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By tyroneross
Classify, diagnose, rewrite, and score prompts by model tier and deployment
npx claudepluginhub tyroneross/rosslabs-ai-toolkit --plugin prompt-builderA/B compare two versions of a prompt with dimensional scoring and regression detection
List all prompts saved in the project-local library with their latest scores
Optimize a raw prompt for a target model tier and deployment context
Main prompt-builder entry. Dispatches to a subcommand based on your request, or lists options if unclear. Use `prompt-builder:<subcommand>` to target a specific action directly.
Persist an optimized prompt to the project-local prompt library
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A Claude Code plugin that wraps the prompt-builder skill — a Prompt Policy Engine that classifies, diagnoses, rewrites, and scores prompts calibrated to model tier and deployment context.
Designed to be callable by agents, tools, and humans through a shared input/output contract.
Given a raw prompt, prompt-builder returns:
[ASSUMED: ...]) the caller should reviewTEMPERATURE_HINTCalibrates the rewrite to:
Standalone repo with symlink install (matches bookmark/build-loop pattern):
cd ~/.claude/plugins
ln -s ~/dev/git-folder/prompt-builder prompt-builder
Verify:
claude --print "List my installed plugins"
# should include prompt-builder
| Command | Purpose |
|---|---|
/prompt-builder:optimize [prompt or path] | Optimize a prompt end-to-end |
/prompt-builder:score [prompt or path] | Score-only; returns dimensional breakdown + diagnosis |
/prompt-builder:compare <a> <b> | A/B two prompt versions with regression detection |
/prompt-builder:save <id> | Persist the last optimized prompt to the library |
/prompt-builder:list [filter] | Show saved prompts and their latest scores |
Any caller can invoke the skill directly with labeled inputs. The skill returns a structured response per skills/prompt-builder/references/caller-contract.md.
Example invoking prompt (sent by an agent):
Use the `prompt-builder` skill.
raw_prompt: You are a helpful assistant. Analyze the CSV and tell me what's interesting.
model_tier: T2
deployment: plugin
output_consumer: human
risk_level: medium
Example response (abridged):
CONFIG: v1.0 | T2 | plugin | Instructional+Analytical | SCORE: 21/25 [A:4|C:5|Cs:4|D:4|Cp:4]
DIAGNOSIS:
- Ambiguity: "what's interesting" is not a deliverable
- Missing constraints: no null/type handling
- Missing context: no column descriptions, no audience
OPTIMIZED_PROMPT:
...
TEMPERATURE_HINT: 0.2
Full contract: skills/prompt-builder/references/caller-contract.md
Saved prompts live under .prompt-builder/prompts/<id>/ in the consuming project:
.prompt-builder/
├── prompts/
│ ├── summarizer/
│ │ ├── v1.md # The optimized prompt (copy-ready)
│ │ ├── v1.meta.json # tier, deployment, score, assumptions, timestamp
│ │ ├── v2.md
│ │ └── v2.meta.json
│ └── rag-reranker/
│ └── ...
└── memory/
└── lessons.md # Append-only cross-prompt insights (optional)
Versions are never overwritten — /prompt-builder:save always increments.
The plugin ships with a zero-dependency eval runner:
cd ~/dev/git-folder/prompt-builder
node evals/run-evals.mjs # run all cases
node evals/run-evals.mjs reranker-t3 # run one case
node evals/run-evals.mjs --dry # preview invoking prompts without calling claude
The runner spawns claude -p per case, parses the CONFIG line, and asserts on must_contain, must_not_contain, required_sections, and min_score. Exit code is non-zero on any failure.
prompt-builder) — the engine. Progressive disclosure via 4 reference files + 5 worked examples.MIT