📊
DataPython

Evo Report

by evo-hq

Evo Report is a Data skill for Claude Code, published by evo-hq in evo.

1.4K stars105 forkson evo-hq/evoAdded 2026/08/19+1% in starsRepository updated 2026/07/17
View source on GitHubDownload repo ZIP
agent-skillsautonomous-agentsautoresearchclaude-codecode-optimizationcodexevolutionary-algorithmsllm-agents
Install in seconds
Install Evo Report
Copy Evo Report into your Claude Code skills folder. Run the command in your terminal, or review the source on GitHub before installing.
terminal
npx degit https://github.com/evo-hq/evo/tree/main/plugins/evo/skills/report ~/.claude/skills/report

Requires Node.js. Downloads this skill only — not the rest of the repository — into your Claude Code skills folder.

Without Node.js

git clone https://github.com/evo-hq/evo.git

Clones the whole repository, then copy the skill’s own directory into your skills folder yourself.

In this catalog

Source file
plugins/evo/skills/report/SKILL.md in evo-hq/evo
Installs to
~/.claude/skills/report
Collection
One of 8 skills cataloged from this repository
Category
Data — 668 skills

What Evo Report does

Evo Report provides read-only reports from recorded evo workspace state, including score charts, frontier candidates, and status summaries. Use it when someone asks what happened overnight, what improved, or wants a quick chart without opening the dashboard.

Evo Report is cataloged under Data on DirSkills. Evo Report comes from a repository tagged agent-skills, autonomous-agents, autoresearch, claude-code and code-optimization.

Documentation

README

Report

Report the current evo workspace from recorded state only. A report request is read-only, even if the user phrases it casually as "what happened?", "what got better?", "what should I pay attention to?", or "I just woke up".

Do not spend compute while reporting:

  • Do not run evo run, evo gate check, benchmark commands, or project eval scripts.
  • Do not run python bench.py, python slurm_eval.py, sbatch, srun, squeue, sacct, or scancel to verify a result.
  • Do not create launcher, monitor, parsing, or analysis scripts.
  • Do not edit files.

Use stored evo state instead: evo report, evo status, evo tree, evo frontier, evo show <id>, evo diff <id>, and immutable artifacts under .evo/run_*/experiments/<exp>/attempts/<NNN>/.

This is the opening of the README. Read the full README on GitHub.

Frequently asked about Evo Report

  • What else does evo-hq publish alongside Evo Report?

    Evo Report is one of 8 skills that DirSkills catalogs from evo-hq/evo, the repository it ships in. Its siblings there include Discover, Evo Subagent Protocol and Finetuning. Each one is a separate skill with its own page in this directory, installs the same way Evo Report does, and is maintained by evo-hq in that same repository. The rest of the collection is listed on the evo-hq/evo page.

  • How does Evo Report compare to other Data skills?

    Evo Report ranks #405 by stars among the 668 Data skills in this catalog. The most-starred ones next to it are Benchmark Methodology, Jupyter Notebook and Solana. DirSkills ranks by the star count of the repository each skill ships in, so that order reflects how popular those repositories are rather than any review of Evo Report against them. Open each page to compare what they document and how they install.

More from evo-hq/evo

Evo Report is one of 8 skills cataloged on DirSkills from evo-hq/evo.

See all 8 skills →
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Finetuning

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Infra Setup

Infra Setup guides switching the evo backend between local worktrees, pool slots, and remote providers like Modal, E2B, or SSH. It checks prerequisites, verifies auth, and shows the exact config commands so experiments run on the intended infrastructure.
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Optimize

Optimize drives structured autoresearch iteration after evo:discover and the baseline commit, orchestrating subagents to run experiments and improve the current best frontier. Use it when the user invokes /evo:optimize or asks to try ideas, variants, or continue an evo search.
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Optimize

Optimize runs the evo optimization loop with parallel subagents until interrupted or the stall limit is reached. Use it after a baseline experiment is committed to evolve code changes against benchmarks and prune dead branches.
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