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 deep-company-seriesgit 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/deep-company-series)<a href="https://agentmods.dev/skills/travisun/opptrix/deep-company-series"><img src="https://agentmods.dev/badge/skills/travisun/opptrix/deep-company-series/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/deep-company-series"><img src="https://agentmods.dev/badge/skills/travisun/opptrix/deep-company-series.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 | $0.00081 | $0.01261 |
| Opus 5 | $0.00041 | $0.00630 |
| Sonnet 5 | $0.00016 | $0.00252 |
| Haiku 4.5 | $0.00008 | $0.00126 |
Grade A, and why
deep-company-series 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 10d 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.
What it actually says
《看懂 XX》长文系列
为指定公司撰写 3–8 篇可独立分享的系列长文。核心能力是改得严，不是堆文采。署名：Opptrix · AI Berkshire 分析。
何时使用 / 边界
| 使用 | 不要用本技能 |
|---|---|
| 教科书级多篇系列，认知重置→决策闭环 | 单篇公众号三 Agent → @skill:wechat-article（勿合并） |
| 愿花多轮修订与跨篇一致性扫描 | 单篇研报 → @skill:investment-research |
季报点评 → @skill:earnings-review / @skill:earnings-team |
|
行业全景 → @skill:industry-research |
研究质量（硬性）
- 事实核查 > 文采；禁用「显然/必然/我认为」等（见
fact-check-checklist.md）。 - 禁止概率加权期望年化；情景只列触发条件与方向。
- 跨篇数字一致；防双算（并表 vs 投资组合）。
- 终篇须镜子测试与红线清单；决策档位明确。
get_current_time；A/B/C；关键数字 rigor + 抽检。
篇数适配
| 复杂度 | 篇数 | 特征 |
|---|---|---|
| 高 | 7–8 | 多业务 + 隐藏资产 + 丰富管理层史料 |
| 中 | 4–6 | 2–3 业务线 + 时代变量 |
| 低 | 3 | 主业清晰（开篇护城河 / 最大变量 / 估值决策） |
8 主轴模板与篇内骨架：references/series-template.md。无独立内容的篇合并，禁止凑字数。
取数
同投资研究工具栈。可先跑 @skill:investment-research 或 @skill:investment-team 作内部底稿，再改写成系列。用户确认篇目与核心论点后再写。
python scripts/run_rigor_json.py --input data.json --output result.json
python scripts/report_audit.py extract --report chapter.md
python scripts/scorecard.py --input evidence.json --output scorecard.json
步骤
阶段 1 — 调研
近 5 年年报/最新季报；独立观点多源；与用户确认篇数与主轴。
阶段 2 — 写作（01→末篇顺序）
每篇：workspace_write 存稿。篇头引用块 + 钩子开篇 + 要点回顾 + 下期预告 + 免责斜体。旧系列目录冲突时用带日期后缀新目录，不覆盖。
阶段 3 — 跨篇一致性
扫描：市值/净利/持股跨篇一致；术语首次解释；交叉引用有效；要点回顾数字与正文一致。可选用子 Agent 扫描后 reclaim。
阶段 4 — 交付
优先 create_web（索引页 + 分章，或用户指定篇）。注意单 skill 产物体积；超大则多页/多轮更新。署名与免责声明。隐私：勿写入本机路径/个人身份信息。
修订流程
硬错误必改 → 主观化弱化 → 颗粒度按可读性 → 不可靠第三方宁可删。改一处联动全系列引用。
禁止
- 替读者做买卖指令式荐股；预测点位假装事实
- 概率加权期望；「大佬也持有」背书
- 强求 8 篇凑数；脚本联网；与 wechat-article 混用
What ships with it
10 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/fact-check-checklist.md 647 B
- references/series-template.md 1006 B
- 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.
- 10d ago First seen · 94 lines · 81 tokens per session scan A 5f4e3a93ef7f
deep-company-series is a skill published in the GitHub repository Travisun/Opptrix (231 stars, last pushed 3d ago), licensed Apache-2.0. It adds 81 tokens to every session and 1,261 once invoked, about $0.0004 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 2026-08-30.
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