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 earnings-reviewgit 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/earnings-review)<a href="https://agentmods.dev/skills/travisun/opptrix/earnings-review"><img src="https://agentmods.dev/badge/skills/travisun/opptrix/earnings-review/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/earnings-review"><img src="https://agentmods.dev/badge/skills/travisun/opptrix/earnings-review.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 | $0.00082 | $0.01239 |
| Opus 5 | $0.00041 | $0.00620 |
| Sonnet 5 | $0.00016 | $0.00248 |
| Haiku 4.5 | $0.00008 | $0.00124 |
Grade A, and why
earnings-review 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
财报精读（一手资料）
输入：公司名 季度（如 腾讯 2025Q4、美团 最新）。署名：Opptrix · AI Berkshire 分析。
「我从不看卖方研报，只读原始财报。」—— 李录
何时使用 / 边界
| 使用 | 不要用本技能 |
|---|---|
| 单人精读一期年报/季报/纪要，形成判断 | 速读摘要 → @skill:earnings-quick-read（勿合并） |
| 强调 MD&A 语气、附注、承诺追踪 | 团队+成稿发布 → @skill:earnings-team |
完整公司研究 → @skill:investment-research |
研究质量（硬性）
- 资料可得性 A/B/C：A 完整原文；B 部分原文/汇总（标非原始）；C 仅新闻摘要（跳过附注深挖，标不足）。
- 强制回答：超预期 / 符合预期 / 低于预期（禁止「基本符合」两面话）。
- 对投资论文：强化 / 无影响 / 削弱 / 破裂。
- 关键数字走
financial_rigor；发布前report_audit。 get_current_time；事实/观点分栏。
取数
| 优先级 | 工具 |
|---|---|
| 公告原文 | get_instrument_notices → get_notice_content |
| 三表 | get_instrument_income_statement / get_instrument_balance_sheet / get_instrument_cash_flow / get_instrument_financials |
| 用户上传 | read_document |
| 补洞 | http_fetch / browser_navigate（IR/交易所披露页） |
| 市值验算 | get_instrument_quotes + 脚本 |
无法取得原文时按 @skill:financial-data 拼凑，必须标注「非原始财报，来自汇总」，双源误差 >1% 须标记。
python scripts/run_rigor_json.py --input data.json --output result.json
python scripts/report_audit.py extract --report draft.md
python scripts/scorecard.py --input evidence.json --output scorecard.json
步骤
- 评级资料可得性 A/B/C，写入报告头。
- 并行收集：财报原文、电话会纪要、股东信、投资者日材料（能拿到多少算多少）。
- 核心数据：利润表 / 现金流（经营现金流 vs 净利）/ 资产负债表健康度 → rigor 验算。
- MD&A 精读：坦诚/清晰 vs 模糊/转移/归因外部；上期承诺兑现表；Q&A 尖锐问题评分。
- 附注清单：关联交易、股权激励稀释、或有负债、会计政策变更、分部、集中度；异常信号勾选。
- 历史对比：≥4 季或 3 年趋势；vs 指引。
- 结论四问 +
create_web+ 抽检准出。
报告结构
- 核心数据速览
- 本期最重要的 3 个变化（≤500 字）
- 管理层语气与承诺追踪
- 附注隐藏信息
- 电话会 Q&A 精选
- 与投资论文关系
- 结论：这份财报改变了什么？
- 免责声明
禁止
- 只读二手摘要却假装精读原文
- 「基本符合预期」式太极
- 脚本联网；无结论交付；与 quick-read 混淆
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 534 B
- scripts/fixtures/sample_scorecard_full.json 605 B
- scripts/fixtures/sample_scorecard_insufficient.json 148 B
- scripts/fixtures/sample_verify_market_cap.json 309 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 · 91 lines · 82 tokens per session scan A 1acd8d7d3718
earnings-review is a skill published in the GitHub repository Travisun/Opptrix (231 stars, last pushed 2d ago), licensed Apache-2.0. It adds 82 tokens to every session and 1,239 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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