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Report

OrganizationPopular
evo-hq
report

Read-only evo run reporting. Use when the user invokes /evo:report, asks what happened overnight, asks what improved recently, asks for the best/frontier candidates, asks for a quick score chart without opening the dashboard, or wants the scatter plot in chat output. Never run benchmarks, gates, Slurm commands, evo run, or ad-hoc verification scripts for report requests.

Overview

Publisherevo-hq
Repositoryevo
Skill namereport
Stars
1.5K
Forks
110
Bundled files
Instructions only
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by evo-hq on GitHub. Read the source before you install it.

Installation

Install the Report AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/evo-hq/evo.git /tmp/evo
mkdir -p .claude/skills
cp -r /tmp/evo/plugins/evo/npm/skills/report .claude/skills/report
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Report in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Report on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Report is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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>/.

For chart requests, render the dashboard's scatter plot as a colored terminal block, one chart per run, sized to the current terminal.

What it shows

Mirrors the web dashboard's score scatter (left rail of evo dashboard):

  • X = experiment creation order, Y = score
  • Dot color by status: green = committed valid result, red = failed, purple = active, grey = pending / evaluated / discarded / pruned
  • ★ marks the current best valid committed-result experiment. pruned with prune_kind=exhausted can still be best; prune_kind=invalid and its descendants cannot.
  • Yellow ring on dots that sit on the best-path spine (root → best)
  • Yellow stair line traces cumulative-best across valid committed-result experiments
  • ○ at the baseline for experiments that have no score yet (active / pending)

Every run in the workspace is rendered, stacked top-to-bottom, with a header line showing run_id · target · metric.

How to invoke

Run:

bash
evo report

That is it. Print the output verbatim in your reply so the user sees the chart. Do not summarize the chart in prose — the visual is the point.

Flags:

  • --color always|never|auto — force or suppress ANSI color. Default auto (color when stdout is a TTY). Pass --color always if you are piping through a host that strips TTY but renders ANSI in chat.
  • --watch [SECONDS] — live-refresh mode (like nvidia-smi -l). Re-reads the workspace every N seconds (default 2) and redraws in place. Ctrl-C to exit. Use this when you want to babysit a running optimization without manually re-invoking the report.

When not to use

  • For one-off score lookups, evo status or evo show <id> is faster.
  • For navigating the tree shape, evo tree is the right command.
  • For interactive exploration (click a dot, open a drawer), point the user at evo dashboard instead.

Overnight / Improvement Reports

When the user asks what happened recently or what improved, summarize from recorded evo state:

  1. Run evo status, evo frontier, and evo tree.
  2. Use evo show <id> for the best node and any recent committed/evaluated nodes you mention.
  3. Use evo diff <id> only to explain what changed in a recorded experiment.
  4. If you need benchmark details, read the existing outcome.json, benchmark.log, or declared artifacts for that experiment. Treat missing artifacts as "not recorded", not as permission to rerun.

Report:

  • best current experiment and score;
  • score delta versus baseline or parent;
  • top candidates/frontier if relevant;
  • failed/evaluated nodes that need attention;
  • any caveats about gates, missing held-out checks, or tied candidates.

If the user wants fresh validation or reruns, ask them to explicitly start a new optimization or evaluation command. Do not infer that from a report request.

Frequently asked questions

What does the Report AI skill do?

Read-only evo run reporting. Use when the user invokes /evo:report, asks what happened overnight, asks what improved recently, asks for the best/frontier candidates, asks for a quick score chart without opening the dashboard, or wants the scatter plot in chat output. Never run benchmarks, gates, Slurm commands, evo run, or ad-hoc verification scripts for report requests.

Why use Report on TypingMind?

Because you install it once and use it with any model. Report is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Report in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/evo-hq/evo/tree/main/plugins/evo/npm/skills/report. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Report?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Report?

As many as you like. As long as a model supports skills, you can use Report with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Report AI skill free?

Yes. It is published on GitHub by evo-hq under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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