skills/job-babysitter/SKILL.md
This skill should be used to watch a long-running background job (ffmpeg/media encode, qmd or other embedding/vector-DB run, batch agent/LLM pipeline, or a real-browser/agent-browser daemon) until it finishes or wedges, then deliver a verdict (done, needs-attention, or blocked) plus the exact next command, without burning dozens of manual poll commands. Triggers on "babysit this job", "watch this until it's done", "ping me when the encode/embed/batch finishes", "is this background process stuck", "monitor this ffmpeg/qmd run", or any request to wait on a long-running process and be told when it's complete or hung.
npx skillsauth add glebis/claude-skills job-babysitterInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Stop manually polling long-running background jobs. Instead of dozens-to-hundreds of
ls -lh / ps checks while guessing at completion, start one background watcher that
detects the terminal state via plateau heuristics, then routes a verdict — done,
needs-attention, or blocked — with the exact next command.
A night-shift nurse for background jobs: it checks vitals on a schedule and escalates only when something is actually wrong.
Use when a job will run long enough that babysitting it by hand wastes attention:
Do NOT use for jobs that finish in seconds, or where a single Bash call already
returns the result.
This skill orchestrates Claude Code's own primitives — do not reimplement them:
run_in_background: true. When it exits, the harness
re-invokes the agent automatically — no manual polling loop needed.scripts/watch_job.py) owns the deterministic part: poll with backoff,
detect plateau, distinguish done from stuck, emit a verdict JSON.references/playbook.md.Determine what can be watched, in order of reliability:
pgrep, or ps.Read references/playbook.md § "Completion heuristics by job type" to pick flags for
the specific job type (ffmpeg, embed, batch, browser).
Run with run_in_background: true. Always pass --pid when known; add file/log
signals as corroboration. Write the verdict to a known path.
scripts/watch_job.py \
--label "lab05 stream encode" \
--pid <PID> \
--output-file /path/to/output.mp4 \
--plateau-bytes 65536 --plateau-polls 5 --stuck-after 120 \
--max-wait 7200 \
--verdict-out /tmp/job-babysitter-<label>.json
The watcher prints a one-line JSON heartbeat per poll (tail it for live progress) and
writes the final verdict JSON to --verdict-out on exit.
Tuning lives in the playbook; sensible defaults: --interval 10 (backs off to 60),
--plateau-polls 4, --stuck-after 300, --max-wait 7200.
The harness re-invokes the agent when the background watcher finishes. Read the
verdict JSON. It has status ∈ {done, needs-attention, blocked}, a reason,
suggested_next, elapsed time, and final size.
ffprobe for media, count match for embeds), then proceed with the original task.--max-wait. Report honestly: "gave up
waiting" ≠ "failed". Offer to re-check or extend the ceiling.Default to in-session resume. If the user picked a channel (Telegram, voice/TTS,
desktop notification), route per references/playbook.md § "Notification routing".
Always include the status emoji, label, elapsed time, and the exact next command.
--stuck-after — the watcher already enforces this before returning needs-attention.pkill, kill, WAL checkpoint, VACUUM,
daemon restart. Diagnose read-only first.scripts/watch_job.py — background watcher: plateau detection, stuck-vs-done logic,
verdict JSON. Stdlib only, Python 3.11+.references/playbook.md — per-job-type completion heuristics, the safe-recovery
table, and notification routing. Load when picking watcher flags or handling a
needs-attention/blocked verdict.development
This skill should be used when designing, running, validating, or auditing statistical experiments on personal or observational time-series data (health metrics, speech/text corpora, behavioral logs, diaries, n-of-1 self-tracking). It enforces pre-registration, exact permutation tests, FDR discipline, data-validation gates, adversarial code review, and cross-validation with external models. Triggers on "design an experiment", "test this hypothesis on my data", "is this correlation real", "audit these findings", "pre-register", "validate this dataset", or any n-of-1 / quantified-self analysis request.
development
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development
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