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 FTShare-Lab/FTShare-skill --skill ashare-rating-factor-snapshotgit clone --depth 1 https://github.com/FTShare-Lab/FTShare-skillWrote 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/ftshare-lab/ftshare-skill/ashare-rating-factor-snapshot)<a href="https://agentmods.dev/skills/ftshare-lab/ftshare-skill/ashare-rating-factor-snapshot"><img src="https://agentmods.dev/badge/skills/ftshare-lab/ftshare-skill/ashare-rating-factor-snapshot/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/ftshare-lab/ftshare-skill/ashare-rating-factor-snapshot"><img src="https://agentmods.dev/badge/skills/ftshare-lab/ftshare-skill/ashare-rating-factor-snapshot.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 | $0.00035 | $0.00179 |
| Opus 5 | $0.00017 | $0.00089 |
| Sonnet 5 | $0.00007 | $0.00036 |
| Haiku 4.5 | $0.00003 | $0.00018 |
Grade A, and why
ashare-rating-factor-snapshot 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.
What it actually says
A股相关性 Top-K
外部接口：GET /api/v3/market/data/ashare-rating-factor-snapshot
必填：--trade-code。可选：--date（YYYYMMDD）、--top-k（1–500，默认由服务端设为 20）。不分页。
python <RUN_PY> ashare-rating-factor-snapshot --trade-code 600000.SH --date 20260819 --top-k 10
返回 code、message、data，其中 data.related_securities 按相关性分数返回相关证券。
What ships with it
1 file 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.
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 · 17 lines · 35 tokens per session scan A c4b4426b2068
ashare-rating-factor-snapshot is a skill published in the GitHub repository FTShare-Lab/FTShare-skill (63 stars, last pushed 2d ago), licensed MIT. It adds 35 tokens to every session and 179 once invoked, about $0.0002 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.
Other skills, from other repositories
defeatbeta-earnings-analysis
Create professional equity research earnings update reports (8-12 pages, 3,000-5,000 words) analyzing quarterly results for companies already under coverage. Fast-turnaround format focusing on beat/miss analysis, key metrics, updated estimates, and revised thesis. Includes 1-3 summary tables and 8-12 charts. Use when…
sprr
Single PR reviewer for awesome-quant. Use when the user asks to review, validate, comment on, label, close, or merge one specific pull request that adds README.md entries. Triggers include "sprr", "review PR", "check PR", and "validate contribution".
bprr
Bulk PR reviewer for awesome-quant. Use when the user asks to review all open PRs, review unreviewed PRs, bulk review, or mentions "bprr". Reviews open PRs lacking the reviewed label and presents a summary before any merge/comment/label action.
update-pypi-dates
Refresh tracked PyPI last-updated dates in awesome-quant README.md. Use when the user asks to update PyPI dates, refresh PyPI metadata, or run update-pypi-dates.
defeatbeta-earnings-preview
Build pre-earnings analysis with normalized baselines, weighted decision models, company-specific veto gates, scenario frameworks, catalysts, historical reactions, and options-implied moves. Use before a company reports quarterly earnings to prepare positioning notes or bilingual three-page PDF reports.
defeatbeta-earnings-key-data
Run one of three traceable earnings-call transcript templates through the DefeatBeta MCP tools: (1) extract current-quarter results plus next-quarter and full-year guidance, (2) extract factual QoQ or YoY financial-metric change sentences, or (3) extract numerical forward-looking forecast sentences with…