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Skill evo-hq/evo/plugins/evo/npm/skills/report

turns your codebase into an autoresearch loop — discovers what to measure, instruments the benchmark, then runs tree search with parallel subagents.

Install
npx -y skills add evo-hq/evo --skill report

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What its author says it does

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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.

SKILL.md

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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:

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.

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.