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-teamgit 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-team)<a href="https://agentmods.dev/skills/travisun/opptrix/earnings-team"><img src="https://agentmods.dev/badge/skills/travisun/opptrix/earnings-team.svg" alt="Measured on agentmods" 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 | $0.00073 | $0.01311 |
| Opus 5 | $0.00036 | $0.00656 |
| Sonnet 5 | $0.00015 | $0.00262 |
| Haiku 4.5 | $0.00007 | $0.00131 |
Grade A, and why
earnings-team 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 8d 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
财报精读团队 + 成稿
输入：公司名 季度。三阶段：研究 → 合成 → 发布。署名：Opptrix · AI Berkshire 分析。
何时使用 / 边界
| 使用 | 不要用本技能 |
|---|---|
| 重要公司关键财报，需要深度 + 可读成稿 | 单人精读 → @skill:earnings-review |
| 四大师并行读同一期财报 | 非财报公众号三 Agent → @skill:wechat-article |
全面公司研究团队 → @skill:investment-team |
研究质量（硬性）
一手优先（A/B/C 资料可得性）；强制超/符/低预期；论文影响四态；反面检验；禁止太极结论。并行独立成稿再综合。
团队角色
| 阶段 | 角色 | 大师 | 核心问题 |
|---|---|---|---|
| 研究 | Team Lead（父） | — | 统筹、找交集与矛盾、定稿 |
| 研究 | business-reader | 段永平 | 生意变好还是变差？ |
| 研究 | financial-auditor | 巴菲特 | 真钱还是假钱？安全边际？ |
| 研究 | competition-reader | 芒格 | 竞争格局怎么变？ |
| 研究 | risk-hunter | 李录 | 隐瞒与风险信号？ |
| 发布 | editor | — | 研究报告 → 可读长文 |
| 发布 | reader-reviewer | — | 可读性/价值/可信度/行动指引 |
Brief：references/role-briefs.md。
取数与脚本
同 @skill:earnings-review。巴菲特角色必须跑 rigor（cross-validate / verify-market-cap / verify-valuation / three-scenario）。
python scripts/run_rigor_json.py --input data.json --output result.json
python scripts/scorecard.py --input evidence.json --output scorecard.json
python scripts/report_audit.py extract --report draft.md
并行编排（硬性）
阶段一 · 研究
get_current_time；拉取一手材料；评定 A/B/C；update_research_checklist。- 父先验证公告/财务工具可达；失败则停并降级标注。
- 同一轮
run_subagent×4（四大师）；子任务禁止再委派。 - 每路终态：
get_subagent→ 立即reclaim_subagent。 - 取数失败禁止伪装：顶栏降级声明。
阶段二 · 合成
综合四稿：共识点、矛盾点、被忽略角落。产出研究底稿（结构见下）。scorecard.py 辅助档位。
阶段三 · 发布
并行 run_subagent：editor + reader-reviewer → reclaim。Lead 处理「必须修改」，通读定稿。report_audit 准出 → create_web（可一篇成稿；底稿可 workspace_write，注意附件体积）。
收尾
cancel_subagent；无未 reclaim。
研究底稿目录
- 一句话结论
- 本期最重要 3 个变化
- 四大师评分表
- 核心数据速览
- 各视角深度摘要
- 管理层语气与承诺
- 四大师会怎么做
- 结论四问
成稿原则
保留专业深度与关键数据；倒金字塔；标题有信息量不做标题党；正反面；文末「所以呢」对持有者/观望者分别写清；免责声明。
禁止
- 伪并行；跳过 reclaim；拼报告不找矛盾
- 「基本符合」太极；编辑降维成空话科普
- 脚本联网；与 earnings-review / wechat-article 边界混淆
- 无 web 交付结束（除非用户只要口头要点）
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/checklist.json 433 B
- references/role-briefs.md 1.2 KB
- scripts/financial_rigor.py 20 KB runs code
- scripts/fixtures/sample_cross_validate_full.json 532 B
- scripts/fixtures/sample_scorecard_full.json 603 B
- scripts/fixtures/sample_scorecard_insufficient.json 146 B
- scripts/fixtures/sample_verify_market_cap.json 307 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.
- 8d ago First seen · 109 lines · 73 tokens per session scan A 901e6feed71f
earnings-team is a skill published in the GitHub repository Travisun/Opptrix (230 stars, last pushed today), licensed Apache-2.0. It adds 73 tokens to every session and 1,311 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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caijing-earnings
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