agentmods.dev

analyze-results

analyze-results is a skill for Claude Code from wanshuiyin/Auto-claude-code-research-in-sleep. It costs 37 tokens per session (376 once invoked), scanned A, original, MIT.

A tool for interpreting machine-learning experiment results by comparing data, calculating statistics, and summarizing findings.

In plain words
What is it for?
Use it to compare models or settings, calculate averages and standard deviations across random seeds, find parameter trends, and suggest follow-up experiments.
Why use it?
It turns scattered JSON or CSV result files into comparisons that show differences from a baseline, variation across runs, and suspicious values.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter.

Part of the aris plugin — 87 skills, 1 command, 1 agent shipped together

not rated 19krepo +287 2d ago A scan Socket: passSnyk: passSkillSpector: pass 37 tokens original MIT

Good fit Use it to compare models or settings, calculate averages and standard deviations across random seeds, find parameter trends, and suggest follow-up experiments.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/wanshuiyin/auto-claude-code-research-in-sleep/analyze-results
View source ↗ wanshuiyin/Auto-claude-code-research-in-sleep
About the project

ARIS is a collection of Markdown-based skills that define a workflow for autonomous machine-learning research, including idea discovery, experiment automation, and review loops. Researchers and AI coding agents use it across tools such as Claude Code, Codex, Cursor, and OpenClaw without depending on a single framework. The catalogue entries are ARIS workflow skills and agents.

wanshuiyin/Auto-claude-code-research-in-sleep · 19,107 stars · on GitHub

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 wanshuiyin/Auto-claude-code-research-in-sleep --skill analyze-results
Clone the repo
git clone --depth 1 https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep

Made for: Claude Code.

Or install aris, the plugin that ships this one along with the rest of its 87 skills, 1 command, 1 agent.

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 analyze-results

README.md
[![agentmods](https://agentmods.dev/badge/skills/wanshuiyin/auto-claude-code-research-in-sleep/analyze-results/github.svg)](https://agentmods.dev/skills/wanshuiyin/auto-claude-code-research-in-sleep/analyze-results)
Your own site
<a href="https://agentmods.dev/skills/wanshuiyin/auto-claude-code-research-in-sleep/analyze-results"><img src="https://agentmods.dev/badge/skills/wanshuiyin/auto-claude-code-research-in-sleep/analyze-results/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 analyze-results

Your own site · 80×15
<a href="https://agentmods.dev/skills/wanshuiyin/auto-claude-code-research-in-sleep/analyze-results"><img src="https://agentmods.dev/badge/skills/wanshuiyin/auto-claude-code-research-in-sleep/analyze-results.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 37 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 376 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 pass 24 Oct 2026
  • Snyk pass 24 Oct 2026
  • NVIDIA SkillSpector pass 7 Sept 2026
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 $0.00037 $0.00376
Opus 5.5 $0.00015 $0.00150
Sonnet 5.5 $0.00007 $0.00075
Haiku 4.5 $0.00004 $0.00038

Measured 2mo ago against content hash 2b97b8ae1a27, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

analyze-results 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 2mo 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.

Origin

Copies of this mod

7 near-identical copies found in the catalogue:

skills/analyze-results/SKILL.md · 47 lines

What it actually says

Analyze Experiment Results

Analyze: $ARGUMENTS

Workflow

Step 1: Locate Results

Find all relevant JSON/CSV result files:

  • Check figures/, results/, or project-specific output directories
  • Parse JSON results into structured data

Step 2: Build Comparison Table

Organize results by:

  • Independent variables: model type, hyperparameters, data config
  • Dependent variables: primary metric (e.g., perplexity, accuracy, loss), secondary metrics
  • Delta vs baseline: always compute relative improvement

Step 3: Statistical Analysis

  • If multiple seeds: report mean +/- std, check reproducibility
  • If sweeping a parameter: identify trends (monotonic, U-shaped, plateau)
  • Flag outliers or suspicious results

Step 4: Generate Insights

For each finding, structure as:

  1. Observation: what the data shows (with numbers)
  2. Interpretation: why this might be happening
  3. Implication: what this means for the research question
  4. Next step: what experiment would test the interpretation

Step 5: Update Documentation

If findings are significant:

  • Propose updates to project notes or experiment reports
  • Draft a concise finding statement (1-2 sentences)

Output Format

Always include:

  1. Raw data table
  2. Key findings (numbered, concise)
  3. Suggested next experiments (if any)
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. 2mo ago First seen · 47 lines · 37 tokens per session scan A 2b97b8ae1a27

Subscribe to this mod's changes

analyze-results is a skill published in the GitHub repository wanshuiyin/Auto-claude-code-research-in-sleep (19,107 stars, last pushed 2d ago), licensed MIT. It adds 37 tokens to every session and 376 once invoked, about $0.0001 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.

Analytics and metricsClaude CodeData and AIai-researchautonomous-agentcodexmcpml-research
Related

analyze-results compared with skills from other repositories

analyze-results next to 6 skills from other repositories
ModTypeStarsLast pushLicenceScanTokensWorks with
analyze-resultswanshuiyin/Auto-claude-code-research-in-sleepSkill19k2d agoMITA37Claude Code
esmfold2aipoch/open-scienceSkill5.5ktodayApache-2.0A223Claude Code, Codex
evo2aipoch/open-scienceSkill5.5ktodayApache-2.0A83Claude Code, Codex
scgptaipoch/open-scienceSkill5.5ktodayApache-2.0A89Claude Code, Codex
ml-training-recipesOrchestra-Research/AI-Research-SKILLsSkill15k1mo agoMITA88Claude Code
experiment-tracking-swanlabOrchestra-Research/AI-Research-SKILLsSkill15k1mo agoMITA40Claude Code, Codex
prompt-guardOrchestra-Research/AI-Research-SKILLsSkill15k1mo agoMITB65Claude Code