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AI Agent & Automation Workflows

Skill pack
6 skills0 selected
stempeckSkills in this pack

Evaluates a skill library to identify which SKILL.md files would produce valuable autonomous agents when converted via /formula-create. Use when assessing a repository of skills for agent creation candidates, prioritizing which skills to convert to formulas, or auditing an existing agent roster for gaps.

English

Validate the architectural ALTITUDE of a stated root cause or proposed fix before peer review or implementation. Interrogates the frame itself — whether the concern exists because of an abstraction that could be removed — before mapping enforcement sites within the concern. Tests whether a different boundary could DELETE the concern's substrate, not merely reorganize its enforcement. Invoked as a gate from /rootcause-all (after synthesis), /design-v5 (between Phase 3 and Phase 4), and /rootcause-review (at intake). Can also be invoked standalone.

English

Create a new agentfactory formula from a description or SKILL.md file. Generates a properly structured TOML formula with steps, dependencies, inputs, and iteration mechanisms. When given a SKILL.md, preserves phase gates as separate formula steps with enforcement language.

English

Lean, adaptive implementation skill that classifies task complexity and scales process accordingly. Uses native Claude Code sub-agents and checkpoints for speed and token efficiency while maintaining quality gates.

English

Perform scientific peer review of a rootcauseanalysis.md document, validating all claims with independent verification. Appends review findings without modifying original content.

English

Challenges the quality of any completed review or assessment to determine what would achieve six-sigma (99.9999%) quality outcome. Works with scored reviews (X/10), qualitative reviews (PASS/FAIL), or unstructured assessments. Identifies gaps, independently discovers missed issues, stress-tests each proposed improvement for feasibility including logical impossibilities, and appends a "Six-Sigma Caveats" section. Use when a review is less than perfect, when the user asks "what would make this perfect," or after any quality gate completes.

English

Includes Agentic Skill Eval, Architecture Elevation, Formula Create, and other workflows.

Install with
Claude CodeCodexCursorGitHub CopilotGemini CLI

Supported by the Atlas install prompt. Skill-specific requirements vary.

Pack stats

Skills6CategorygeneralStars0

What’s included

Agentic Skill Eval

Evaluates a skill library to identify which SKILL.md files would produce valuable autonomous agents when converted via /formula-create. Use when assessing a repository of skills for agent creation candidates, prioritizing which skills to convert to formulas, or auditing an existing agent roster for gaps.

Architecture Elevation

Validate the architectural ALTITUDE of a stated root cause or proposed fix before peer review or implementation. Interrogates the frame itself — whether the concern exists because of an abstraction that could be removed — before mapping enforcement sites within the concern. Tests whether a different boundary could DELETE the concern's substrate, not merely reorganize its enforcement. Invoked as a gate from /rootcause-all (after synthesis), /design-v5 (between Phase 3 and Phase 4), and /rootcause-review (at intake). Can also be invoked standalone.

Formula Create

Create a new agentfactory formula from a description or SKILL.md file. Generates a properly structured TOML formula with steps, dependencies, inputs, and iteration mechanisms. When given a SKILL.md, preserves phase gates as separate formula steps with enforcement language.

Rapid Implement

Lean, adaptive implementation skill that classifies task complexity and scales process accordingly. Uses native Claude Code sub-agents and checkpoints for speed and token efficiency while maintaining quality gates.

Rootcause Review

Perform scientific peer review of a rootcauseanalysis.md document, validating all claims with independent verification. Appends review findings without modifying original content.

Six Sigma Challenge

Challenges the quality of any completed review or assessment to determine what would achieve six-sigma (99.9999%) quality outcome. Works with scored reviews (X/10), qualitative reviews (PASS/FAIL), or unstructured assessments. Identifies gaps, independently discovers missed issues, stress-tests each proposed improvement for feasibility including logical impossibilities, and appends a "Six-Sigma Caveats" section. Use when a review is less than perfect, when the user asks "what would make this perfect," or after any quality gate completes.

Quick start

  1. Choose your skills. Select individual skills above, or choose the entire pack.
  2. Copy the install prompt. Use Copy install prompt above the list.
  3. Send it to your agent. Paste into your coding agent’s chat. Your agent reviews the source and guides the setup.

Try it with your agent

After installation, start with a request like:

Example request
Use the Agentic Skill Eval skill to help with my task.
Ask me for the context you need before getting started.

Requirements & license

  • A coding agent that can access your project files and install skills.
  • Your agent checks each skill’s instructions, dependencies, and permissions during setup.
  • Reported license for the skills shown: AGPL-3.0. The author’s terms apply.
AI Agent & Automation Workflows | Agent Skills Atlas