Skip to main content

autocontext-consumer

Use when an agent needs to USE knowledge Autocontext already produced - find which scenarios have knowledge, read the playbook and lessons for one, understand the on-disk file and folder layout, and move knowledge between checkouts. Host-agnostic; requires only the autoctx CLI and the filesystem.

소스 정보

저장소
greyhaven-ai/autocontext
최근 소스 활동
2026년 8월 13일 19:30
감지된 SKILL.md 언어
영어
스타
1,309
포크
115

설치 방법

기본적으로 소스를 먼저 확인하는 Prompt가 선택됩니다. 직접 명령으로 전환하거나 로컬 사본을 다운로드할 수도 있습니다.

소스 파일 검토

설치 여부를 결정하기 전에 SKILL.md와 SkillsMP에 표시된 보조 파일을 읽어 보세요.

SKILL.md 표시 중

SKILL.md
소스 지침 · 읽기 전용 미리보기
name
autocontext-consumer
description
Use when an agent needs to USE knowledge Autocontext already produced - find which scenarios have knowledge, read the playbook and lessons for one, understand the on-disk file and folder layout, and move knowledge between checkouts. Host-agnostic; requires only the autoctx CLI and the filesystem.
version
1.0.0
author
Autocontext
license
Apache-2.0
# Autocontext: Using Existing Knowledge ## Overview Autocontext writes what it learns to a knowledge directory. This skill covers reading and moving that knowledge. To *produce* it, use `autocontext-creator`. Nothing here assumes a particular agent host, and most of it is plain file reading - the layout is documented below precisely so an agent can go straight to the file it wants. ## When to Use - You want to know whether Autocontext has learned anything about a task. - You want the current playbook or lessons for a scenario. - You want to move knowledge from one checkout or machine to another. Do not use this skill to run scenarios or judge output. That is `autocontext-creator`. ## Where Knowledge Lives The root defaults to `./knowledge` and moves with `AUTOCONTEXT_KNOWLEDGE_ROOT`. Inside it, each scenario owns a directory: ``` <knowledge_root>/ <scenario>/ playbook.md the current approach, rewritten as the loop learns lessons.json accumulated lessons, newest last hints.md hints carried into the next attempt mutation_log.jsonl one line per change, append-only package_metadata.json present once the scenario has been exported reports/<run_id>.md per-run written reports analytics/ cross-scenario analytics _hub/ shared research hub state _evaluator_epochs/ evaluator versioning ``` Directories starting with `_` are shared across scenarios rather than owned by one. `playbook.md` is the file to read first: it is the current answer, where `lessons.json` is the history of how it got there. ## Reading Knowledge The playbook and lessons are plain files. Read them directly: ```bash cat "${AUTOCONTEXT_KNOWLEDGE_ROOT:-knowledge}/grid_ctf/playbook.md" ``` An absent file means nothing has been learned for that scenario yet. That is a normal state, not an error. ## Finding Runs ```bash autoctx list --json autoctx status "$RUN_ID" --json autoctx show "$RUN_ID" ``` `list` is the entry point when you do not know what exists. ## Moving Knowledge Between Checkouts Export a scenario's knowledge as a portable package: ```bash autoctx export --scenario grid_ctf --output grid_ctf_package.json --json ``` Import one somewhere else. The package file is a positional argument, not a flag, and it is required: ```bash autoctx import-package grid_ctf_package.json --json ``` `--conflict` decides what happens when the target scenario already has knowledge: `overwrite`, `merge`, or `skip`. `--scenario` imports under a different name than the package was exported from. Use these rather than copying the directory by hand: the package carries the metadata that makes the knowledge legible on the far side. ## Reading a Generation in Detail When a result is surprising, the generation JSON is the level that explains it: ```bash autoctx replay "$RUN_ID" --generation 1 ``` ## What Not to Assume - **Knowledge is scenario-scoped.** A playbook for one scenario says nothing about another. - **`lessons.json` is append-only.** The last entries are the newest; do not assume the file is ordered by importance. - **An empty playbook is meaningful.** It means the loop has not yet found an approach worth keeping, which is different from the scenario not existing.
GitHub에서 보기