Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add Travisun/Opptrix --skill private-company-researchgit clone --depth 1 https://github.com/Travisun/OpptrixWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/travisun/opptrix/private-company-research)<a href="https://agentmods.dev/skills/travisun/opptrix/private-company-research"><img src="https://agentmods.dev/badge/skills/travisun/opptrix/private-company-research/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/travisun/opptrix/private-company-research"><img src="https://agentmods.dev/badge/skills/travisun/opptrix/private-company-research.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00093 | $0.01437 |
| Opus 5 | $0.00046 | $0.00718 |
| Sonnet 5 | $0.00019 | $0.00287 |
| Haiku 4.5 | $0.00009 | $0.00144 |
Grade A, and why
private-company-research scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 100 lines — stays where its author put it; the contents beside it link to each section on GitHub.
未上市公司深度研究
面向蚂蚁、小红书、SpaceX、Stripe 等未上市标的。最终目标：在信息天然稀缺下，尽可能还原生意真实价值（不是融资叙事估值）。署名：Opptrix · AI Berkshire 分析。
详细任务说明书见 references/（经 get_agent_skill_file 读取）。偏见原则见 bias-and-principles.md。
何时使用 / 边界
| 使用 | 不要用本技能 |
|---|---|
| 无标准财报的公司/独角兽深度拼图 | 上市标的尽调 → @skill:equity-deep-dive / @skill:investment-research（勿混用） |
| 融资轮次 + 可比公司 + 情景估值 | 已上市四大师研究 → @skill:investment-team |
默认常 proxy/insufficient，诚实留白 |
研究质量（硬性）
- 宁可留白「不知道」，禁止推测填满模板伪装确定性。
- 关键数据标置信度 🟢/🟡/🔴；事实 vs 推理分栏。
- 信息极度稀缺 → 第一性原理四问（见 bias 文档），不追求形式完整。
- 强制结论：投资 / 观望 / 回避（或灰色地带），并写明置信度。
- 镜子测试；快速否决（诚信/能力圈）。
get_current_time；禁止训练知识冒充已刷新公开线索。
团队角色（最多并行 4～6 路）
| 角色 | 职责 | 说明书 |
|---|---|---|
| Team Lead（父） | 拼图、冲突仲裁、定稿 | 本文件 |
| business-decoder | 商业模式与用户 | role-business.md |
| financial-detective | 财务拼凑与估值 | role-financial.md |
| competitive-mapper | 行业与竞争 | role-competitive.md |
| risk-governance-analyst | 风险与治理 | role-risk-governance.md |
| tech-ip-analyst + signal-miner | 技术与替代数据（可合并一路） | role-tech-and-signals.md |
若配额紧张：先并行 business / financial / competitive / risk 四路；tech+signal 由 Lead 补扫或第二波并行后 reclaim。
取数（Opptrix）
| 用途 | 工具 |
|---|---|
| 公开线索 | http_fetch / browser_navigate / list_news_articles / search_library |
| 可比上市同业 | search_instruments + get_instrument_* / batch_instrument_snapshots |
| 用户导入融资 JSON | workspace_write / ask_user |
禁止脚本联网爬虫；无雪球凭据流。可比算术可用本地脚本：
python scripts/run_rigor_json.py --input data.json --output result.json
python scripts/scorecard.py --input evidence.json --output scorecard.json
data_mode 默认常为 proxy；完全无法支撑则 insufficient + 灰色地带。也可 get_agent_skill_file 取 @skill:financial-data 的 rigor 脚本对照。
并行编排
- 展示团队框架；确认后启动。
update_research_checklist。 - 父预检：至少一次
http_fetch或新闻工具可达。 - 同一轮
run_subagent并行（建议 ≤4，必要时两波）；子任务禁止再委派。 - 每路：
get_subagent→ 立即reclaim_subagent。 - 交叉验证：数据冲突仲裁；增长叙事 vs 招聘等信号一致性；白/灰/黑区地图。
- 按
report-outline.md汇总 →scorecard→create_web。 - 收尾 cancel/reclaim。
What ships with it
13 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- references/bias-and-principles.md 1.3 KB
- references/report-outline.md 935 B
- references/role-business.md 1.2 KB
- references/role-competitive.md 759 B
- references/role-financial.md 1.2 KB
- references/role-risk-governance.md 920 B
- references/role-tech-and-signals.md 1.1 KB
- scripts/financial_rigor.py 20 KB runs code
- scripts/fixtures/sample_scorecard_full.json 614 B
- scripts/fixtures/sample_scorecard_insufficient.json 157 B
- scripts/report_audit.py 22 KB runs code
- scripts/run_rigor_json.py 13 KB runs code
- scripts/scorecard.py 7.8 KB runs code
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 7d ago First seen · 100 lines · 93 tokens per session scan A 542ffc37ecb3
private-company-research is a skill published in the GitHub repository Travisun/Opptrix (231 stars, last pushed 3d ago), licensed Apache-2.0. It adds 93 tokens to every session and 1,437 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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A Chinese-language adviser for Hong Kong stock initial public offerings, or IPOs—the first sale of a company's shares to the public. It covers how to apply, how much to apply for, and risks such as the share price falling below the offering price.
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rodya-caijing-studio
A toolkit for researching Chinese A-share and Hong Kong-listed companies and producing financial content. It includes separate workflows for company fundamentals, earnings, valuation, risks, industries, and IPO checks.
caijing-earnings
A finance research skill for reviewing listed companies’ earnings reports, or preparing for an upcoming report. It focuses on Chinese A- and Hong Kong-listed companies.
caijing-valuation
A Chinese-language adviser that assesses whether a stock's current valuation looks high or low. It adapts the comparison to the industry and examines historical and peer-company valuation ranges.