architect
Produces Alon's design-doc system BEFORE code: SOURCE_OF_TRUTH, ARCHITECTURE_ROADMAP, TODO_WORKFLOW, CLAUDE.md (+ modular docs/architecture). Model first: data…
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.
/prompt-generatorContext 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
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]"
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.
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.
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.
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.
**Grounding (no hallucination):**
function names, numbers, or citations.
a plausible guess.
asserted.
**Efficiency (no tokenmaxing):**
question", no summary of what you're about to do.
the task, not to fill space.
**Agent discipline (when the prompt drives a coding/tool agent):**
not prompting.
prompt.
into its steps.
design docs instead of one mega-prompt.
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.
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.
Produces Alon's design-doc system BEFORE code: SOURCE_OF_TRUTH, ARCHITECTURE_ROADMAP, TODO_WORKFLOW, CLAUDE.md (+ modular docs/architecture). Model first: data…
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