autonomize

autonomize is a skill for Claude Code, Codex from AcKeskin/contexture. It costs 93 tokens per session (3,228 once invoked), scanned A, original, MIT.

A setting for how much effort the agent should make, when it should stop, and when it should ask you for input.

In plain words
What is it for?
Use it to set a default or task-specific working style that other workflow commands can follow.
Why use it?
It reduces unnecessary questions and avoids work stopping too early or continuing beyond what you want.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: mentions subagents; names the AskUserQuestion tool.

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/ackeskin/contexture/autonomize
Any agent
npx skills add AcKeskin/contexture --skill autonomize
Clone the repo
git clone --depth 1 https://github.com/AcKeskin/contexture

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for autonomize

README.md
[![agentmods](https://agentmods.dev/badge/skills/ackeskin/contexture/autonomize.svg)](https://agentmods.dev/skills/ackeskin/contexture/autonomize)
Your own site
<a href="https://agentmods.dev/skills/ackeskin/contexture/autonomize"><img src="https://agentmods.dev/badge/skills/ackeskin/contexture/autonomize.svg" alt="Measured on agentmods" height="20"></a>
Per session 93 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,228 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00093 $0.03228
Opus 5 $0.00046 $0.01614
Sonnet 5 $0.00019 $0.00646
Haiku 4.5 $0.00009 $0.00323

Measured 2d ago against content hash 2ee8c4ca0576, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-06, from the pricing page.

Security

Grade A, and why

autonomize scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/autonomize/SKILL.md · 165 lines

How it starts

The opening of the file, as written. The whole thing — 165 lines — stays where its author put it; the contents beside it link to each section on GitHub.

autonomize

The goal-directed autonomy organ. It owns one contracthow hard to push, when to stop, when to ask — and is the single writer of it. The existing workflow organs are readers of that contract at their decision points: one owner, many readers (the retrospect-core shape). The contract is leverage (set calibration once, the whole workflow shares it), not ceremony.

The two problems it solves:

  1. Grounding-before-asking — the model re-asks mid-task what the front-loaded spec/example/checklist already answers. The contract's ask posture rides the rule-prime hook as a recall-before-ask reminder: scan the handed-over artefact and state why the answer is not derivable before interrupting.
  2. Effort / stopping calibration — the model under- or over-builds, forcing "keep going" / "leave it here, perfect next session". The contract's effort + stopping fields are the dial the organs read to calibrate.
/autonomize → .claude/autonomy/active.md → read by execute / checkpoint / orchestrate / dispatch / coordinate / wrap / the rule-prime hook

The contract object

One object, three fields, persisted at .claude/autonomy/active.md (gitignored, session-scoped):

# .claude/autonomy/active.md
effort: balanced        # minimal | balanced | thorough | exhaustive
                        #   depth-of-iteration AND the loop's willingness-to-continue
stopping: criteria-met  # criteria-met | diminishing-returns | budget | user-anytime
                        #   the STOPPING POSTURE — criteria-met DEFERS to the spec's
                        #   done_criteria; it does not redefine "done"
ask: forks-only         # forks-only | every-step | until-blocked
                        #   a POINTER, not a new protocol:
                        #     forks-only   → act-dont-ask (reversible) + ambiguity-depth
                        #     every-step   → confirm each consequential step
                        #     until-blocked → run until genuinely blocked or irreversible

Read the full file on GitHub · 165 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2d ago First seen · 165 lines · 93 tokens per session scan A 2ee8c4ca0576

Subscribe to this mod's changes

autonomize is a skill published in the GitHub repository AcKeskin/contexture (2 stars, last pushed 1mo ago), licensed MIT. It adds 93 tokens to every session and 3,228 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.