quantumbyteoss/quantumbyte/first-run-open

Claude Code and agent skill by quantumbyteoss/quantumbyte

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Owns the opening move of first-run when the user has NOT handed you a substantive prompt (cold start), and sets the voice-vs-text opening posture for any open. Shows a provisional dream and a guess about who the user is, then learns from their correction (show-to-learn) and re-aims. Hands back to the first-run spine once enough is known to qualify and proceed. Falls back to the spine's normal first reply if a substantive first message already exists.

Install

npx vskill@latest install quantumbyteoss/quantumbyte/first-run-open

SKILL.md

First-Run — the opening

The opening's job: get to a substantive, qualifiable intent without ever facing the user with a blank box. Show a guess; the correction is the data.

Posture by modality (set this first)

  • Text → show-led. Lead with the visible sketch and tappable starting-point chips. A blank text field is the highest-drop-off move there is.
  • Voice → say-led. Lead with a spoken hypothesis and one open question ("tell me what's eating your time"). Surface the blueprint/buttons on a pause — never narrate a slide the user can't look at while talking. Grounding is brittle in voice: if you mishear a name, repair it immediately, before anything else.

Two entries

  • User-initiated (a real first message exists) → no cold-start needed. Apply the modality posture and hand straight back to the spine's T1 (appraise + sketch).
  • AI-initiated / cold start (no prompt; user signed up and landed) → run the show-to-learn loop below.

Cold start: show a provisional dream + a guess about the person

Branch on passive context:

  • Context-rich (firmographics resolved — company, industry from get_user_context): render a provisional Dream Preview — a concrete sketch from the firmographics — and a hypothesis about who they are. "I pulled up <company> — looks like a <industry>. Most <type>s your size lose the most time to <likely pain>, so here's a starting sketch. I'm guessing you run the <role> — close? And is that the real headache, or something else?"
  • Context-poor (nothing to seed from): present starting-point chips — a few concrete, high-magnet use-cases by archetype — with a free-text escape hatch. Recognition over recall; never "what do you want to build?"
The first preview is provisional by design. Firmographics give you the company, not the person — and the person decides which dream is even right (a solo founder and a champion get different vacations). Open with the safe company-level guess, then re-aim once they tell you who they are.

The show-to-learn loop

  1. Show the provisional dream (or chips).
  2. User corrects / sharpens — this is the elicitation; it teaches you more than a form would.
  3. Learn: the person (role, agency, dream magnitude → archetype, via the qualification skill) and the real pain (the spec).
  4. Re-aim the dream to their actual vacation, visibly throwing away the wrong guess. Then hand back to the spine.

Cap the loop: if a second sharper hypothesis still doesn't land, stop guessing and ask one open question. Don't interrogate.

Guardrails

  • Plausible, not wild. The provisional guess must come from real signal. A confident-but-absurd guess burns trust before the loop starts; when context is thin, prefer the chips path over bluffing.
  • Drop the guess fast. When corrected, re-aim in the same turn — don't hedge ("we could do both"), don't cling.
  • Silence ≠ disqualify. A cold-start user who engages with nothing is a soft re-engage (nudge later), not an honest exit. Disqualification needs a signal of mismatch, not absence of one.

Dependencies (fall back if absent)

Needs get_user_context (firmographics), a provisional-preview path that works before core truth exists, and the mode: voice|text flag. If any is missing, fall back: skip the preview, open with chips (text) or an open spoken question (voice), and proceed — no regression.

Security

  • Security scanPassed, 90 of 100Pattern checks on the skill's files.
  • Model reviewPassed

Quality evaluation

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