prompt-tuner
Improve embedded LLM system prompt based on evaluation test failures
npx skills add majiayu000/claude-skill-registry --skill prompt-tuner --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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 equ
- When to Use
- Workflow
- Phase 1: Baseline Measurement
- Phase 2: Failure Analysis
- Phase 3: Prompt Improvement
- Phase 4: Verification
- Phase 5: Iterate or Commit
- Success Metrics
- Tips
- Example Session
cargo build --release git add src/backends/embedded/embedded_backend.rs git commit -m "feat(prompt): Improve embedded backend accuracy from X% to Y%"
What does the prompt-tuner skill do?
Improve embedded LLM system prompt based on evaluation test failures
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill prompt-tuner --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.