment_queue.md
Use this structure:
## EXP-<N>: <name>
- Purpose:
- Hypothesis:
- Baseline or variant:
- Config:
- Command:
- Expected duration:
- Hardware:
- Success criterion:
- Failure criterion:
- Status:
2. Required Experiment Types
Default order:
SMOKE: environment and data sanity
BASE: baseline reproduction or minimum baseline
MAIN: proposed method
ABL: ablation that isolates the change
ROB: robustness/sensitivity if resources allow
FAIL: diagnosis for unexpected failure
Do not run expensive variants before baseline and smoke tests.
3. Logging
For every run, save:
config
command
stdout/stderr
metric output
random seed
hardware
start/end time
git diff or file state
notes
Write summary to:
research_workspace/09_experiment_log.md
Store raw logs under:
research_workspace/experiments/logs/
4. Result Table
Maintain:
research_workspace/experiments/results.csv
Minimum columns:
experiment_id
method
dataset
split
seed
metric
value
runtime
hardware
log_path
notes
Figures and LaTeX tables must be generated from this file or a documented equivalent.
5. Iteration Decision
After each batch of experiments, decide:
continue
debug
ablate
pivot
downgrade
stop_success
stop_negative_result
blocked
Record the decision and evidence in decision_register.md.
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.
- 5d ago First seen · 131 lines · 27 tokens per session scan A 49207957c617
experiment-iteration-loop is a skill published in the GitHub repository BingHanOfUESTC/open_agent_team (109 stars, last pushed 2mo ago), licensed MIT. It adds 27 tokens to every session and 416 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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