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TinyTroupe Feed Research Lab

作者 Zakhar Pashkin · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ✓ 安全检测通过
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在 OpenClaw 中安装
/install tinytroupe-feed-research-lab
功能描述
Run bounded synthetic audience research for draft posts and X-style feed experiments inspired by TinyTroupe and public xai-org/x-algorithm architecture. Use...
使用说明 (SKILL.md)

TinyTroupe Feed Research Lab

Use this skill to compare draft posts with synthetic audience personas and produce a research report. Treat outputs as qualitative pretesting and hypothesis generation, not live X ranking predictions.

Core Workflow

  1. Collect 2-10 draft posts or content angles.
  2. Clarify the target audience if available.
  3. Run scripts/tinytroupe_feed_research_lab.py in deterministic mode.
  4. Read feed_research_report.md, feed_research.json, and persona_reactions.csv.
  5. Present the best draft, why it won, key objections, rewrite suggestions, and the boundary statement.
  6. If the user asks for TinyTroupe proper, use the generated persona specs and experiment plan as the input to a separate TinyTroupe notebook or script.

Quick Start

SKILL_DIR="${CODEX_HOME:-$HOME/.codex}/skills/tinytroupe-feed-research-lab"
python3 "$SKILL_DIR/scripts/tinytroupe_feed_research_lab.py" \
  --audience "AI builders and creator-operators interested in X algorithm research" \
  --draft "I audited this viral X algorithm claim against public source. Verdict: misleading." \
  --draft "Replies are king. Here is what the public repo actually proves." \
  --output-dir /tmp/tinytroupe-feed-research

Use files:

python3 "$SKILL_DIR/scripts/tinytroupe_feed_research_lab.py" \
  --drafts-file /tmp/drafts.json \
  --personas-file /tmp/personas.json \
  --output-dir /tmp/tinytroupe-feed-research

The script writes:

  • feed_research_report.md: human-readable comparison and rewrite guidance.
  • feed_research.json: machine-readable drafts, personas, reactions, and warnings.
  • persona_reactions.csv: row-level persona reactions.
  • share_card.md: short public-safe summary.
  • share_card.svg: visual summary card.
  • tinytroupe_experiment_plan.md: optional bridge plan for a real TinyTroupe run.

Input Formats

--drafts-file accepts:

  • JSON list of strings.
  • JSON list of objects with id and text.
  • Plain text blocks separated by ---.

--personas-file accepts JSON objects with:

  • name
  • segment
  • interests
  • dislikes
  • reply_bias
  • skepticism
  • link_sensitivity
  • safety_strictness

Missing persona fields fall back to conservative defaults.

Boundaries

Read references/research-boundaries.md before presenting results that mention algorithms, feed ranking, virality, reach, shadowbans, or account status.

Never say:

  • "this predicts reach,"
  • "this clones the X For You feed,"
  • "this proves a shadowban,"
  • "this optimizes for the live algorithm,"
  • "this is what real users will do."

Prefer:

  • "synthetic audience reaction,"
  • "draft pretest,"
  • "conversation-quality signal,"
  • "X-style feed research sandbox,"
  • "hypothesis to validate with real posting or user research."

TinyTroupe Bridge

The MVP script does not require TinyTroupe. It produces tinytroupe_experiment_plan.md so a later agent can create a TinyTroupe notebook with:

  • the same personas,
  • the same draft set,
  • a structured reaction schema,
  • a validation note that simulation outputs are research signals.

Companion Skills

Use x-algo-claim-auditor when the task is checking whether a viral algorithm claim is true. Use open-feed-recsys-lab when the task is verifying the public source repo, Phoenix artifact readiness, or architecture map.

安全使用建议
This looks safe to install for local draft-research use. Before running it, review the command, provide only drafts you are comfortable storing locally, and choose a private output directory for the generated reports.
功能分析
Type: OpenClaw Skill Name: tinytroupe-feed-research-lab Version: 1.0.0 The skill bundle provides a deterministic simulation tool for analyzing social media draft posts against synthetic personas. The Python script (scripts/tinytroupe_feed_research_lab.py) performs local data processing using standard libraries without network access, shell execution, or sensitive file reads. The instructions in SKILL.md and documentation in research-boundaries.md include explicit safety guidelines to prevent the AI agent from making misleading claims about reach or algorithm prediction.
能力标签
crypto
能力评估
Purpose & Capability
The stated purpose is synthetic audience pretesting for draft posts, and the visible artifacts align with that purpose through persona scoring, bounded claims, and explicit warnings not to present results as live X reach or ranking predictions.
Instruction Scope
The instructions are scoped to collecting drafts, running the bundled script, reading generated reports, and presenting bounded qualitative findings. No prompt override, hidden goal change, or forced unsafe tool use is evident.
Install Mechanism
There is no package install step or external dependency installation, but use does require running the bundled local Python script. This is expected for the skill's purpose.
Credentials
The script reads user-selected draft/persona files and writes reports to a user-specified output directory. This is proportionate, but generated files may contain draft content and should be stored carefully.
Persistence & Privilege
No credentials, privileged access, network calls, or background persistence are declared. The only persistence shown is local report output such as JSON, CSV, Markdown, and SVG files.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install tinytroupe-feed-research-lab
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /tinytroupe-feed-research-lab 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v1.0.0
Initial release: compare draft posts with deterministic synthetic audience personas and bounded feed-research reports.
元数据
Slug tinytroupe-feed-research-lab
版本 1.0.0
许可证 MIT-0
累计安装 0
当前安装数 0
历史版本数 1
常见问题

TinyTroupe Feed Research Lab 是什么?

Run bounded synthetic audience research for draft posts and X-style feed experiments inspired by TinyTroupe and public xai-org/x-algorithm architecture. Use... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 87 次。

如何安装 TinyTroupe Feed Research Lab?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install tinytroupe-feed-research-lab」即可一键安装,无需额外配置。

TinyTroupe Feed Research Lab 是免费的吗?

是的,TinyTroupe Feed Research Lab 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

TinyTroupe Feed Research Lab 支持哪些平台?

TinyTroupe Feed Research Lab 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。

谁开发了 TinyTroupe Feed Research Lab?

由 Zakhar Pashkin(@zack-dev-cm)开发并维护,当前版本 v1.0.0。

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