Product Feedback Sim

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="",
    goal="",
)
# → 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="",
    candidates=["variant A", "variant B", "variant C"],
    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.

See also

• ~/.claude/skills/persona-sim/SKILL.md for the underlying API and rules
• vote-predict for policy/voting, social-sandbox for social experiments

Source & license

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

• Author: Yrzhe
• Source: Yrzhe/claude-skills
• License: MIT

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