Resume

Skill timdevai/proteus/skills/community/from-alireza/engineering/autoresearch-agent/skills/resume

Resume a paused experiment. Checkout the experiment branch, read results history, continue iterating.From its SKILL.md

Install

npx -y skills add timdevai/proteus --skill resume

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

1 stars1 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

SKILL.md

2.0 KB, 481 tokens by cl100k_base, as published. Nobody here has run it

/ar:resume — Resume Experiment

Resume a paused or context-limited experiment. Reads all history and continues where you left off.

Usage

/ar:resume                                  # List experiments, let user pick
/ar:resume engineering/api-speed            # Resume specific experiment

What It Does

Step 1: List experiments if needed

If no experiment specified:

python {skill_path}/scripts/setup_experiment.py --list

Show status for each (active/paused/done based on results.tsv age). Let user pick.

Step 2: Load full context

# Checkout the experiment branch
git checkout autoresearch/{domain}/{name}

# Read config
cat .autoresearch/{domain}/{name}/config.cfg

# Read strategy
cat .autoresearch/{domain}/{name}/program.md

# Read full results history
cat .autoresearch/{domain}/{name}/results.tsv

# Read recent git log for the branch
git log --oneline -20

Step 3: Report current state

Summarize for the user:

Resuming: engineering/api-speed
  Target: src/api/search.py
  Metric: p50_ms (lower is better)
  Experiments: 23 total — 8 kept, 12 discarded, 3 crashed
  Best: 185ms (-42% from baseline of 320ms)
  Last experiment: "added response caching" → KEEP (185ms)

  Recent patterns:
  - Caching changes: 3 kept, 1 discarded (consistently helpful)
  - Algorithm changes: 2 discarded, 1 crashed (high risk, low reward so far)
  - I/O optimization: 2 kept (promising direction)

Step 4: Ask next action

How would you like to continue?
  1. Single iteration (/ar:run)  — I'll make one change and evaluate
  2. Start a loop (/ar:loop)     — Autonomous with scheduled interval
  3. Just show me the results    — I'll review and decide

If the user picks loop, hand off to /ar:loop with the experiment pre-selected. If single, hand off to /ar:run.

What ships with it

Read from the repository

Just SKILL.md. No reference files, no scripts.

Gives 0 of the 12 instructions most hr recruiting skills give in 481 tokens

Counted across 356 of the 357 authors here whose files we hold, read 2026-08-07

Quantify achievements with specific metricsin 14 of 356, across 6 files
Keep the resume under two pagesin 14 of 356, across 6 files
Request the full job description if not providedin 12 of 356, across 4 files
Extract keywords and prioritize job requirementsin 12 of 356, across 4 files
Stop and ask for clarification if required inputs are missingin 12 of 356, across 5 files
Map candidate experience to job requirementsin 11 of 356, across 3 files
Ask if the user wants adjustmentsin 11 of 356, across 3 files
Provide strengths and gap analysis after the resumein 10 of 356, across 2 files
Request candidate background details if not providedin 10 of 356, across 2 files
Format experience bullets as action verb plus resultin 10 of 356, across 2 files
Ask for missing inputs before startingin 10 of 356, across 9 files
Use exact job description terminologyin 9 of 356, across 1 file

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.