agentleFS
TheAstroByteTheAstroByte/astrobyte-agent-skills/skills/setup-astrobyte-skills/SKILL.md

setup-astrobyte-skills

Skill1 starsChanged 13 days agoMITView on GitHub

Install the shared Astrobyte profile without onboarding, or configure a personalized profile when requested. Invoke explicitly to install or change this stack on a machine.

The file

We split this skill into the rules it gives an agent

2 rules. About 0 are common, 0 are distinctive, and 2 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈0

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 2

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    Run python3 scripts/install.py --operator <handle> --interview in an interactive terminal.

    line 15
  2. Very specific:

    Run python3 scripts/validate.py and python3 -m unittest discover -s tests -v from the repository.

    line 18

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Setup Astrobyte skills

Default to the shared Astrobyte profile without onboarding. Follow skills/adopt-skill-stack/references/quick-install.md in the source checkout, or references/quick-install.md in the installed adopt-skill-stack folder. Resolve the source URL supplied by the user if neither is available. Continue below only when the user explicitly requests a machine setup interview or personalization. Personalization records differences from the baseline; installation does not require creating a new profile or private repository.

  1. Locate the repository root containing config/repository.json, catalog.json, and scripts/install.py.
  2. Resolve the committed operator handle or approved baseline delta. Use the existing Astrobyte baseline when no personal profile exists. For requested local-only personalization, follow adopt-skill-stack's delta path; use this machine interview only when machine-specific setup was requested.
  3. Run python3 scripts/install.py --operator <handle> --interview in an interactive terminal. Relay every question to the user when the agent product cannot give the script direct terminal input.
  4. Use detected agent products as evidence, not as consent. Confirm active products, preferred product, machine alias, delivery default, autonomy, skill profile, model environment, reviewer model, parallelism, unattended work, interactive and unattended external-action policies, Git workflow, human and per-product Git identities, tracker, docs path, compute, experiment budget, browser tools, and containers. When the working agreement names no reviewer, ask once which model to use and offer to record the answer there.
  5. Present the script's plan. Apply it only after the operator confirms.
  6. Run python3 scripts/validate.py and python3 -m unittest discover -s tests -v from the repository.
  7. Report the installed profile, machine alias, delivery default, destinations, routing locations, config path, backups, and manual follow-up, such as the Cursor Cloud User Rule in docs/routing.md.

The setup is complete when python3 scripts/install.py --check passes and each selected harness can discover astrobyte-mode.

Preserve unrelated skills and machine configuration. Treat credentials, private repository access, and harness permission changes as separate user-authorized actions.

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