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Ultra-Sharp Agent Skills — a research-first skill-authoring system + 72 production-ready skills for AI agents.

Python 100%

# name: writing-tutorials description: Writes step-by-step tutorials and how-to guides where every step has a verifiable checkpoint, full commands, and expected output. Use when the user asks to write a tutorial, a how-to guide, a getting-started guide, a walkthrough, or onboarding steps for learners. Do not use for reference documentation, conceptual overviews and READMEs, or API endpoint docs — separate skills cover those.

# Writing Tutorials

# When to use / when NOT to use

  • Use for: tutorials, how-to guides, getting-started walkthroughs, workshop material — content a learner follows top to bottom.
  • Do NOT use for: reference docs (writing-api-documentation), conceptual overviews/READMEs (writing-technical-documentation), or blog posts (writing-blog-posts).

# House rules

  1. State the destination upfront: end result, prerequisites (with versions), and honest time estimate — before step 1.

    • ✅ "By the end you'll have a deployed webhook receiver. Prerequisites: Python 3.12, an ngrok account. Time: ~20 minutes."
    • ❌ Diving into mkdir project with no destination stated.
  2. One path only. No forks, options, or "alternatively…" mid-tutorial; link alternatives at the end.

    • ✅ "We'll use SQLite. (Using Postgres instead? See the appendix link at the end.)"
    • ❌ "You can use SQLite, Postgres, or MySQL here — configure accordingly."
  3. Every step ends with a verifiable checkpoint.

    • ✅ "Run curl localhost:8000/health — you should see {"status":"ok"}."
    • ❌ Three file edits in a row with no way to know they worked.
  4. Full commands, full expected output. Never cd <your-project>; never truncate output the learner must compare against.

    • ✅ The exact command plus the exact lines it prints.
    • ❌ "Run the usual migration commands."
  5. Never skip steps that "everyone knows." Activating the virtualenv, exporting the variable, saving the file — write them.

    • ✅ "Save the file, then in the same terminal run…"
    • ❌ "Simply configure your environment."
  6. Troubleshoot the 3 most likely failures inline, at the step where they occur — not in a distant appendix.

    • ✅ "If you see Address already in use, another process holds port 8000: run lsof -i :8000…"
    • ❌ A generic "Troubleshooting" section listing every possible error.
  7. End with "what you built + where to go next": recap the result and give 2–3 concrete next links.

# Workflow

  1. Do the task yourself start to finish; record every command, output, and mistake you hit (mistakes become inline troubleshooting).
  2. Write the header: end result, prerequisites with versions, time estimate.
  3. Convert your recording into numbered steps, each with command → expected output → checkpoint.
  4. Add inline troubleshooting for the 3 most likely failures at the exact steps they occur.
  5. Validate: replay the tutorial verbatim in a clean environment (fresh directory/venv/container); any deviation between the doc and reality is a bug — fix it and replay until it runs clean.

# Edge cases

  • Can't provide a clean environment for replay → state the tested environment explicitly ("Tested on macOS 15, Python 3.12.4") and flag untested paths.
  • Long tutorial (over ~15 steps) → split into parts, each part ending at a working state a learner can stop at.
  • Steps involving paid/external services → say the cost and offer the free-tier route as the single main path.

# References

Extended before/after examples: see references/examples.md.