Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add alonbaron/claude-skills --skill prompt-generatorgit clone --depth 1 https://github.com/alonbaron/claude-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/alonbaron/claude-skills/prompt-generator)<a href="https://agentmods.dev/skills/alonbaron/claude-skills/prompt-generator"><img src="https://agentmods.dev/badge/skills/alonbaron/claude-skills/prompt-generator/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/alonbaron/claude-skills/prompt-generator"><img src="https://agentmods.dev/badge/skills/alonbaron/claude-skills/prompt-generator.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00109 | $0.01097 |
| Opus 5 | $0.00055 | $0.00549 |
| Sonnet 5 | $0.00022 | $0.00219 |
| Haiku 4.5 | $0.00011 | $0.00110 |
Grade A, and why
prompt-generator scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 104 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prompt Generator
Produce the prompt the user should have written. A good prompt is precise, grounded, and short — every token earns its place. You output a prompt, not an essay about prompting.
Proactive use
If the user hands over text another agent or LLM will run — a rough prompt, a task spec, a "have it do X" — invoke this without being asked: announce in one line ("Tightening this into a rigorous prompt") and proceed. Never ask permission to run the skill.
Process
- Read the intent. What outcome does the user actually want, and who runs the prompt (a coding agent, a chat model, a one-shot task)?
- Ask only if blocked. At most 1–2 questions, and only when a missing fact would change the prompt's structure. Otherwise proceed and list assumptions in one line.
- Draft using the section menu — include only the sections the task needs (a one-shot classifier doesn't need "When blocked").
- Self-check against the rubric below. Cut anything that doesn't change the model's behavior.
- Output the finished prompt in a fenced block, then ≤3 lines on key choices and what to tune.
Section menu (use what the task needs)
- Role — who the model is and its single objective.
- Context / Inputs — what it's given; mark placeholders as
{{like_this}}. - Ground truth — the paths, versions, commands, and names you have actually verified, stated as facts the agent must check rather than invent, with an explicit "correct me if any of these is wrong". A coding-agent prompt without this section is where hallucinated file paths come from.
- Rules — the grounding + efficiency rules below, plus task-specific ones.
- Steps — ordered, only if the task is genuinely multi-step.
- Output format — exact shape; lead with the answer.
- When blocked — what to do on ambiguity or missing data.
- Done when — testable success criteria.
Rules every generated prompt must carry
Grounding (no hallucination):
- Use only what's given or verifiably checked. Never invent file paths, APIs, function names, numbers, or citations.
- Unknown? Say "I don't know" or state the assumption — don't fill the gap with a plausible guess.
- Separate fact from inference. Claims that need checking are flagged, not asserted.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 104 lines · 109 tokens per session scan A de4884e2882b
prompt-generator is a skill published in the GitHub repository alonbaron/claude-skills (13 stars, last pushed 1mo ago), licensed MIT. It adds 109 tokens to every session and 1,097 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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