autocontext-creator

name	autocontext-creator
description	Use when an agent needs to CREATE knowledge with Autocontext - run a scenario or plain-language task through the improvement loop, judge or improve a single output, and inspect what the run produced. Host-agnostic; requires only the autoctx CLI.
version	1.0.0
author	Autocontext
license	Apache-2.0

Autocontext: Creating Knowledge

Overview

Autocontext runs an improvement loop over a task and writes what it learned to disk. This skill covers producing that knowledge. To read knowledge that already exists, use autocontext-consumer instead.

Nothing here assumes a particular agent host. The only requirement is that you can run autoctx and read its output.

When to Use

• You have a task and want Autocontext to improve an approach to it over several generations.
• You have one output and one rubric, and want it scored or improved without a full loop.
• You want to see what a finished run produced.

Do not use this skill to look up existing knowledge. That is autocontext-consumer.

Always Pass --json When Parsing

Every command below accepts --json. Use it whenever you intend to read the result programmatically; the human-readable form is not a stable interface.

Running a Scenario

autoctx run grid_ctf --iterations 3 --json

--iterations is the number of generations. Each one produces a candidate, scores it, and folds what it learned into the knowledge for that scenario.

Give the run an id you choose when you need to refer back to it:

RUN_ID="my_run_$(date +%s)"
autoctx run grid_ctf --iterations 3 --run-id "$RUN_ID" --json
autoctx status "$RUN_ID" --json

Starting From a Plain-Language Task

When there is no scenario, describe the task:

autoctx solve "Improve the support-triage response policy." --iterations 3 --json

Scoring or Improving a Single Output

For one-shot work, without a loop:

autoctx judge --task-prompt "..." --output "..." --rubric "..." --json
autoctx improve --task-prompt "..." --rubric "..." --rounds 3 --json

judge scores an output you already have. improve iterates on it.

Seeing What a Run Produced

autoctx list --json
autoctx status "$RUN_ID" --json
autoctx show "$RUN_ID"
autoctx replay "$RUN_ID" --generation 1

show renders the run's artifacts. replay prints the JSON for one generation, which is the level to inspect when a score looks wrong.

Watching a Run in Flight

autoctx watch "$RUN_ID"

Creating a New Scenario

autoctx scenario create --list
autoctx scenario create --template content-generation --name support-content

Scaffolds from the template library. Use this when the task recurs and deserves a named scenario rather than a one-off solve.

Choosing a Provider

Autocontext defaults to a hosted Anthropic model. To point it somewhere else, including a local server, set the provider before running:

export AUTOCONTEXT_AGENT_PROVIDER=openai-compatible
export AUTOCONTEXT_AGENT_BASE_URL=http://localhost:11434/v1
export AUTOCONTEXT_AGENT_API_KEY=no-key
export AUTOCONTEXT_LOCAL_MODEL=llama3.1
autoctx run grid_ctf --iterations 3 --json

Keep secrets and base URLs in the environment or the user's profile, never in a skill file.

Before a Long Run

autoctx run preflights every endpoint it will use and refuses to start on a dead endpoint, a rejected credential, or a model the server does not serve. That check is why a misconfigured run fails in seconds rather than after spending tokens. --skip-preflight exists but wastes that protection.

Privacy

Runs write to the local knowledge root and stay there. Nothing is uploaded. Treat run artifacts as you would any local file containing the task text and model output - they contain whatever you put in the prompt.