Agent skill

prompt-tuner

Improve embedded LLM system prompt based on evaluation test failures

View SKILL.md on GitHub Repository
Stars 163
Forks 31

Install this agent skill to your Project

npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/data/prompt-tuner

SKILL.md

Prompt Tuner

Iteratively improve the embedded backend's system prompt to increase command generation accuracy.

When to Use

  • "tune the prompt"
  • "improve embedded accuracy"
  • "fix LLM command generation"
  • "run prompt tuning cycle"
  • After evaluation tests show low accuracy

Workflow

Phase 1: Baseline Measurement

Run evaluation tests with embedded backend:

bash
./target/release/caro test --backend embedded

Record:

  • Overall accuracy percentage
  • Category breakdown (Website Claim, Natural Variant, Edge Case)
  • List of failed test cases with expected vs actual commands

Phase 2: Failure Analysis

For each failed test case, identify the pattern:

Pattern Example Fix
Wrong path find / instead of find . Add rule: "ALWAYS use current directory '.'"
GNU flags --max-depth on macOS Add rule: "Use BSD-compatible flags"
Missing filters No -name "*.py" Add rule: "Include ALL relevant filters"
Time semantics -mtime -1 vs -mtime 1 Add clear mtime documentation
Quote style Single vs double quotes Usually equivalent, low priority
Flag order -type f -name vs -name -type f Usually equivalent, low priority

Phase 3: Prompt Improvement

Edit the system prompt in:

src/backends/embedded/embedded_backend.rs

Function: create_system_prompt()

Improvement strategies:

  1. Add explicit rules for common failure patterns
  2. Add concrete examples matching test cases
  3. Use numbered rules with clear priorities
  4. Keep prompt concise (LLMs perform worse with verbose prompts)

Phase 4: Verification

Build and re-run tests:

bash
cargo build --release
./target/release/caro test --backend embedded

Compare results:

  • Did overall accuracy improve?
  • Which categories improved/regressed?
  • Are remaining failures semantic equivalents (flag order, quotes)?

Phase 5: Iterate or Commit

If accuracy improved significantly:

bash
git add src/backends/embedded/embedded_backend.rs
git commit -m "feat(prompt): Improve embedded backend accuracy from X% to Y%"

If not improved or regressed:

  • Revert changes
  • Try different approach
  • Consider if test expectations need adjustment

Success Metrics

Level Accuracy Action
Poor < 50% Major prompt rewrite needed
Acceptable 50-70% Targeted improvements
Good 70-85% Minor tuning
Excellent > 85% Consider semantic equivalence in remaining failures

Tips

  1. One change at a time: Make small, focused improvements
  2. Check equivalents: Some "failures" are functionally equivalent commands
  3. Test edge cases: Simple prompts often break on edge cases
  4. BSD vs GNU: macOS uses BSD utilities, not GNU coreutils
  5. Keep examples minimal: Too many examples can confuse small models

Example Session

User: /prompt-tuner

Didn't find tool you were looking for?

Be as detailed as possible for better results