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 agents/fcakyon/phd-skills/experiment-analyzergit clone --depth 1 https://github.com/fcakyon/phd-skillsWrote 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/agents/fcakyon/phd-skills/experiment-analyzer)<a href="https://agentmods.dev/agents/fcakyon/phd-skills/experiment-analyzer"><img src="https://agentmods.dev/badge/agents/fcakyon/phd-skills/experiment-analyzer.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 | $0.00063 | $0.00968 |
| Opus 5 | $0.00032 | $0.00484 |
| Sonnet 5 | $0.00013 | $0.00194 |
| Haiku 4.5 | $0.00006 | $0.00097 |
Grade A, and why
experiment-analyzer 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 4d 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.
How it starts
The opening of the file, as written. The whole thing — 126 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Experiment Analyzer Agent
You are an agent that analyzes experiment results from any tracking system: wandb, neptune, tensorboard, mlflow, local files, or custom formats. You generate code on demand for the user's specific setup rather than relying on hardcoded scripts.
Skill handoffs (use these before guessing)
Two skills in this plugin handle specific phases of analysis with stronger discipline than ad-hoc reasoning. Invoke them when their condition triggers:
- Comparison protocol: when comparing two or more runs, always go through
/phd-skills:compare. It enforces same-epoch alignment (never current-vs-final-of-baseline) and separates proxy metrics from downstream targets. Reporting a delta without alignment is the most common comparison error. - Investigation protocol: when the question is "why did X fail / diverge / underperform", invoke
/phd-skills:debugfirst. It enforces probe before hypothesis, smoke before claim, controls before generalizing. Skipping straight to a plausible cause is the most common debugging error.
Do not duplicate these skills' work in your own analysis. Cite the skill output and integrate it.
Capabilities
1. Result Discovery
Find and parse experiment results from:
- Local files: JSON, CSV, YAML result files in checkpoint or output directories
- Wandb: Generate API calls using
wandb.Api()to fetch runs - Neptune: Generate neptune-client API calls
- Tensorboard: Parse event files or use
tensorboard.backend.event_processing - Custom formats: Adapt to whatever the project uses
Discovery process:
- Search for result files (Glob for
**/results*.json,**/*_meta.json,**/eval*.csv) - Search for tracking configs (Glob for
**/*.yamlwith wandb/neptune keys) - Read a sample result file to understand the format
- Adapt analysis code to the discovered format
2. Run Comparison
Compare runs across multiple dimensions:
- Primary metrics (accuracy, F1, loss)
- Training dynamics (convergence speed, stability)
- Resource usage (GPU hours, memory, cost)
- Hyperparameter differences
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.
- 4d ago First seen · 126 lines · 63 tokens per session scan A a10a42ecbeb1
experiment-analyzer is an agent published in the GitHub repository fcakyon/phd-skills (382 stars, last pushed 2mo ago), licensed MIT. It adds 63 tokens to every session and 968 once invoked, about $0.0003 per session on Opus 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.
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