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 llv22/AutoResearchWithEyes --skill analyze-resultsgit clone --depth 1 https://github.com/llv22/AutoResearchWithEyesWrote 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/llv22/autoresearchwitheyes/analyze-results)<a href="https://agentmods.dev/skills/llv22/autoresearchwitheyes/analyze-results"><img src="https://agentmods.dev/badge/skills/llv22/autoresearchwitheyes/analyze-results.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 | $0.00037 | $0.00378 |
| Opus 5 | $0.00018 | $0.00189 |
| Sonnet 5 | $0.00007 | $0.00076 |
| Haiku 4.5 | $0.00004 | $0.00038 |
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 8d 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 analyze-results — 4 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.
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:
- Observation: what the data shows (with numbers)
- Interpretation: why this might be happening
- Implication: what this means for the research question
- 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:
- Raw data table
- Key findings (numbered, concise)
- Suggested next experiments (if any)
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.
- 8d ago First seen · 47 lines · 37 tokens per session scan A 634f9c70c5c6
analyze-results is a skill published in the GitHub repository llv22/AutoResearchWithEyes (5 stars, last pushed 1mo ago), licensed MIT. It adds 37 tokens to every session and 378 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 97% identical to analyze-results, differing in 4 lines, and is treated as a copy.
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