# Autonomous Continuation

> Keep Claude Fable 5 / 5.1 running to completion in unattended pipelines — no stopping on a statement of intent, no mid-run permission questions, no quiet narrowing of the task, no self-imposed session splits over context worries. Use for overnight runs, scheduled jobs, CI agents, and any harness where nobody is watching to type "continue".

- Skill: `kpab/autonomous-continuation` (Agent Skill)
- Install (CLI): `npx skillmds@latest add kpab/autonomous-continuation`
- Raw SKILL.md: https://api.skillmd.com/api/skills/kpab/autonomous-continuation/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Productivity
- Author: kpab (https://skillmd.com/u/kpab)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/kpab/autonomous-continuation

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# Autonomous Continuation

Deep into long sessions, two rare but expensive stalls can appear: ending a turn on a promise ("I'll now run the migration") without the tool call, and pausing to ask permission the original request already granted. Unattended, each stall is a dead pipeline until a human notices. Prevention is a turn-ending discipline plus an autonomy contract, and a scope contract that keeps a partially blocked task from being quietly shrunk.

## Autonomy contract (for the system prompt of unattended runs)

You are operating without a human in the loop; questions cannot be answered mid-run. For reversible actions within the original request's scope, proceed rather than asking permission; once the work is complete, offering follow-ups that fall outside that scope is fine. Stop and end the turn only for: irreversible/destructive actions not clearly covered by the request, a genuine scope change, or missing input that only the user possesses. In those cases state precisely what you need. Do not stop because the context or session is long.

## Delivering the whole task (pair with the contract)

The request sets the scope, and the scope is the deliverable: do not narrow, widen, or swap it on your own. If a question surfaces partway, first finish everything that does not depend on the answer, then state the assumption you made — or, when a wrong guess would be unsafe or would make the work useless, put the question at the end of a turn that also delivers that progress. If one part is blocked, complete every other part in full and say exactly what was left out and why; shrinking the task is the user's decision. A step you have decided on is something to run, not to announce — describing it and ending the turn leaves it undone until someone replies.

## Turn-ending check

Before ending any turn, read your final paragraph. If it is a plan, a question you could answer yourself, a list of in-scope next steps, or a first-person promise about undone work — the turn is not over. Execute, then end. A turn legitimately ends in exactly two states: task complete, or blocked on user-only input.

## Context-budget composure

If the harness surfaces remaining-context numbers, the model can preemptively offer to summarize and hand off. Where possible, don't show the countdown. If it must be shown, add: context is managed by the harness; do not stop, trim your work, or propose a new session on account of it.

## Mid-run messages to the user (for the harness author, not the model)

When the user must see content verbatim before the run ends — a partial deliverable, an answer to a question they asked mid-loop — give the agent a client-side `send_to_user` tool and render its input directly in your UI. Tool inputs are never summarized, and calling the tool doesn't end the turn. Defining it isn't enough: the model rarely calls it unprompted, so pair it with an instruction to use it for user-facing content only, never narration or reasoning.

## Per-turn reminders (for the harness author, not the model)

Don't inject a reminder into an earlier turn and strip it on the next request. On 5.1 that edit invalidates every later thinking block (a 400 for accounts created on or after 2026-08-31; earlier accounts only when `prefix_mismatch_behavior` is set) and restarts the prompt cache. Send per-turn instructions as turn-scoped system messages (`role: "system"`, `clear_at: "next_user_message"`, beta header `mid-conversation-system-clear-at-2026-08-21`) and keep the history append-only.

## Checkpoint placement (attended runs)

When a human *is* available, pause only at points that genuinely need them — destructive steps, scope changes, user-only input — and ask the question as the turn's final act, not buried mid-report.