intraday-auction-volume

intraday-auction-volume is a skill for Claude Code, Codex from FTShare-Lab/FTShare-skill. It costs 34 tokens per session (181 once invoked), scanned A, original, MIT.

A query for a single stock's minute-by-minute trading volume and transaction-value share during continuous auction trading, either today or on a past trading day.

In plain words
What is it for?
Use it to inspect intraday trading concentration for one stock and retrieve the results page by page.
Why use it?
It provides a focused view of when and how much of a stock's trading took place during the session.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to inspect intraday trading concentration for one stock and retrieve the results page by page.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/ftshare-lab/ftshare-skill/intraday-auction-volume
Install

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.

Any agent
npx skills add FTShare-Lab/FTShare-skill --skill intraday-auction-volume
Clone the repo
git clone --depth 1 https://github.com/FTShare-Lab/FTShare-skill

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for intraday-auction-volume

README.md
[![agentmods](https://agentmods.dev/badge/skills/ftshare-lab/ftshare-skill/intraday-auction-volume/github.svg)](https://agentmods.dev/skills/ftshare-lab/ftshare-skill/intraday-auction-volume)
Your own site
<a href="https://agentmods.dev/skills/ftshare-lab/ftshare-skill/intraday-auction-volume"><img src="https://agentmods.dev/badge/skills/ftshare-lab/ftshare-skill/intraday-auction-volume/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.

agentmods 80×15 button for intraday-auction-volume

Your own site · 80×15
<a href="https://agentmods.dev/skills/ftshare-lab/ftshare-skill/intraday-auction-volume"><img src="https://agentmods.dev/badge/skills/ftshare-lab/ftshare-skill/intraday-auction-volume.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 34 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 181 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00034 $0.00181
Opus 5 $0.00017 $0.00090
Sonnet 5 $0.00007 $0.00036
Haiku 4.5 $0.00003 $0.00018

Measured 6d ago against content hash fa90d0fa2347, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

intraday-auction-volume 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.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/handler.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

ftshare-market-data/sub-skills/intraday-auction-volume/SKILL.md · 15 lines

What it actually says

单标的连续竞价成交量

接口:GET /api/v1/market/data/intraday-auction-volume/symbol。必填 --symbol;可选 --trade-date(YYYYMMDD)、--page--page-size,每页最多 200 条。

python <RUN_PY> intraday-auction-volume --symbol 600000.SH --page 1 --page-size 50

不传交易日查询当日实时数据,传入历史交易日查询历史分钟数据;响应为 code/message/data 分页信封,分钟记录位于 data.records

Files

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.

Changes

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.

  1. 6d ago First seen · 15 lines · 34 tokens per session scan A fa90d0fa2347

Subscribe to this mod's changes

intraday-auction-volume is a skill published in the GitHub repository FTShare-Lab/FTShare-skill (63 stars, last pushed 2d ago), licensed MIT. It adds 34 tokens to every session and 181 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.

Related

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…

defeat-beta/defeatbeta-api · 112 tokens

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".

wilsonfreitas/awesome-quant · 60 tokens

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.

wilsonfreitas/awesome-quant · 61 tokens

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.

wilsonfreitas/awesome-quant · 47 tokens

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.

defeat-beta/defeatbeta-api · 60 tokens

defeatbeta-analyst

Professional financial analysis using 60+ market data APIs. Use for: company fundamentals (revenue, margins, EPS, balance sheet), valuation (P/E, P/B, P/S, PEG, DCF, intrinsic value), profitability (ROE, ROA, ROIC), growth trends (YoY revenue/earnings/FCF), earnings transcripts (key data, changes, guidance), industry…

defeat-beta/defeatbeta-api · 154 tokens