Simulate

/simulate — Reaction Simulator

12 personas. 3 rounds. They react to your announcement AND to each other — camps form, opinions shift, objections emerge.

Usage: /simulate [paste your announcement, or describe the situation]

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How It Works

Inspired by social simulation research (OASIS engine). The key insight: agents that interact with each other produce fundamentally different output than 12 independent prompts, because opinions shift when personas see each other's arguments.

1. Context — read product data, identify audience and channel
2. Personas — generate 12 with detailed profiles (MBTI, influence, bias)
3. Round 1 — all 12 react independently to the announcement (parallel, each does 1 web search)
4. Round 2 — each persona reads ALL Round 1 reactions, can shift stance, reply, form camps (parallel)
5. Round 3 — final positions after seeing the full debate (parallel)
6. Report — opinion trajectory, camps, ranked objections, suggested rewrite

~36 agent calls total across 3 sequential rounds.

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Phase 1: Context

Extract from input

• Text — the announcement ($ARGUMENTS, or ask)
• Product — what product/company is this about?
• Audience — who sees this?
• Channel — where is it published? (email, Twitter, blog, HN, internal)

Read product context silently

• Check for README.md, CLAUDE.md, or docs in the current project for product details
• If a URL is provided, fetch it with web tools
• Look for: current pricing, customer count, competitors, recent changes
• If context is thin, ask the user for key details (price, customer count, what changed)

This context goes into every persona prompt so reactions reference real pricing, competitors, and customers.

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Phase 2: Generate Personas

Generate 12 personas in ONE call. See [references/persona-schema.md](references/persona-schema.md) for:

• Full field spec (name, handle, MBTI, stance, influence, sentiment bias, key bias, etc.)
• 5 mandatory archetypes (early adopter, bootstrapper, enterprise, skeptic, journalist)
• Dynamic archetypes by announcement type (price increase, launch, removal, layoff, tweet, policy)
• Diversity rules (MBTI spread, age range, stance balance)

The persona panel should guarantee at least 2 opposing personas with negative sentiment — anti-sycophancy is the entire point of this skill.

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Phase 3: Run 3 Rounds

See [references/round-prompts.md](references/round-prompts.md) for exact prompt templates.

Round 1 — Initial Reactions

Spawn all 12 personas in parallel using the Agent tool (subagent_type: "general-purpose"). Each:

• Picks 1-2 web search tools to find real-world context (similar announcements, competitor pricing, community sentiment)
• Reacts honestly in character
• Returns: stance, post, objection, what would fix it, research context

Collect all 12 responses before proceeding.

Round 2 — Debate & Shifts

Spawn all 12 again. Each persona sees the FULL Round 1 feed (all 11 others' posts). They:

• Can shift stance (and must explain why)
• Can reply to specific personas
• Declare which camp they belong to

Do not summarize the feed. Pass full posts — summarizing kills emergent dynamics.

After Round 2, compute: shift count, shift direction, camp clusters.

Round 3 — Final Positions

Spawn all 12 again with Round 1 + Round 2 feed + shift summary. Each returns:

• Final stance and confidence
• Deal breaker (or "None")
• Suggested fix (one sentence rewrite)

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Phase 4: Synthesize Report

See [references/report-template.md](references/report-template.md) for the full output format.

Key sections:

1. Verdict — one bold sentence + explanation
2. Opinion trajectory — table showing stance counts across Round 1 → 2 → 3
3. Camps that formed — who grouped together and why
4. Key opinion shifts — who changed their mind and which argument caused it
5. Top 3 objections — ranked by frequency, with best persona quotes and fix suggestions
6. Real-world context — aggregated from personas' web searches
7. Suggested rewrite — new version addressing the top 3 objections
8. Hottest take — the most memorable quote from any persona

After presenting, offer: "Want to talk to any persona? I can spawn a conversation where they remember the full debate."

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Gotchas

• Round 2 is the magic. Round 1 is "ask 12 prompts." Round 2 is where personas react to each other and camps emerge. Never skip Round 2.
• Full feed, never summarized. Each Round 2/3 persona must see ALL other personas' prior posts verbatim. Summarizing destroys the emergent dynamics.
• MBTI drives communication diversity. An INTJ writes data-driven analysis. An ESFP writes emotional reactions. Include MBTI in the persona prompt.
• Web search is the grounding advantage. Without web search, personas give generic LLM-flavored reactions. With search, they reference real price doublings, real competitor pricing, real community backlash.
• Product context is non-negotiable. A "price increase" simulation without knowing the current price produces worthless output. Always read product files first.
• The trajectory table is the highest-value output. The opinion shift from Round 1 → 3 shows what ARGUMENTS change minds.
• 12 personas x 3 rounds = ~36 agent calls. Budget accordingly.
• All agents must run in foreground. Never use run_in_background — results are needed for the next round.
• Don't oversell the split. 12 personas is not a focus group of 1,000. The value is the OBJECTIONS, the SHIFTS, and the REWRITE — not statistical significance.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: progrmoiz
• Source: progrmoiz/skills
• License: MIT

Install and usage instructions live in the source repository linked above.