name	product-feedback-sim
description	Get structured feedback on a product, copy, pricing, or feature from a simulated panel of target users. Use when the user wants to rank A/B candidates, find persuadable-middle users, or compute a semantic gradient before shipping. Triggers on 产品反馈模拟, A/B 排序, SGO 梯度, persuadable middle, 50 个虚拟用户打分, 看看目标用户怎么想.

Product Feedback Sim

Thin scenario wrapper around persona-sim. Runs SGO on a product/copy candidate against a target user panel.

Recipe

import sys
sys.path.insert(0, str(__import__('pathlib').Path.home() / '.claude/skills/persona-sim'))
from lib import sampler, sim_engine

# 1. Define target audience
panel = sampler.sample_personas(
    n=30,                                              # 30-50 is cost-effective
    filters={"occupation_isco": "software", "age": (25, 50)},  # substring match on occupation
    source="nemotron_usa",
    mode="stream",
)

# 2. Single-version feedback (fast)
base = sim_engine.panel_score(
    panel,
    target="<your product/copy/feature description>",
    goal="<what you want to maximize, e.g. paid conversions>",
)
# → aggregate.histogram / median / iqr / disagreement_flag / by_age / by_gender

# 3. A/B (or N-way) gradient ranking — uses anchored counterfactuals on persuadable middle
ranked = sim_engine.sgo(
    panel,
    target="<original version>",
    candidates=["variant A", "variant B", "variant C"],
    goal="<same goal>",
)
# → ranking: [{"candidate", "avg_score_lift", "n_probed"}, ...]  sorted descending

Decision rules

Use panel_score only when you want to understand a single version (no alternatives yet).
Use sgo ONLY when there's a persuadable middle (score 4-7). If base median is ≤3 or ≥8, SGO has no signal — iterate on the base version first.
n=30 minimum for stable distribution; n=50-100 if you need tight CIs.
Always expose disagreement_flag to the user — high-variance results are signal, not noise.
Always prepend the disclaimer from result["warning"] when showing output.

Target selection guidance

If the user says "我的产品用户是 X"：

User type	Filter expression
软件开发者	`{"occupation_isco": "software"}`
企业决策者	`{"occupation_isco": "manager", "age": (35, 60)}`
Gen Z	`{"age": (18, 27)}`
大城市高收入	`{"region": ["NY","CA","MA","WA"], "age": (28, 50)}`
老年人医疗产品	`{"age": (65, 100), "adults_only": False}`（覆盖默认的成人限制无需）

Ambiguous user type → use no occupation filter, let distribution surface the signal.

Cost note

Haiku 4.5 via Colorist at ~$0.003/call. A 30-person panel + 3-candidate SGO = 30 + (persuadable × 3) ≈ 50-100 calls ≈ $0.15-0.30 per full run.

import sys sys.path.insert(0, str(__import__('pathlib').Path.home() / '.claude/skills/persona-sim')) from lib import sampler, sim_engine # 1. Define target audience panel = sampler.sample_personas( n=30, # 30-50 is cost-effective filters={"occupation_isco": "software", "age": (25, 50)}, # substring match on occupation source="nemotron_usa", mode="stream", ) # 2. Single-version feedback (fast) base = sim_engine.panel_score( panel, target="<your product/copy/feature description>", goal="<what you want to maximize, e.g. paid conversions>", ) # → aggregate.histogram / median / iqr / disagreement_flag / by_age / by_gender # 3. A/B (or N-way) gradient ranking — uses anchored counterfactuals on persuadable middle ranked = sim_engine.sgo( panel, target="<original version>", candidates=["variant A", "variant B", "variant C"], goal="<same goal>", ) # → ranking: [{"candidate", "avg_score_lift", "n_probed"}, ...] sorted descending

Decision rules

Use panel_score only when you want to understand a single version (no alternatives yet).

Use sgo ONLY when there's a persuadable middle (score 4-7). If base median is ≤3 or ≥8, SGO has no signal — iterate on the base version first.

n=30 minimum for stable distribution; n=50-100 if you need tight CIs.

Always expose disagreement_flag to the user — high-variance results are signal, not noise.

Always prepend the disclaimer from result["warning"] when showing output.

Target selection guidance

If the user says "我的产品用户是 X"：

User type

Filter expression

软件开发者

{"occupation_isco": "software"}

企业决策者

{"occupation_isco": "manager", "age": (35, 60)}

Gen Z

{"age": (18, 27)}

大城市高收入

{"region": ["NY","CA","MA","WA"], "age": (28, 50)}

老年人医疗产品

{"age": (65, 100), "adults_only": False}（覆盖默认的成人限制无需）

Ambiguous user type → use no occupation filter, let distribution surface the signal.

Cost note

Haiku 4.5 via Colorist at ~$0.003/call. A 30-person panel + 3-candidate SGO = 30 + (persuadable × 3) ≈ 50-100 calls ≈ $0.15-0.30 per full run.

product-feedback-sim