TinyTroupe Feed Research Lab
/install tinytroupe-feed-research-lab
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
- Collect 2-10 draft posts or content angles.
- Clarify the target audience if available.
- Run
scripts/tinytroupe_feed_research_lab.pyin deterministic mode. - Read
feed_research_report.md,feed_research.json, andpersona_reactions.csv. - Present the best draft, why it won, key objections, rewrite suggestions, and the boundary statement.
- 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
idandtext. - Plain text blocks separated by
---.
--personas-file accepts JSON objects with:
namesegmentinterestsdislikesreply_biasskepticismlink_sensitivitysafety_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.
- 确保已安装 OpenClaw(本地或 Docker 部署)
- 在对话框中输入安装命令:
/install tinytroupe-feed-research-lab - 安装完成后,直接呼叫该 Skill 的名称或使用
/tinytroupe-feed-research-lab触发 - 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
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。