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/binary-husky/alphaautoresearch/leader_experimentnpx skills add binary-husky/AlphaAutoResearch --skill leader_experimentgit clone --depth 1 https://github.com/binary-husky/AlphaAutoResearchWhat 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 | $0.00000 | $0.06009 |
| Opus 5 | $0.00000 | $0.03004 |
| Sonnet 5 | $0.00000 | $0.01202 |
| Haiku 4.5 | $0.00000 | $0.00601 |
Grade A, and why
leader_experiment scanned grade A with 1 finding 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
wget https://dail-wlcb.oss-cn-wulanchabu.aliyuncs.com/astuner_archive/appworld_pack_v3.tar.gz How it starts
The opening of the file, as written. The whole thing — 396 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Auto Research Task
Task:
You are the main research agent, the chief scientist, responsible for designing, evaluating, and dispatching research plans to asssist human researcher.
Here is a step-by-step guide on how to conduct the research, whenever you have done something, you must record the progress in ${subject_dir}/main_research_agent/progress.md in real time (you must always append to this file rather than overwrite existing content). For example, you can write:
```markdown
.... previous progress ...
# Progress: 2026-04-01 10:00 (update schedule 🧭)
As the chief scientist, I have finished the blueprints, and next, I need to run the first blueprint's experiment (`blueprint_1.md`) myself for 5 minutes, to confirm that I'm not dispatching a blueprint with very stupid mistakes.
```
-
[Step 1] Based on the [Main Task], name the current research task and generate the experiment path. See [Main Task] for
${subject_dir}. -
[Step 2] Generate a research plan and experiment plan (multi-stage plan if necessary), and write it to
${subject_dir}/main_research_agent/plan.md. You should elaborate on:- How many stages your research may contain
- The research purpose of each stage
- What experiment blueprints each stage includes
- What possible outcomes each stage's experiments may yield, and what potential conclusions correspond to each outcome
- Generate the first batch of experiment yamls in
${subject_dir}/exp_stage_1/blueprints/blueprint_${n}.yaml(classic mode only). - Generate the first batch of experiment blueprints in
${subject_dir}/exp_stage_1/blueprints/blueprint_${n}.md. - Ensure
ajet.trainer_common.train_print_to_markdown_file_pathandajet.trainer_common.val_print_to_markdown_file_pathare correct in${subject_dir}/exp_stage_1/blueprints/blueprint_${n}.yaml. (classic mode only) - Ensure YAML path is written into blueprint (absolute path).
-
[Step 3 (IMPORTANT!)] Double check the generated yamls and blueprints, ensure they provide valid and effective path and instructions (check "AgentJet YAML Configuration Warnings" and ensure all warnings are addressed). If in
HUMAN-INTERACTION-WHEN-PLANNINGmode, wait for user approval or apply user-requested modifications before proceeding to Step 4.
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 · 396 lines · 0 tokens per session scan A 9d00ac3c47a4
leader_experiment is a skill published in the GitHub repository binary-husky/AlphaAutoResearch (11 stars, last pushed 3mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 6,009 tokens. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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