/prompt-generator
Turns a vague ask into a rigorous, grounded, token-efficient prompt: role + objective, testable done criteria, anti-hallucination (verify or say "I don't know"), anti-tokenmaxing (lead with the answer, output budget), strict agent discipline. Use proactively when the user hands
$ npx -y skills add alonbaron/claude-skills --skill prompt-generator --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.
- You can call itInvoke it directly when you want it.
- Slash command
/prompt-generator
Context preview
The summary Claude sees to decide when to auto-load this skill.
Turns a vague ask into a rigorous, grounded, token-efficient prompt: role + objective, testable done criteria, anti-hallucination (verify or say "I don't know"), anti-tokenmaxing (lead with the answer, output budget), strict agent discipline. Use proactively when the user hands
SKILL.md
prompt-generator.SKILL.mdname: prompt-generator
description: >
Turns a vague ask into a rigorous, grounded, token-efficient prompt: role +
objective, testable done criteria, anti-hallucination (verify or say "I don't
know"), anti-tokenmaxing (lead with the answer, output budget), strict agent
discipline. Use proactively when the user hands over a prompt or task spec
destined for another agent or LLM. Also on "write a prompt", "improve this
prompt", "prompt for an agent". Not for human-facing prose or work you'll
execute yourself this session.
argument-hint: "[task, or a rough prompt to refine]"
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
1. **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)? 2. **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. 3. **Draft** using the section menu — include only the sections the task needs (a one-shot classifier doesn't need "When blocked"). 4. **Self-check** against the rubric below. Cut anything that doesn't change the model's behavior. 5. **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.
**Efficiency (no tokenmaxing):**
- Lead with the answer. No preamble, no restating the question, no "great
question", no summary of what you're about to do.
- Set an output budget (e.g. "≤200 words", "code + ≤3 lines"). Match length to
the task, not to fill space.
- No hedging filler. One clear recommendation beats five caveated options.
**Agent discipline (when the prompt drives a coding/tool agent):**
- Read before you edit; verify a path or symbol exists before referencing it.
- Make small, reversible changes. Don't refactor unasked.
- When blocked or ambiguous, ask — don't guess and barrel ahead.
- Report honestly: if a step failed or was skipped, say so.
- Commits are the user's alone — author = the user; never add an AI co-author or `Co-Authored-By`/credit line, and don't mention AI in commit messages.
When not to use
- Text a human will read (docs, messages, specs for people) — that's writing,
not prompting.
- Work you'll do yourself in this session — just do it; don't write yourself a
prompt.
Hand-offs
- The prompt drives a coding agent onto a repo → bake "run `up-to-date` first"
into its steps.
- The task behind the prompt is a whole build → suggest `architect` for the
design docs instead of one mega-prompt.
Output
The prompt in a fenced block, ready to paste. Then at most three lines: what you assumed, what to tweak, and (if relevant) which rule you emphasized and why. No lecture on prompt engineering.
Done when
The prompt is self-contained, has a testable definition of done, carries the grounding + efficiency rules, and contains nothing that doesn't change the output. If your notes are longer than the prompt, cut the notes.
Read more
name: prompt-generator description: > Turns a vague ask into a rigorous, grounded, token-efficient prompt: role + objective, testable done criteria, anti-hallucination (verify or say "I don't know"), anti-tokenmaxing (lead with the answer, output budget), strict agent discipline. Use proactively when the user hands over a prompt or task spec destined for another agent or LLM. Also on "write a prompt", "improve this prompt", "prompt for an agent". Not for human-facing prose or work you'll execute yourself this session. argument-hint: "[task, or a rough prompt to refine]"
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
1. **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)? 2. **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. 3. **Draft** using the section menu — include only the sections the task needs (a one-shot classifier doesn't need "When blocked"). 4. **Self-check** against the rubric below. Cut anything that doesn't change the model's behavior. 5. **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.
**Efficiency (no tokenmaxing):**
- Lead with the answer. No preamble, no restating the question, no "great
question", no summary of what you're about to do.
- Set an output budget (e.g. "≤200 words", "code + ≤3 lines"). Match length to
the task, not to fill space.
- No hedging filler. One clear recommendation beats five caveated options.
**Agent discipline (when the prompt drives a coding/tool agent):**
- Read before you edit; verify a path or symbol exists before referencing it.
- Make small, reversible changes. Don't refactor unasked.
- When blocked or ambiguous, ask — don't guess and barrel ahead.
- Report honestly: if a step failed or was skipped, say so.
- Commits are the user's alone — author = the user; never add an AI co-author or `Co-Authored-By`/credit line, and don't mention AI in commit messages.
When not to use
- Text a human will read (docs, messages, specs for people) — that's writing,
not prompting.
- Work you'll do yourself in this session — just do it; don't write yourself a
prompt.
Hand-offs
- The prompt drives a coding agent onto a repo → bake "run `up-to-date` first"
into its steps.
- The task behind the prompt is a whole build → suggest `architect` for the
design docs instead of one mega-prompt.
Output
The prompt in a fenced block, ready to paste. Then at most three lines: what you assumed, what to tweak, and (if relevant) which rule you emphasized and why. No lecture on prompt engineering.
Done when
The prompt is self-contained, has a testable definition of done, carries the grounding + efficiency rules, and contains nothing that doesn't change the output. If your notes are longer than the prompt, cut the notes.
Six Claude Code skills (architect, review-swarm, ask-the-council, prompt-generator, up-to-date, ponytail) bundled as an installable plugin.
Other skills on alon-skills.
- /architect
Produces Alon's design-doc system BEFORE code: SOURCE_OF_TRUTH, ARCHITECTURE_ROADMAP, TODO_WORKFLOW, CLAUDE.md (+ modular docs/architecture). Model first: data + invariants → core enforcement → failure paths → API contracts → phased workstream. Use proactively when a new app,
Open skill - /ask-the-council
Convenes a panel of opinionated advisors (parallel Claude subagents, each with a distinct mandate and a forbidden move so they genuinely diverge), then a Chairman synthesis that COMMITS to one recommendation with explicit tradeoffs. Use proactively for any high-stakes design,
Open skill - /ponytail
Forces the laziest solution that actually works — YAGNI, reuse before new code, stdlib before custom, native platform before dependencies, one line before fifty. Levels: lite/full/ultra. Use proactively when a solution is growing beyond the minimum: new abstractions,
Open skill - /review-swarm
Local, free, multi-specialist review of a diff: parallel Claude subagents (correctness, security/trust boundaries, data/perf, architecture-altitude, ponytail-simplicity, tests/failure paths), adversarial verification, dedup, ranked file:line report. Use proactively when asked to
Open skill - /up-to-date
Preflight sync + situational brief before repo work: fetch origin, ahead/behind divergence, recent commits, open PRs/issues, dirty-state warnings, one recommended first action. Read-only — never pulls or rewrites a dirty tree without explicit OK. Use proactively before starting
Open skill

