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 sutchan/Agent-Skills-Hub --skill ai-research-reproductiongit clone --depth 1 https://github.com/sutchan/Agent-Skills-HubWrote 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/sutchan/agent-skills-hub/ai-research-reproduction)<a href="https://agentmods.dev/skills/sutchan/agent-skills-hub/ai-research-reproduction"><img src="https://agentmods.dev/badge/skills/sutchan/agent-skills-hub/ai-research-reproduction/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/sutchan/agent-skills-hub/ai-research-reproduction"><img src="https://agentmods.dev/badge/skills/sutchan/agent-skills-hub/ai-research-reproduction.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 | $0.00136 | $0.01497 |
| Opus 5 | $0.00068 | $0.00749 |
| Sonnet 5 | $0.00027 | $0.00299 |
| Haiku 4.5 | $0.00014 | $0.00150 |
Grade A, and why
ai-research-reproduction 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.
This is a copy
97% identical to ai-research-reproduction — 2 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 131 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ai-research-reproduction
Purpose
Guide README-first deep learning reproduction toward a minimal trustworthy run
with auditable evidence. Reproduction is not "make it run by changing
anything"; faithfully read the README, environment, weights, datasets, and
documented commands, then record results and deviations. Start with
references/agent-operating-principles.md; load
references/research-rigor-principles.md and
references/deep-learning-experiment-principles.md when scientific meaning or
experiment details are at stake.
The deterministic entrypoint is scripts/orchestrate_repro.py. It includes a
self-contained _bundled/ runtime, so this skill works when installed alone;
separately installed companion skills remain optional reusable entrypoints.
Executed commands persist lifecycle state, append-only events, and full streamed
stdout/stderr under repro_outputs/_runtime/<run_id>/. A CANCEL file in the
active run directory requests process-tree cancellation.
For recovery, queues or model gates, read references/runtime-and-model-adapter.md; for the optional model/tool loop, read references/agent-runner.md and use scripts/run_agent.py.
Fit
Use this skill when all are true:
- The target is an AI code repository with a README, scripts, configs, or documented commands.
- The request spans multiple trusted phases such as intake, setup, execution, training verification, analysis, paper-gap resolution, and reporting.
- The desired result is a small reproducible target, not broad experimentation.
Do not use this skill for paper summaries, generic environment setup, isolated repo scanning, standalone command execution, open-ended research design, or explicit candidate-only exploration.
Trusted Target Selection
Choose the smallest target that can honestly demonstrate repository-grounded reproduction:
- documented inference
- documented evaluation
- documented training startup or partial verification
- full training only after explicit user confirmation
What ships with it
32 files 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.
- _bundled/MANIFEST.json 4.4 KB
- _bundled/shared/scripts/agent_provider.py 2.9 KB runs code
- _bundled/shared/scripts/command_utils.py 3.0 KB runs code
- _bundled/shared/scripts/lessons_store.py 8.4 KB runs code
- _bundled/shared/scripts/model_adapter.py 6.2 KB runs code
- _bundled/shared/scripts/resource_monitor.py 8.0 KB runs code
- _bundled/shared/scripts/runtime_runner.py 25 KB runs code
- _bundled/shared/scripts/task_queue.py 30 KB runs code
- _bundled/shared/scripts/write_run_bundle.py 27 KB runs code
- agents/openai.yaml 389 B
- assets/COMMANDS.template.md 348 B
- assets/LOG.template.md 669 B
- assets/model_profile.template.json 183 B
- assets/PATCHES.template.md 525 B
- assets/readme_annotations.template.json 1.1 KB
- assets/status.template.json 1.1 KB
- assets/SUMMARY.template.md 621 B
- references/agent-operating-principles.md 2.7 KB
- references/agent-runner.md 4.4 KB
- references/architecture.md 1.3 KB
- references/continuous-learning-policy.md 3.6 KB
- references/deep-learning-experiment-principles.md 2.0 KB
- references/language-policy.md 794 B
- references/output-spec.md 5.1 KB
- references/patch-policy.md 1.6 KB
- references/readme-annotation-policy.md 4.3 KB
- references/research-rigor-principles.md 3.7 KB
- references/research-safety-principles.md 1.9 KB
- references/runtime-and-model-adapter.md 4.5 KB
- scripts/annotate_readme.py 28 KB runs code
- scripts/orchestrate_repro.py 58 KB runs code
- scripts/run_agent.py 23 KB runs code
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 Changed · -4 lines · +78 tokens per session 7cc128f04cc0
- 12d ago First seen · 135 lines · 58 tokens per session scan A 52d9ec63c1b1
ai-research-reproduction is a skill published in the GitHub repository sutchan/Agent-Skills-Hub (2 stars, last pushed yesterday), licensed MIT. It adds 136 tokens to every session and 1,497 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 0 findings. It is 97% identical to ai-research-reproduction, differing in 2 lines, and is treated as a copy.
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