Borrowing it
Nothing to install: this file belongs to spy007111/Vibe-Research. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/spy007111/Vibe-Research/main/.agents/skills/earnings-analysis/SKILL.mdgit clone --depth 1 https://github.com/spy007111/Vibe-ResearchWrote 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/spy007111/vibe-research/earnings-analysis)<a href="https://agentmods.dev/skills/spy007111/vibe-research/earnings-analysis"><img src="https://agentmods.dev/badge/skills/spy007111/vibe-research/earnings-analysis/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/spy007111/vibe-research/earnings-analysis"><img src="https://agentmods.dev/badge/skills/spy007111/vibe-research/earnings-analysis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00216 | $0.02696 |
| Opus 5.5 | $0.00086 | $0.01078 |
| Sonnet 5.5 | $0.00043 | $0.00539 |
| Haiku 4.5 | $0.00022 | $0.00270 |
Grade A, and why
earnings-analysis 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 26d 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.
This is a copy
100% identical to earnings-analysis — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 83 lines — stays where its author put it; the contents beside it link to each section on GitHub.
财报拆解(earnings-analysis)
对应 company-research SOP 第 2 阶段(financials)的口径说明书,也用于任何"业绩怎么样"的追问。原则:累计值是原料,单季是基本单位,TTM 看规模,最新期看转向;每一步拆分 / 求和 / 比率都是一次 calc 调用。
0. 四条铁律
- A 股财报披露的是报告期累计值(Q1 / H1 / Q1–Q3 / 全年);任何"单季""环比"都必须经
quarterize拆出来,不在脑子里减。 - 扣非 vs 归母:估值分子用扣非(一次性损益不能 ×4),增速主判用归母(与一致预期 EPS 同口径),两者并列报、差异大时必须解释(投资收益 / 补助 / 减值 / 公允价值变动)。
- 分子分母同期、同口径:同比对同季、TTM 对 TTM,不拿累计值比单季。
- 比率(毛利率 / 净利率 / 费用率 / 负债率 / 占比)一律
ratio(numerator, denominator, label, unit_in),两数同单位由你保证,calc 不换算单位。
1. 口径地图
| 口径 | 定义 | calc 函数 | 用途 | 禁用 |
|---|---|---|---|---|
| 累计值(YTD) | 报告期披露原值 | —(证据) | 原料 | 不直接比较不同长度的报告期 |
| 单季 | Q1 = YTD(Q1);Qn = YTD(n) − YTD(n−1) | quarterize(cumulative, unit, money=true) |
基本单位 | 上一期缺失 → 该季无值,不拿平均代替 |
| 最新单季 | 最新非空单季 | latest_quarter(single_quarters, unit, money=true) |
估值分子(扣非)、转向判断 | — |
| TTM | 近 4 季之和 | ttm_sum(single_quarters, end_period, unit, money=true) |
规模、TTM PE | 任一季缺失即 not_meaningful(不拿 3 季凑) |
| TTM 同比 | TTM(end) ÷ TTM(end − 4 季) − 1 | ttm_yoy(...) |
可持续增速事实、交叉验证前瞻 CAGR | 需 8 季连续 |
| 环比 | Q(end) ÷ Q(end − 1) − 1 | qoq(...) |
拐点 / 动量信号 | 禁止当增速、禁止年化、禁止做 PEG 分母 |
| 单季同比 | Q(end) ÷ Q(end − 4) − 1 | growth_rate(current, base, label) |
看"这一季相对去年"的方向 | 禁止做 PEG 分母(低基数假性吹大) |
| 比率 | 两个同期同单位科目之比 | ratio(numerator, denominator, label, unit_in) |
毛利率 / 净利率 / 费用率 / 负债率 / 经营现金流 ÷ 净利润 | 分母 ≤ 0 → not_meaningful,如实写 |
EPS 序列(元/股)用 money=false;金额类 money=true 由 calc 归一单位。
2. 拆解流程(与 SOP financials 阶段一致)
fetch_financials(★ 必需)→ 营收 / 归母 / 扣非 / EPS 的累计值序列(近 8–12 报告期)。quarterize各跑一次:revenue_cum、net_profit_parent_cum、net_profit_deducted_cum(unit=元, money=true)。latest_quarter(扣非)→ 估值分子;ttm_sum(归母与扣非各一次)→ 中间量;ttm_yoy(主用归母,扣非并列)→ 增速事实;qoq(最新单季扣非)→ 拐点信号。- 三表交叉(○ 可选端点):
sina_income_statement/sina_balance_sheet/sina_cashflow(A 股)、em_global_income/em_global_balance/em_global_cashflow或yahoo_financials(US / HK;period= annual 全年 / quarterly 单季 / trailing 近四季,金额按报告币种)。只作核对与补充科目(毛利、费用、经营现金流、应收、存货、有息负债),科目原值按源原样记录,不换算。 - 比率:毛利率 =
ratio(毛利, 营收);净利率 =ratio(净利润, 营收);费用率 =ratio(某费用, 营收);经营现金流 / 净利润 =ratio(经营现金流净额, 净利润);资产负债率 =ratio(总负债, 总资产)——每个比率一次调用,分子分母同期同单位,记 calc id。 - 每次 calc 调用都传
--run-dir(CLI 会把该次计算记录写入运行目录的calcs/,编排器收尾时合并为契约产物calculations.json;不要手工写任何文件),阶段 JSON 引用 calculation_id;缺口走 SOP §2 的结构化 gaps。
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.
- 26d ago Changed · +10 lines fafbd5bbe3ad
- 1mo ago First seen · 73 lines · 216 tokens per session scan A c4a0db40c1e0
earnings-analysis is a skill published in the GitHub repository spy007111/Vibe-Research (8 stars, last pushed 8d ago), licensed MIT. It adds 216 tokens to every session and 2,696 once invoked, about $0.0009 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to earnings-analysis, differing in 0 lines, and is treated as a copy.
earnings-analysis compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| earnings-analysisspy007111/Vibe-Research | Skill | 8 | 8d ago | MIT | A | 216 | Claude Code, Codex |
| sector-rotationHKUDS/Vibe-Trading | Skill | 38k | yesterday | MIT | A | 39 | Claude Code, Codex |
| twitter-readerhimself65/finance-skills | Skill | 3.5k | 2d ago | MIT | A | 110 | Claude Code |
| strategy-pivot-designertradermonty/claude-trading-skills | Skill | 3.7k | 2d ago | MIT | A | 28 | Claude Code, Codex |
| energy-procurementhashgraph-online/awesome-codex-plugins | Skill | 1.5k | yesterday | Apache-2.0 | A | 93 | Claude Code, Codex |
| airdrop-hunting-guidenirholas/three.ws | Skill | 269 | yesterday | Apache-2.0 | A | 54 | Claude Code, Codex |
| multi-expert-analyzerdigoal/blog | Skill | 8.6k | 2d ago | GPL-2.0 | A | 292 | Codex |