objective-autorun

Drives an Objective through repeated parent-judged implementation steps in Git-based autorun workflows.

2|Updated Apr 2, 2026
One-click install
npx skills add https://github.com/nseng-ai/ns --skill objective-autorun-nseng-ai
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: objective-autorun
Source: https://github.com/nseng-ai/ns/tree/main/skills/incubating/objectives/objective-autorun
Command: npx skills add https://github.com/nseng-ai/ns --skill objective-autorun-nseng-ai

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve? Implementing a multi-step Objective by hand requires repeatedly deciding what slice to build next, dispatching work, verifying results, and keeping commits and tracking honest. This Skill automates that loop: it selects an Objective, previews the run, dispatches one implementation step at a time, and judges each result against real repository evidence before accepting it. ## Core Features & Use Cases - Dual execution modes: ns-bookended mode uses runner-begin/runner-finish for runner-attested checkpoints, while portable mode works with plain Git and parent-verified ordinary commits. - Parent-judged step loop: each step gets thin guidance, exactly one implementation dispatch, and evidence-based accept/recover/stop judgment, with a hard step-budget ceiling. - Honest run reporting: finishes with a structured Autorun digest separating runner-attested facts, parent-verified evidence, child claims, tracking commits, and publication outcomes. - Use Case: You have an Objective recorded under .ns/objectives/ with a roadmap. Ask the agent to autorun it for 3 steps; it previews the branch topology and verification authority, implements each slice, verifies the diff itself, commits accepted work on a dedicated run branch, and ends with a digest of what changed. ## Quick Start Ask the agent to autorun the Objective at .ns/objectives/my-feature for up to three steps and confirm the launch preview it shows you.

Frequently Asked Questions about objective-autorun

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I autorun an Objective across multiple implementation steps?

Select the Objective by slug or path, confirm the launch preview showing mode, branch topology, and step budget, then the skill dispatches one implementation step at a time. Each step is judged against actual repository evidence before the next begins, and the run ends with a structured digest.

What is the difference between ns-bookended and portable mode?

ns-bookended mode uses runner-begin and runner-finish so accepted steps end in runner-attested Runner Checkpoints with provenance commits. Portable mode uses ordinary Git: the child leaves changes uncommitted and the parent verifies the diff and creates one ordinary local commit per accepted slice.

Does objective-autorun require the ns CLI to work?

No. Portable mode requires only Git and checkout-local Objective Markdown records. The skill probes runner-begin and runner-finish once; if either probe fails it selects portable mode, and ns-bookended is only used when both commands succeed.

Can the autorun skill push, submit, or create pull requests?

No. Implementation children are forbidden from push, submit, publish, merge, PR creation, deploy, or any external write. Publication is off by default in ns-bookended mode and unavailable in portable mode; any later push or PR is a separate explicitly requested workflow.

What happens when an implementation step fails verification?

A repairable failure gets one fresh recovery attempt with sharpened guidance, leaving the dirty tree visible. Ambiguous, unsafe, or repeated failures stop the run and ask the user; the skill never auto-resets or discards uncertain work.

When should I not use objective-autorun?

Avoid it when the Objective record is missing, closed, ambiguous, or lacks enough scope for one coherent slice, or when you need immediate external publication. It is designed for local, judged, incremental implementation rather than one-shot code generation.