Skip to content
Agentmart
Submit a tool

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…

Skill by kpab

IndexedMIT★ 19
Write a review

Chalk Labs

Need help deploying autonomous-continuation?

Chalk Labs sets up agents like this inside real teams: connected to your tools, tested on your data, with a human in the loop where it matters.

Book a call

Install

Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.

npx agentmarket install skill/kpab-autonomous-continuation

Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.

Get the weekly diff

The weekly diff: one email a week with new agent tools, notable updates and grade changes, plus security alerts for tools you follow. Double opt-in, one-click unsubscribe.

We use your email only for this newsletter. Privacy

About

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".

Use cases: devops, productivity

No reviews yet. Installed it? Reviews come from verified installers only. How reviews work

Permissions

What this skill can do and what it asks for, from its SKILL.md and code. Checked Oct 26, 2026.

Tools
No tools found
Secrets
None asked for
Install scripts
None found

Network hosts it talks to 1

  • codeload.github.com · found in install command

Files it touches

  • ~/.claude/skills/autonomous-continuation

What's inside

Skill name
autonomous-continuation
Description
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".
License
MIT
Instructions
33 lines in SKILL.md · view on GitHub

Files (1)

  • SKILL.md

SKILL.md (first lines)

# 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.

From kpab/claude-fable-5-skills, MIT.

More from kpab/claude-fable-5-skills

Signals

Stars
19
Forks
2
Open issues
0
Last commit (this folder)
Sep 2, 2026 (1 month ago)

From GitHub (fetched Oct 24, 2026).

Details

License
MIT
First seen
Oct 24, 2026
Last checked
Oct 24, 2026

More from kpab

Done for you

Rather not set it up yourself?

Chalk Labs scopes, builds and runs agents for your team: the task audit, the eval harness, the integrations and the weekly ops report. Fixed scope, priced before we start.

See how we build AI agents →Read the case studies

Chalk Labs is a two-founder AI and growth studio. We build AI agents, CRMs and MVPs for Web3 and AI companies, and have shipped 50+ product and token launches.

launches shipped
50+
launches shipped
media placements
500+
media placements
founders on every account
2
founders on every account

Press we've placed: Forbes · CoinDesk · Decrypt · Cointelegraph · The Block

Rahil Jain, Co-founder, Chalk Labs. Ex-Goldman Sachs engineer. You talk to the person who builds it.

Get autonomous-continuation running in your business

Tell us what you want to automate. A founder replies within one working day with a plan, not a pitch deck.

Goes to the Chalk Labs founders, not the newsletter. Privacy

Need help deploying autonomous-continuation?

CallGet help
Cite this page

autonomous-continuation skill. Agent Mart. https://agentmart.dev/skills/kpab-autonomous-continuation (last updated 2026-10-24)