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ai-research-reproduction

ai-research-reproduction is a skill for Codex from lllllllama/RigorPilot-Skills. It costs 136 tokens per session (1,436 once invoked), scanned A, original, MIT.

A guide for reproducing results from a deep-learning research repository. It starts with the repository's README and follows the smallest documented inference or evaluation run that can provide trustworthy evidence.

In plain words
What is it for?
Recreating a paper or project run, setting up its environment, running documented inference or evaluation, verifying training-related steps when needed, and reporting what matched or differed.
Why use it?
It prevents reproduction from becoming an undocumented process of changing things until code runs. The workflow checks the environment, weights, datasets, commands, deviations, and results.

Skill for Codex

Written for Codex: agents/openai.yaml present.

not rated 484repo +2 changed 1mo ago A scan Socket: warnSnyk: passSkillSpector: warn 136 tokens original MIT

Good fit Recreating a paper or project run, setting up its environment, running documented inference or evaluation, verifying training-related steps when needed, and reporting what matched or differed.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/lllllllama/rigorpilot-skills/ai-research-reproduction
View source ↗ lllllllama/RigorPilot-Skills
Install

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.

Any agent
npx skills add lllllllama/RigorPilot-Skills --skill ai-research-reproduction
Clone the repo
git clone --depth 1 https://github.com/lllllllama/RigorPilot-Skills

Made for: Codex.

Wrote 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.

agentmods badge for ai-research-reproduction

README.md
[![agentmods](https://agentmods.dev/badge/skills/lllllllama/rigorpilot-skills/ai-research-reproduction/github.svg)](https://agentmods.dev/skills/lllllllama/rigorpilot-skills/ai-research-reproduction)
Your own site
<a href="https://agentmods.dev/skills/lllllllama/rigorpilot-skills/ai-research-reproduction"><img src="https://agentmods.dev/badge/skills/lllllllama/rigorpilot-skills/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.

agentmods 80×15 button for ai-research-reproduction

Your own site · 80×15
<a href="https://agentmods.dev/skills/lllllllama/rigorpilot-skills/ai-research-reproduction"><img src="https://agentmods.dev/badge/skills/lllllllama/rigorpilot-skills/ai-research-reproduction.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 136 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,436 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • Socket warn 24 Sept 2026
  • Snyk pass 24 Sept 2026
  • NVIDIA SkillSpector warn 7 Sept 2026
SkillSpector: 1 finding, up to medium

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

  • medium Rogue Agent · line 71
    Skill establishes unauthorized persistence across sessions via cron jobs, startup scripts, or state files. Session persistence allows an attacker to maintain access beyond the current interaction.
    Fix: Remove any persistence mechanisms (cron jobs, startup scripts, state files). Skills should not maintain state across sessions without explicit user consent.
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00136 $0.01436
Opus 5.5 $0.00054 $0.00574
Sonnet 5.5 $0.00027 $0.00287
Haiku 4.5 $0.00014 $0.00144

Measured 1mo ago against content hash 429b7d6c29a6, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

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 1mo ago.

The scan reads SKILL.md. This mod also ships 22 executable files (_bundled/shared/scripts/agent_provider.py, _bundled/shared/scripts/command_utils.py, _bundled/shared/scripts/lessons_store.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

Origin

Copies of this mod

1 near-identical copy found in the catalogue:

skills/ai-research-reproduction/SKILL.md · 108 lines

How it starts

The opening of the file, as written. The whole thing — 108 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 the smallest trustworthy run with auditable evidence. Preserve documented meaning; record assumptions, deviations and blockers instead of changing semantics to manufacture success. Load specialized references only for a concrete uncertainty.

Fast Path

For a routine bounded run, keep the control path short:

  1. Read the target README and only the target test/config/source needed to understand the documented command.
  2. Run scripts/orchestrate_repro.py --repo <repo> --plan-only --agent-output with any explicit user timeout bound (--timeout or --train-timeout) already supplied; review command_candidates, the selected cmd-XX, side-effect contract, selection fingerprint, and returned reviewed_run_args. With no --output-dir, later evidence goes to <repo>/repro_outputs regardless of caller cwd.
  3. Run the selected candidate, or another reviewed candidate, with --run-selected --command-id <cmd-XX> --plan-fingerprint <fingerprint> --agent-output plus requested timeout/metric/source-adjacent options. Preserve an explicit user command-timeout bound instead of silently making it stricter on a routine trusted run. --timeout limits the target command; do not wrap the whole orchestrator in an equal or shorter external timeout, because it still needs time to terminate children and write terminal evidence. A changed command set fails closed; setup/download commands are never target candidates.
  4. Run --verify-output --agent-output; inspect detailed evidence files only when verification fails or the result is partial/blocked.
  5. Deliver the bounded result and stop.

For a host with short tool-call deadlines, rerun planning with --include-agent-handoff and follow references/agent-job.md; otherwise keep the direct path above. Job completion is not task acceptance, and uncertain state is never a reason for automatic replay.

Do not inspect orchestrate_repro.py, annotate_readme.py, _bundled/, writers, or runtime internals on a normal success path. Inspect implementation only for a concrete blocker, unexpected side effect, bundle-integrity failure, or unresolved safety question. Use scripts/doctor.py for first-use environment/install diagnostics. Executed commands keep full lifecycle/log evidence under repro_outputs/_runtime/<run_id>/.

Read the full file on GitHub · 108 lines

Files

What ships with it

47 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.

Changes

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.

  1. 1mo ago Changed · -23 lines 429b7d6c29a6
  2. 1mo ago Changed ea3c9789a0bd
  3. 1mo ago Changed c45e2f58572e
  4. 1mo ago Changed 7cc128f04cc0
  5. 1mo ago Changed e7e737bb9713
  6. 1mo ago First seen · 131 lines · 136 tokens per session scan A 2dd32f685ef0

Subscribe to this mod's changes

ai-research-reproduction is a skill published in the GitHub repository lllllllama/RigorPilot-Skills (484 stars, last pushed 1mo ago), licensed MIT. It adds 136 tokens to every session and 1,436 once invoked, about $0.0005 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 2026-08-30.

Data and AIScientificagent-skillsai-agentsdeep-learningreproducibilityresearch-tools
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