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Stack os

Skill Asaf-Dahan/super-skill/examples/stack-os

A portable, AI-native intelligence layer that gives any language model full context, methodology, and operational capability over a defined domain.

Install
npx -y skills add Asaf-Dahan/super-skill --skill stack-os

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.

SKILL.md

3.0 KB, as published. Nobody here has run it

<!-- Note: All names, companies, and projects in this example are fictional and used for instructional purposes only. -->

name: super-skill-stack-os description: > Technical stack domain expert for a solopreneur SaaS operation. Loads full context on infrastructure, hosting, services, tiers, and cross-product dependencies for GreenLedger and PulseLog. Gives any AI agent verified current state before any action. version: "1.0" author: "Tomer Naveh / Raincode Labs" tags:

  • infrastructure
  • hosting
  • technical-stack
  • devops
  • cloud-services

Super Skill -- Stack OS

Before Any Action

Read these files in this order:

  1. CONTEXT.md - who owns this, what domain, what goals
  2. CURRENT_STATE.md - what the domain looks like right now
  3. PENDING.md - what is waiting for user approval

Never skip this sequence. Context before action, always.

Operating Principle

The model proposes. The user decides. The Super Skill records. The system executes.

Write all proposals and evaluations to PENDING.md. Do not modify any layer file without explicit user approval.

Domain Scope

Products: GreenLedger (sustainability reporting SaaS), PulseLog (uptime and incident logging dashboard) Shared services: DBHost, CDNLayer, GitHub, Anthropic API GreenLedger-specific: UIBuilder, Stripe, Resend PulseLog-specific: AppHost, Streamlit (admin), Python backend

Domain Files

After reading the three required files above, load the remaining layer files as needed for the current task: DOMAIN_MAP.md - structure and sub-domain relationships EVALUATION.md - criteria for evaluating new entrants DECISIONS.md - decisions made and reasoning MONITORING.md - sources to watch for drift LEARNING.md - NotebookLM integration structure traces/ - execution traces for causal reasoning

Evidence Routing

Before proposing any change, load the files most likely to contain relevant evidence for your task type.

Task typeEvidence files (load in order)
Evaluate a new tool/methodEVALUATION.md, CURRENT_STATE.md, DECISIONS.md
Propose architecture changeDECISIONS.md, DOMAIN_MAP.md, CURRENT_STATE.md
Investigate drift or breakageMONITORING.md, CURRENT_STATE.md, traces/
Resolve a PENDING itemPENDING.md, DECISIONS.md, EVALUATION.md
Generate learning contentLEARNING.md, CONTEXT.md, CURRENT_STATE.md
Root-cause analysistraces/, DECISIONS.md, LOG.md
Cross-domain impact checkDOMAIN_MAP.md, CURRENT_STATE.md, DECISIONS.md

Load the minimum set. Do not load files not listed for your task type.

Learning Generation

To generate learning content from this Super Skill: python scripts/generate_learning.py audio python scripts/generate_learning.py quiz python scripts/generate_learning.py mindmap

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.