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Automation skill forge

Skill Arnie016/codex-prompt-templates/skills/automation-skill-forge

Color-coded Codex prompt templates and Agent Skills for plugin-orchestrated AI coding workflows, MCP safety, repo intelligence, and automation.

Install
npx -y skills add Arnie016/codex-prompt-templates --skill automation-skill-forge

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

  • 1 stars1 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

Turn automation runs into one or more specific Codex skill candidates. Use when an automation should produce reusable skills, stronger prompts, trend workflows, GitHub-ready skill artifacts, or generated-skill naming rules. Triggers: automation to skill, make this into a skill, generated skill pack, many skills from GitHub repos, trend-backed skill, skill naming regex. Skip when the task only needs a one-off fix or a broad brainstorm.

SKILL.md

2.8 KB, as published. Nobody here has run it

Automation Skill Forge

Goal: turn recurring automation work and GitHub/source research into sharp, non-overlapping Codex skill candidates or a clear decision not to create any.

Naming Regex

Generated skills from this automation must use:

^auto-skill-(research|build|review|audit|ops|safety|trend)-[a-z0-9]+(?:-[a-z0-9]+){1,4}$

Format: auto-skill-<mode>-<domain>-<artifact>.

Examples:

  • auto-skill-trend-agentic-disease-intake
  • auto-skill-safety-mcp-secret-guard
  • auto-skill-build-github-repo-radar

Workflow

  1. Read automation memory and the current workspace index first.
  2. Read references/maintenance-automation-scope.md for the full agency model.
  3. Choose a primary aim: research, build, review, audit, ops, safety, or trend.
  4. Gather compact evidence with rg, manifests, README/docs, and exact source files.
  5. For multiple local source clones, use $auto-skill-build-source-pattern-synthesis to cluster workflow patterns before writing skill files.
  6. Create as many useful non-overlapping skills as the evidence justifies.
  7. Move broad catalogs or long examples into references.
  8. Validate each new or changed skill with $skill-quality-gate.

Skill Candidate Contract

Name:
Regex match:
Generated by:
Aim:
Trigger:
Inputs:
Workflow:
Commands:
References:
Skip when:
Validation:
Useful end goal:

Command Defaults

  • Search: rg, rg --files, exact symbols, package manifests.
  • Repo scan: scripts/repo_snapshot.sh <repo> and scripts/token_budget.sh <repo> when available.
  • Validation: scripts/skill_audit.sh plugins/codex-supercharge/skills and quick_validate.py.
  • GitHub packaging: prepare files, docs, validation output, and commit-ready notes only; do not publish unless asked.

Trend Guardrail

Use trend signals to choose domains, not to invent claims. Current trend-backed skill ideas must cite source/date and include a safety or usefulness gate.

Read references/automation-skill-patterns.md and references/maintenance-automation-scope.md before creating generated skills or broad skill batches.

Validation

  • Every generated skill name matches the automation regex unless promoted to a stable first-class name.
  • Each candidate has a trigger, skip condition, workflow, references, and validation.
  • Broad catalogs or examples are moved into references before installation.

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.