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inspect-eval-log-analysis Skill

AI Agent SkillJupyter NotebookOpen source

Guide for parsing and analyzing inspectai .eval log files. Use this when asked to interpret eval results, extract tool calls, find scores, or investigate agent behavior from .eval logs. Published by microsoft in ACESEvals.

What is inspect-eval-log-analysis Skill?

Guide for parsing and analyzing inspectai .eval log files. Use this when asked to interpret eval results, extract tool calls, find scores, or investigate agent behavior from .eval logs. Published by microsoft in ACESEvals. This profile combines repository metadata with install, compatibility, and usage signals so developers can quickly decide whether it fits their agent workflow before opening the source repository.

Trust signal
95/100
Maintenance signal
90/100
Adoption signal
17/100

Automated repository signals based on public metadata such as recency, license, installation evidence, and adoption. These are not a security audit or endorsement.

Key capabilities

  • Includes SKILL.md support
  • Reusable instructions support
  • Data analysis
  • Data analysis use cases

Technical details

Copy skill directory
  • Install or run with Copy skill directory

When to use inspect-eval-log-analysis Skill

  • Use it for data analysis.

Built with

Jupyter NotebookCopy skill directory

Editorial notes

Source

  • Creator: microsoft
  • Repository: microsoft/ACESEvals
  • Skill file: .github/skills/inspect-eval-log-analysis/SKILL.md

What it does

Guide for parsing and analyzing inspectai .eval log files. Use this when asked to interpret eval results, extract tool calls, find scores, or investigate agent behavior from .eval logs.

Skill instructions

To parse and analyze inspectai .eval log files, follow this process: 1. Understanding the .eval File Format .eval files are ZIP archives (not plain text). They contain structured JSON: <hash.eval (ZIP) ├── header.json Eval metadata ├── journal/ │ ├── start.json Eval config, plan, model info │ └── summaries/ │ └── 1.json Per-epoch summary ├── samples/ │ └── <taskidepoch1.json Full sample data per task ├── summaries.json Aggregated score summaries └── reductions.json Score reduction results Never try to read .eval files as plain text. Always use zipfile: python import json, zipfile with zipfile.ZipFile("logs/<file.eval", "r") as zf: print(zf.namelist()) See all files inside 2. Extract Sample Data (Messages, Tool Calls, Scores) The richest data is in the samples/ JSON files. Each sample contains the full conversation: python import json, zipfile with zipfile.ZipFile("logs/<file.eval", "r") as zf: for name in zf.namelist(): if not name.startswith("samples/"): continue sample = json.loads(z

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Frequently asked questions

What is inspect-eval-log-analysis?

inspect-eval-log-analysis is a open-source AI agent skill with Copy skill directory. Guide for parsing and analyzing inspectai .eval log files. Use this when asked to interpret eval results, extract tool calls, find scores, or investigate agent behavior from .eval logs.

Who is inspect-eval-log-analysis best for?

inspect-eval-log-analysis is best for reusing agent instructions, scripts, and references, data analysis workflows.

How do I install inspect-eval-log-analysis?

Install or run inspect-eval-log-analysis using Copy skill directory. Check inspect-eval-log-analysis for the latest setup command.

Is inspect-eval-log-analysis actively maintained?

inspect-eval-log-analysis may need a closer maintenance check before production use.

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Last commit
12 days ago
Repository age
4 months
License
MIT

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