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 investment-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/investment-research)<a href="https://agentmods.dev/skills/travisun/opptrix/investment-research"><img src="https://agentmods.dev/badge/skills/travisun/opptrix/investment-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/investment-research"><img src="https://agentmods.dev/badge/skills/travisun/opptrix/investment-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 | $0.00103 | $0.02075 |
| Opus 5 | $0.00051 | $0.01038 |
| Sonnet 5 | $0.00021 | $0.00415 |
| Haiku 4.5 | $0.00010 | $0.00208 |
Grade A, and why
investment-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 6d 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 — 123 lines — stays where its author put it; the contents beside it link to each section on GitHub.
四大师综合深度研究
对用户指定标的做系统化价值投资研究。署名交付：Opptrix · AI Berkshire 分析。
何时使用 / 非目标 / 边界
| 使用 | 不要用本技能 |
|---|---|
| 单标的四大师框架深度研究 + 明确决策档位 | 多空辩论研讨团 → @skill:multi-role-research-council |
| 首次覆盖或重做完整尽调长文 | 通用尽调无四大师强制框架 → @skill:equity-deep-dive（勿合并） |
| 需要镜子测试与 A/B/C 信息丰富度 | 只要管理层纵深 → @skill:management-deep-dive |
未上市 → @skill:private-company-research |
|
并行四角色团队 → @skill:investment-team |
研究质量（硬性）
- 四大师：段永平（生意/本分）· 巴菲特（财务/安全边际）· 芒格（逆向/失败路径）· 李录（长期确定性/能力圈）——须显式覆盖或诚实声明某师因数据不足无法评分。
- 强制结论：须给出 通过 / 有条件通过 / 不通过 / 灰色地带（数据不足） 之一；区分「好生意」≠「好价格下的好投资」；可附激进/稳健/保守分层与价格或条件区间（无依据则写触发条件，禁止假精确）。
- 镜子测试：买入或「通过」前 ≤5 句说清：买什么生意、为何现在、什么会证伪。说不清 → 不通过。
- 信息丰富度 A/B/C：报告开头标注；资料多≠确定性高；AI 置信度≠投资确定性。C 级用第一性原理，禁止拼凑假完整报告。
- 快速否决：诚信污点、能力圈外且说不清赚钱方式 → 一票否决，估值再便宜不打分对冲。
- 时间：研究前
get_current_time；报告头写数据截止日期。 - 事实/观点分栏；禁止「我认为/显然」；联网失败禁止用训练知识冒充已刷新数据，并降级
data_mode。
Opptrix 取数（主路径）
禁止依赖 （禁止依赖外部源仓路径） 或脚本联网爬虫。主路径：
| 维度 | 工具 |
|---|---|
| 定位 | search_instruments / ask_user |
| 快照/行情 | get_instrument_snapshot / get_instrument_quotes |
| 画像 | get_instrument_profile |
| 财务 | get_instrument_financials / get_instrument_income_statement / get_instrument_balance_sheet / get_instrument_cash_flow / get_instrument_financial_indicators |
| 分红/股东 | get_instrument_dividend / get_instrument_shareholders |
| 资讯/公告 | list_news_articles / get_news_article / get_instrument_notices / get_notice_content |
| 补洞 | http_fetch / browser_navigate（第二源；写入 workspace 后再验算） |
交叉验证规范可激活 @skill:financial-data。取数后 workspace_write 证据 JSON/底稿。
脚本（本地计算，不联网）
python scripts/run_rigor_json.py --input data.json --output result.json
python scripts/financial_rigor.py verify-market-cap --price … --shares … --reported … --currency …
python scripts/financial_rigor.py verify-valuation --price … --eps … --bvps … --fcf-per-share …
python scripts/financial_rigor.py cross-validate --field revenue --values '{"源1":1,"源2":2}' --unit 亿
python scripts/financial_rigor.py three-scenario --price … --eps … --shares … --growth a b c --pe x y z
python scripts/report_audit.py extract --report draft.md
python scripts/report_audit.py verdict --results results.json --report draft.md
python scripts/scorecard.py --input evidence.json --output scorecard.json
What ships with it
8 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.
- scripts/financial_rigor.py 20 KB runs code
- scripts/fixtures/sample_cross_validate_full.json 538 B
- scripts/fixtures/sample_scorecard_full.json 609 B
- scripts/fixtures/sample_scorecard_insufficient.json 152 B
- scripts/fixtures/sample_verify_market_cap.json 313 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.
- 6d ago First seen · 123 lines · 103 tokens per session scan A ed94e8e1ab5a
investment-research is a skill published in the GitHub repository Travisun/Opptrix (231 stars, last pushed 3d ago), licensed Apache-2.0. It adds 103 tokens to every session and 2,075 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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