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PKU-DAIRPKU-DAIR/AutoSci/.claude/skills/exp-status/SKILL.md

exp-status

Skill1.7k starsChanged 34 days agoMITView on GitHub

View the status of all running experiments; optionally auto-collect completed experiments and advance the pipeline

We split this skill into the rules it gives an agent

12 rules. About 0 are common, 1 is distinctive, and 11 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈1

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 11

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    No arguments (default): check all running experiments, print status table

    line 17
  2. Very specific:

    If --pipeline <slug> is specified:

    line 51
  3. Very specific:

    Read wiki/outputs/pipeline-progress.md, extract the slug list from the stage3a_deployed field

    line 52
  4. Very specific:

    If the file does not exist or slug does not match: report error, suggest running /research first or specifying manually

    line 53
  5. Very specific:

    Use Glob to scan wiki/experiments/*.md, filter for status == running

    line 55
  6. Very specific:

    No output → alive: false (session is gone)

    line 79
  7. Very specific:

    If wiki status is already completed: classify as collected

    line 94
  8. Distinctive:

    After all collections are done, re-print the updated status report.

    line 155
  9. Very specific:

    Read stage3a_deployed list from wiki/outputs/pipeline-progress.md

    line 160
  10. Very specific:

    Check the status of each slug's wiki/experiments/{slug}.md

    line 161
  11. Very specific:

    Condition met: all experiments have status == completed

    line 162
  12. Very specific:

    screen -ls returns nothing: does not mean the experiment failed — may be a brief delay.

    line 202

≈ “Common” is an estimate: at least 1 in 5 of the 308,232 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

/exp-status

Unified experiment status monitoring entry point. Scans all running experiments, performs a live status check on each (screen session / SSH), and outputs a status table (alive / anomaly / completed) to guide the user's next actions.

When used with /research --auto, acts as a periodic checker scheduled by CronCreate: when all experiments in a pipeline are completed, automatically triggers /research --start-from stage4.

Inputs

  • No arguments (default): check all running experiments, print status table
  • --pipeline <slug> (optional): check only experiments belonging to the specified pipeline; additionally print overall pipeline progress
  • --collect-ready (optional): auto-call /exp-run --collect for all experiments whose session has already ended
  • --auto-advance (optional, requires --pipeline <slug>): if all pipeline experiments are completed, automatically trigger /research --start-from stage4 without waiting for the user

Outputs

  • Status report (terminal output, all modes): list of experiments in running/anomaly/completed states
  • wiki/experiments/{slug}.md — updated (outcome/key_result/status) when --collect-ready triggers Phase 4
  • wiki/outputs/pipeline-progress.md — --auto-advance updates current_stage → stage4 (done internally by /research --start-from stage4)
  • wiki/log.md — appended status check log

Wiki Interaction

Reads

  • wiki/experiments/*.md — status, remote frontmatter (server/session/started), date_planned
  • wiki/outputs/pipeline-progress.md — in --pipeline mode, identifies target experiments and monitoring_cron_id

Writes

  • wiki/experiments/{slug}.md — updated via /exp-run --collect in --collect-ready mode
  • wiki/outputs/pipeline-progress.md — updated by /research when --auto-advance triggers Stage 4
  • wiki/log.md — appended status check log

Graph edges created

  • None (result writes triggered indirectly via /exp-run --collect do not produce new edges)

Workflow

Precondition: confirm working directory is the wiki project root (directory containing wiki/, raw/, tools/).

Step 1: Collect Target Experiment List

  1. Determine check scope:
    • If --pipeline <slug> is specified:
      • Read wiki/outputs/pipeline-progress.md, extract the slug list from the stage3a_deployed field
      • If the file does not exist or slug does not match: report error, suggest running /research first or specifying manually
    • Otherwise:
      • Use Glob to scan wiki/experiments/*.md, filter for status == running
  2. If no running experiments:
    • Print a friendly message:
      No running experiments found.
      - To start an experiment: /exp-run <slug>
      - To see all experiments: check wiki/experiments/
    • Return

Step 2: Check Status of Each Experiment

For each target experiment, execute in parallel (or sequentially):

  1. Read experiment page: from wiki/experiments/{slug}.md get:
    • remote block (if present, this is a remote experiment)
    • run_log path
    • started (from remote.started or date_planned, used to compute elapsed time)
    • Deployment environment (has remote block → remote, otherwise → local)
  2. Check process status:
    • Local: screen -ls | grep "exp-{slug}"
      • Has output → alive: true
      • No output → alive: false (session is gone)
    • Remote: python3 tools/remote.py check --name "exp-{slug}"
      • Parse JSON: alive, last_lines, anomalies
  3. If alive == true:
    • Fetch recent logs (at most 20 lines):
      • Local: tail -20 {run_log}
      • Remote: use last_lines from the check command response
    • Extract latest metric (loss, accuracy, step, etc. — grep the last metric line)
    • Detect anomalies (NaN/OOM/Traceback/Inf): use anomalies field from remote.py check (remote), or manual grep (local)
    • Compute elapsed time (current time − started)
    • Classify as: running or anomaly
  4. If alive == false:
    • Classify as: completed_pending_collect (session gone but wiki status is still running)
    • If wiki status is already completed: classify as collected
  5. Aggregate results: build status dict {slug: {state, elapsed, latest_metric, anomalies}}

Step 3: Print Status Report

# Experiment Status — {YYYY-MM-DD HH:MM}

### 🔄 Running ({N})
| Experiment | Elapsed | Latest | Env |
|-----------|---------|--------|-----|
| [[exp-foo-baseline]] | 2.3h | loss: 0.42 | local |
| [[exp-foo-validation]] | 1.1h | step: 1200 | remote (gpu1) |

### ⚠️ Anomaly Detected ({N})
| Experiment | Elapsed | Issue | Action |
|-----------|---------|-------|--------|
| [[exp-foo-ablation]] | 0.8h | NaN loss at step 500 | Run `/exp-run exp-foo-ablation --collect` to inspect |

### ✅ Completed — Pending Collect ({N})
| Experiment | Finished (estimate) |
|-----------|---------------------|
| [[exp-foo-sanity]] | session gone |

### 📦 Already Collected ({N})
| Experiment | Outcome |
|-----------|---------|
| [[exp-foo-old]] | succeeded |

---
### Actions
```bash
# Collect all completed experiments at once:
/exp-status --collect-ready

# Collect a specific experiment:
/exp-run exp-foo-sanity --collect

# Pipeline progress (if in /research):
/exp-status --pipeline {pipeline-slug}

Append log:
```bash
python3 tools/research_wiki.py log wiki/ \
  "exp-status | running: {N}, anomaly: {M}, pending-collect: {K}"

Step 4: --collect-ready Auto-Collect (if specified)

For each completed_pending_collect experiment, call /exp-run --collect:

Skill: exp-run
Args: "{slug} --collect"

Collect each completed experiment sequentially (not in parallel, to avoid concurrent wiki writes).

After all collections are done, re-print the updated status report.

Step 5: --auto-advance Pipeline Advance (if both --pipeline and --auto-advance are specified)

  1. Check pipeline completion condition:
    • Read stage3a_deployed list from wiki/outputs/pipeline-progress.md
    • Check the status of each slug's wiki/experiments/{slug}.md
    • Condition met: all experiments have status == completed
  2. If condition is not met (some experiments still running or pending collect):
    • Print current progress: Pipeline {slug}: {M}/{N} experiments completed
    • Return (do not advance)
    • Cron will trigger again in 30 minutes
  3. If condition is met (all experiments completed):

    a. Print notification and trigger Stage 4:

    • Print:
      ✅ All experiments completed for pipeline {slug}!
      Advancing to Stage 4 (Verdict & Iteration)...
    • Append log:
      python3 tools/research_wiki.py log wiki/ \
        "exp-status | pipeline {slug}: all experiments done, advancing to stage4"
    • Trigger next stage:
      Skill: research
      Args: "--start-from stage4"

Constraints

  • Read-only in non --collect-ready mode: without --collect-ready, do not modify any wiki files
  • --auto-advance requires --pipeline: using --auto-advance alone is invalid, report an error
  • Status checks must be non-blocking: each experiment check should complete quickly (single SSH check or screen -ls)
  • Anomalies are not auto-fixed: /exp-status only reports anomalies; fixes require the user to manually call /exp-run --collect
  • pipeline-progress.md must exist: in --pipeline mode, if the file is missing, report an error

Error Handling

  • No running experiments: print friendly message, not an error; provide next step suggestions
  • --pipeline but pipeline-progress.md does not exist: report error "Pipeline progress file not found. Run /research <direction> first or check wiki/outputs/"
  • --auto-advance without --pipeline: report error "--auto-advance requires --pipeline <slug>"
  • SSH connection fails (remote experiment): mark that experiment as check_failed, note it in the report, continue checking other experiments
  • screen -ls returns nothing: does not mean the experiment failed — may be a brief delay; mark as completed_pending_collect
  • /exp-run --collect fails (--collect-ready mode): record the failure, continue collecting other experiments, report all failures at the end

Dependencies

Skills(via Skill tool)

  • /exp-run — call collect phase in --collect-ready mode
  • /research — trigger Stage 4 via --auto-advance

Tools(via Bash)

  • python3 tools/remote.py check --name "exp-{slug}" — remote experiment status check
  • python3 tools/remote.py tail-log --name "exp-{slug}" --lines 20 — fetch remote logs
  • python3 tools/research_wiki.py set-meta <path> <field> <value> — update pipeline-progress
  • python3 tools/research_wiki.py log wiki/ "<message>" — append log
  • screen -ls — local process status
  • tail -20 {log} — fetch local logs

Claude Code Native

  • Read — read experiment pages and pipeline-progress
  • Write — update pipeline-progress status
  • Glob — scan wiki/experiments/*.md
  • Bash — screen/tail and other system commands
  • Skill — call /exp-run --collect and /research

Called by

  • CronCreate schedule (created by /research --auto Stage 3b: triggers every 30 minutes)
  • User directly
  • /research Stage 3b (in interactive mode, suggested to user)

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