agentleFS
nklischnklisch/claude-code-modes/.claude/skills/prompt-author/SKILL.md

prompt-author

Skill116 starsChanged 7 months agoMITView on GitHub

Interactively create custom bases, modifiers, or axis values for claude-code-modes. Guides prompt design using Anthropic emotion research findings (calm vectors, avoiding desperation triggers, positive framing). Produces markdown fragments and base.json manifests. Use when user says "create a base", "write a modifier", "new axis value", "custom prompt", or invokes /prompt-author.

1 other project uses a version of this.

Adopted by

Most recently taken up by

  • Other versions1 project uses a version of this.

    Word for word · 0

    None found.

    Edited · 0

    None found.

    Wrote their own version · 1

    1. sysetup/skills★ 1same name

    How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.

  • In this repository6 other files it gives its agents.

The file

Show its rules and text

We split this skill into the rules it gives an agent

17 rules. About 0 are common, 8 are distinctive, and 9 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈8

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 9

    Very specific

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

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

    Create custom prompt content for claude-code-modes — bases, modifiers, or axis values — grounded in Anthropic's emotion research.

    line 9
  2. Distinctive:

    Read the existing prompts to understand the current patterns:

    line 19
  3. Distinctive:

    Ask the user conversationally (not all at once):

    line 34
  4. Distinctive:

    When would someone use it?

    line 45
  5. Distinctive:

    Zero ALL-CAPS emphasis: No IMPORTANT, CRITICAL, MUST, NEVER.

    line 62
  6. Distinctive:

    Activate calm vectors: Use assured, direct language.

    line 64
  7. Distinctive:

    Avoid desperation triggers: No stacked contradictory requirements.

    line 66
  8. Very specific:

    Include worked examples: Use <example> tags for complex behaviors.

    line 72
  9. Very specific:

    Write a base.json manifest (flat JSON array with "axes" and "modifiers" reserved words)

    line 82
  10. Distinctive:

    Keep under 200 words. Modifiers are behavioral nudges, not full rewrites.

    line 89
  11. Distinctive:

    Use the same calm, positive framing as base content.

    line 90
  12. Very specific:

    Start with a heading (# Modifier Name).

    line 91
  13. Very specific:

    For a base: Create the directory, write base.json and all fragment files.

    line 107
  14. Very specific:

    For a modifier: Write the .md file to prompts/modifiers/.

    line 108
  15. Very specific:

    For an axis value: Write the .md file to prompts/axis/{axis}/.

    line 109
  16. Very specific:

    Run bun scripts/generate-prompts.ts if the content should be embedded (built-in bases/modifiers)

    line 112
  17. Very specific:

    Test with claude-mode create --base <name> --print or claude-mode create --modifier <name> --print

    line 114

≈ “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

Prompt Author

Create custom prompt content for claude-code-modes — bases, modifiers, or axis values — grounded in Anthropic's emotion research.

Before starting

Read these references:

  1. references/emotion-research.md — emotion vector findings and principles
  2. references/prompt-quality.md — 10 quality criteria
  3. references/base-format.md — manifest and fragment specs
  4. references/examples.md — chill base as worked example

Read the existing prompts to understand the current patterns:

  • prompts/base/ — standard base fragments
  • prompts/chill/ — chill base fragments (emotion-research-informed)
  • prompts/modifiers/ — existing modifiers
  • prompts/axis/ — existing axis values

Phase 1: Determine what to create

AskUserQuestion: What type of prompt content?

  • Base — a complete foundational prompt (directory + manifest + fragments)
  • Modifier — a behavioral layer that composes on top of any base/preset
  • Axis value — a custom value for agency, quality, or scope

Phase 2: Interview about desired behavior

Ask the user conversationally (not all at once):

For a base:

  • What's the philosophy? (e.g., minimal, domain-specific, team-flavored)
  • What behaviors matter most?
  • What tone? (direct, warm, formal, casual)
  • How does it differ from standard and chill?
  • What fragments should it have? (consolidate like chill, or granular like standard?)

For a modifier:

  • What behavior does this shape? (investigation, craftsmanship, caution, speed)
  • When would someone use it? (debugging, reviewing, prototyping)
  • What should the agent do differently with this modifier active?
  • What should it explicitly NOT do?

For an axis value:

  • Which axis? (agency, quality, scope)
  • What behavior does this value represent?
  • How does it differ from the existing built-in values?

Phase 3: Draft the content

Apply these principles from the emotion research (see references/emotion-research.md):

Writing rules

  1. Positive framing first. State what to do, not what to avoid. "Keep changes scoped to the request" not "Don't add features beyond what was asked."
  2. Zero ALL-CAPS emphasis. No IMPORTANT, CRITICAL, MUST, NEVER. State instructions with calm confidence.
  3. Activate calm vectors. Use assured, direct language. "Consider the impact" not "you MUST check with the user FIRST."
  4. Avoid desperation triggers. No stacked contradictory requirements. No pressure language. No impossible constraints.
  5. Counter post-training brooding. Encourage leading with answers. Directness over qualifications.
  6. Preserve protective caution. Reframe as professional judgment, not anxiety. "Actions that warrant consideration" not "NEVER do this without asking."
  7. Include worked examples. Use <example> tags for complex behaviors. Show good vs bad. 2-3 examples for core instructions.
  8. Add a priority hierarchy (for bases). One line near the top: "When guidelines conflict: safety first, then user instructions, then correctness, then style."
  9. Security boundaries stay firm. Calm tone, but no ambiguity on injection prevention, auth testing limits, URL restrictions.
  10. Keep it lean. Target 40-70% the token count of the standard base. Every instruction should earn its place.

For bases specifically

  • Write a base.json manifest (flat JSON array with "axes" and "modifiers" reserved words)
  • Decide fragment structure — consolidate like chill (fewer, bigger fragments) or granular like standard
  • Include env.md with all template variables (see references/base-format.md)
  • Actions fragment should be behaviorally neutral — the agency axis handles the autonomous-vs-cautious split

For modifiers specifically

  • Keep under 200 words. Modifiers are behavioral nudges, not full rewrites.
  • Use the same calm, positive framing as base content.
  • Start with a heading (# Modifier Name).
  • Focus on one behavioral dimension — don't try to cover everything.

Phase 4: Review with user

Present the drafted content and ask for feedback. Highlight:

  • Token count compared to standard/chill equivalents
  • Number of negative instructions (target: < 5)
  • Number of ALL-CAPS markers (target: 0)
  • Number of worked examples
  • Whether security boundaries are preserved

Iterate until the user approves.

Phase 5: Write files

For a base: Create the directory, write base.json and all fragment files. For a modifier: Write the .md file to prompts/modifiers/. For an axis value: Write the .md file to prompts/axis/{axis}/.

Remind the user to:

  1. Run bun scripts/generate-prompts.ts if the content should be embedded (built-in bases/modifiers)
  2. Register custom content in .claude-mode.json if it's project-specific
  3. Test with claude-mode create --base <name> --print or claude-mode create --modifier <name> --print

Quality checklist

Before finalizing, verify:

  • Negative instruction count < 5 per 1000 characters
  • Zero ALL-CAPS emphasis markers
  • Security boundaries present and unambiguous
  • At least 2 worked examples (for bases)
  • Priority hierarchy present (for bases)
  • All template variables present in env.md (for bases)
  • Manifest has both "axes" and "modifiers" entries (for bases)
  • Content is shorter than the standard base equivalent

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.