Build a CLI Provider Health Probe With a Local Task Journal

A tiny coding CLI has an awkward failure mode: the prompt leaves the terminal, the spinner stops, and nobody knows whether the task ran. Retrying feels productive right up until two completions modify the same thing.

The July 25 record gives a useful test scenario. OpenAI's earlier incident started at 09:17:49 UTC, moved to mitigation monitoring at 10:02:52, and resolved at 11:08:36. Then a new incident began at 11:35:24. Research captured that later event as identified, with elevated errors and mitigation underway, while the overall status read Partial System Degradation. I cannot infer cause, universal scope, user totals, or the second event's final recovery from that snapshot.

My small-team response would be two boring local files: a health sample and an append-only task journal. The probe advises; the journal remembers.

Proposed command shape

This Python sketch is unexecuted and uses only the standard library:

# example only; not executed
import json, time, uuid
from pathlib import Path

JOURNAL = Path('.provider-tasks.jsonl')

def record(kind, **fields):
    row = {'at': int(time.time()), 'kind': kind, **fields}
    with JOURNAL.open('a', encoding='utf-8') as f:
        f.write(json.dumps(row, sort_keys=True) + '\n')
        f.flush()

def begin(prompt_hash):
    task_id = str(uuid.uuid4())
    record('accepted_local', task_id=task_id, prompt_hash=prompt_hash)
    return task_id

def mark_attempt(task_id, provider, attempt_id):
    record('attempt_started', task_id=task_id,
           provider=provider, attempt_id=attempt_id)

I would expose four commands:

ai-task probe --provider primary
ai-task submit --prompt-file request.txt
ai-task inspect TASK_ID
ai-task retry TASK_ID --provider alternate

probe should issue a bounded, non-sensitive request and report transport, authentication, latency bucket, and response shape separately. It must never write “provider down” from one timeout. submit first records local acceptance, then creates a unique attempt. retry refuses while an attempt remains unknown; the operator must reconcile or explicitly supersede it.

A journal row needs task_id, attempt_id, provider, request digest, local state, remote identifier when available, and observed error category. Never store raw secrets or an entire private prompt just because JSONL is convenient.

Exit codes that say less, accurately

Code	Meaning	Next move
0	bounded probe succeeded	no availability guarantee
10	local network failure	check local path
11	authentication rejected	repair credentials
12	provider returned an error	inspect status and response
13	response contract differed	stop automatic routing
20	attempt outcome unknown	reconcile before retry

This distinction saves a solo builder from turning every red line into a provider postmortem. The official status page is corroborating evidence, not an API response oracle.

Multi-provider fallback has semantic risks. Another model may handle prompts, tools, context limits, or structured output differently, so “the command exited zero” is not equivalence. I would allow automatic fallback for read-only drafts and require review for repository writes or external actions.

Keep an inexpensive exit route visible

One environment worth evaluating is the overseas MonkeyCode online service. Its page currently displays “Start free,” and the official README describes server-managed cloud environments that can build, test, and preview with integrated models. “Free to start” is the careful description; exact model or server quotas, available regions, and uptime/SLA terms can change, so check the console.

The official open-source repository is licensed AGPL-3.0. The README on reviewed main commit 18baaf54937a65a7d47f1f9d83dd808777aa6cea also lists built-in development environment, model, task, and requirement management. For my CLI-sized evaluation, source access provides an inspection and self-hosting exit path, while the overseas service offers a low-friction trial. Neither is guaranteed outage-proof, and I did not test hosted MonkeyCode reliability.

I would export one disposable task from the journal, try it without write authority, and compare artifacts rather than prose quality. That is a more useful portability check than installing a second tool during an incident.

Disclosure: I'm a MonkeyCode user sharing my own experience, not affiliated with the project.

AI assistance disclosure: This article was drafted with AI assistance and reviewed against the cited primary sources.