Tushare Data
zillionare/zillionare
面向中文自然语言的 Tushare 数据研究技能。用于把“看看这只股票最近怎么样”“帮我查财报趋势”“最近哪个板块最强”“北向资金在买什么”“给我导出一份行情数据”这类请求，转成可执行的数据获取、清洗、对比、筛选、导出与简要分析流程。适用于 A 股、指数、ETF/基金、财务、估值、资金流、公告新闻、板块概念与宏观数据等研究场景。
An overview of the core data API of ObsPy, a Python framework for processing seismological data.
Apache-2.0Auto-check passed
Install Obspy Data API
$ npx skills add benchflow-ai/skillsbench --skill obspy-data-api -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install benchflow-ai/skillsbench obspy-data-api --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/benchflow-ai/skillsbench.git skills-src && mkdir -p .claude/skills && cp -r skills-src/tasks/seismic-phase-picking/environment/skills/obspy-data-api .claude/skills/obspy-data-api && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "obspy-data-api" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/seismic-phase-picking/environment/skills/obspy-data-api into .claude/skills/obspy-data-api/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "obspy-data-api", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/benchflow-ai/skillsbench/tree/main/tasks/seismic-phase-picking/environment/skills/obspy-data-apiType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add benchflow-ai/skillsbench --skill obspy-data-api -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install benchflow-ai/skillsbench obspy-data-api --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/benchflow-ai/skillsbench.git skills-src && mkdir -p .agents/skills && cp -r skills-src/tasks/seismic-phase-picking/environment/skills/obspy-data-api .agents/skills/obspy-data-api && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "obspy-data-api" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/seismic-phase-picking/environment/skills/obspy-data-api into .agents/skills/obspy-data-api/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "obspy-data-api", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add benchflow-ai/skillsbench --skill obspy-data-api -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install benchflow-ai/skillsbench obspy-data-api --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/benchflow-ai/skillsbench.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/tasks/seismic-phase-picking/environment/skills/obspy-data-api .cursor/skills/obspy-data-api && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "obspy-data-api" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/seismic-phase-picking/environment/skills/obspy-data-api into .cursor/skills/obspy-data-api/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "obspy-data-api", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/benchflow-ai/skillsbench.git --path tasks/seismic-phase-picking/environment/skills/obspy-data-api--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add benchflow-ai/skillsbench --skill obspy-data-api -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install benchflow-ai/skillsbench obspy-data-api --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/benchflow-ai/skillsbench.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/tasks/seismic-phase-picking/environment/skills/obspy-data-api .gemini/skills/obspy-data-api && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "obspy-data-api" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/seismic-phase-picking/environment/skills/obspy-data-api into .gemini/skills/obspy-data-api/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "obspy-data-api", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install benchflow-ai/skillsbench obspy-data-apiInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add benchflow-ai/skillsbench --skill obspy-data-api -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/benchflow-ai/skillsbench.git skills-src && mkdir -p .github/skills && cp -r skills-src/tasks/seismic-phase-picking/environment/skills/obspy-data-api .github/skills/obspy-data-api && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "obspy-data-api" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/seismic-phase-picking/environment/skills/obspy-data-api into .github/skills/obspy-data-api/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "obspy-data-api", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add benchflow-ai/skillsbench --skill obspy-data-api -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install benchflow-ai/skillsbench obspy-data-api --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/benchflow-ai/skillsbench.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/tasks/seismic-phase-picking/environment/skills/obspy-data-api .opencode/skills/obspy-data-api && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "obspy-data-api" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/seismic-phase-picking/environment/skills/obspy-data-api into .opencode/skills/obspy-data-api/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "obspy-data-api", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
obspy-data-api- GitHub stars
- 1.8k
- Token cost
- ~1.4k tokens
- SKILL.md length
- 478 words
- Files
- 1
- Skills in repo
- 180
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
An overview of the core data API of ObsPy, a Python framework for processing seismological data.
- SKILL.md covers Waveform Data, Event Metadata, Station Metadata and Classes & Functions, plus 1 more section
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Obspy Data API is an agent skill from benchflow-ai/skillsbench. An overview of the core data API of ObsPy, a Python framework for processing seismological data. It is useful for parsing common seismological file formats, or manipulating custom data into standard objects for downstream use cases such as ObsPy's signal processing routines or SeisBench's modeling API.
Its SKILL.md is about 1.4k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It works with Python and NumPy. The repository describes itself as: SkillsBench evaluates how well skills work and how effective agents are at using them. The licence is Apache-2.0.
Example prompts
- “s signal processing routines or SeisBench”
- “/obspy-data-api”
Requirements
- Python 3
What it can do on your machine
Read from SKILL.md and the folder at commit 9a1f4dd. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md (its code samples are python).
From the folder's file list and the shell code blocks in SKILL.md.
Network
Links to these hosts (documentation or services it may open):
examples.obspy.orgquake.ethz.chfdsn.org
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Obspy Data API loads about 1.4k tokens when it runs. Until then it costs about 80 tokens; SKILL.md has 478 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from benchflow-ai/skillsbench at commit 9a1f4dd, republished under its Apache-2.0 licence (© benchflow-ai). 478 words, ~1,362 tokens.
Download SKILL.mdSave it as
.claude/skills/obspy-data-api/SKILL.md (or your agent's skills folder).- name
- obspy-data-api
- description
- An overview of the core data API of ObsPy, a Python framework for processing seismological data. It is useful for parsing common seismological file formats, or manipulating custom data into standard objects for downstream use cases such as ObsPy's signal processing routines or SeisBench's modeling API.
ObsPy Data API
Waveform Data
Summary
Seismograms of various formats (e.g. SAC, MiniSEED, GSE2, SEISAN, Q, etc.) can be imported into a Stream object using the read() function.
Streams are list-like objects which contain multiple Trace objects, i.e. gap-less continuous time series and related header/meta information.
Each Trace object has the attribute data pointing to a NumPy ndarray of the actual time series and the attribute stats which contains all meta information in a dict-like Stats object. Both attributes starttime and endtime of the Stats object are UTCDateTime objects.
A multitude of helper methods are attached to Stream and Trace objects for handling and modifying the waveform data.
Stream and Trace Class Structure
Hierarchy: Stream → Trace (multiple)
Trace - DATA:
data→ NumPy arraystats:network,station,location,channel— Determine physical location and instrumentstarttime,sampling_rate,delta,endtime,npts— Interrelated
Trace - METHODS:
taper()— Tapers the data.filter()— Filters the data.resample()— Resamples the data in the frequency domain.integrate()— Integrates the data with respect to time.remove_response()— Deconvolves the instrument response.
Example
A Stream with an example seismogram can be created by calling read() without any arguments. Local files can be read by specifying the filename, files stored on http servers (e.g. at https://examples.obspy.org) can be read by specifying their URL.
>>> from obspy import read
>>> st = read()
>>> print(st)
3 Trace(s) in Stream:
BW.RJOB..EHZ | 2009-08-24T00:20:03.000000Z - ... | 100.0 Hz, 3000 samples
BW.RJOB..EHN | 2009-08-24T00:20:03.000000Z - ... | 100.0 Hz, 3000 samples
BW.RJOB..EHE | 2009-08-24T00:20:03.000000Z - ... | 100.0 Hz, 3000 samples
>>> tr = st[0]
>>> print(tr)
BW.RJOB..EHZ | 2009-08-24T00:20:03.000000Z - ... | 100.0 Hz, 3000 samples
>>> tr.data
array([ 0. , 0.00694644, 0.07597424, ..., 1.93449584,
0.98196204, 0.44196924])
>>> print(tr.stats)
network: BW
station: RJOB
location:
channel: EHZ
starttime: 2009-08-24T00:20:03.000000Z
endtime: 2009-08-24T00:20:32.990000Z
sampling_rate: 100.0
delta: 0.01
npts: 3000
calib: 1.0
...
>>> tr.stats.starttime
UTCDateTime(2009, 8, 24, 0, 20, 3)Event Metadata
Event metadata are handled in a hierarchy of classes closely modelled after the de-facto standard format QuakeML. See read_events() and Catalog.write() for supported formats.
Event Class Structure
Hierarchy: Catalog → events → Event (multiple)
Event contains:
origins→Origin(multiple)latitude,longitude,depth,time, ...
magnitudes→Magnitude(multiple)mag,magnitude_type, ...
picksfocal_mechanisms
Station Metadata
Station metadata are handled in a hierarchy of classes closely modelled after the de-facto standard format FDSN StationXML which was developed as a human readable XML replacement for Dataless SEED. See read_inventory() and Inventory.write() for supported formats.
Show full SKILL.md (180 more words)Show less
Inventory Class Structure
Hierarchy: Inventory → networks → Network → stations → Station → channels → Channel
Network:
code,description, ...
Station:
code,latitude,longitude,elevation,start_date,end_date, ...
Channel:
code,location_code,latitude,longitude,elevation,depth,dip,azimuth,sample_rate,start_date,end_date,response, ...
Classes & Functions
| Class/Function | Description |
|---|---|
read | Read waveform files into an ObsPy Stream object. |
Stream | List-like object of multiple ObsPy Trace objects. |
Trace | An object containing data of a continuous series, such as a seismic trace. |
Stats | A container for additional header information of an ObsPy Trace object. |
UTCDateTime | A UTC-based datetime object. |
read_events | Read event files into an ObsPy Catalog object. |
Catalog | Container for Event objects. |
Event | Describes a seismic event which does not necessarily need to be a tectonic earthquake. |
read_inventory | Function to read inventory files. |
Inventory | The root object of the Network → Station → Channel hierarchy. |
Modules
| Module | Description |
|---|---|
obspy.core.trace | Module for handling ObsPy Trace and Stats objects. |
obspy.core.stream | Module for handling ObsPy Stream objects. |
obspy.core.utcdatetime | Module containing a UTC-based datetime class. |
obspy.core.event | Module handling event metadata. |
obspy.core.inventory | Module for handling station metadata. |
obspy.core.util | Various utilities for ObsPy. |
obspy.core.preview | Tools for creating and merging previews. |
© benchflow-ai, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in tasks/seismic-phase-picking/environment/skills/obspy-data-api of benchflow-ai/skillsbench.
Open the folder on GitHubat commit 9a1f4dd
Compare with similar skills
Obspy Data API next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Obspy Data API this skillbenchflow-ai/skillsbench | 1.8k | — | ~1.4k | Automated safety check: Pass | Apache-2.0 | |
| Tushare Datazillionare/zillionare | 318 | 2 repos | ~2.3k | Automated safety check: Pass | None | |
| FAISS Similarity SearchOrchestra-Research/AI-Research-SKILLs | 13k | 7 repos | ~1.3k | Automated safety check: Pass | MIT | |
| 13C Metabolic Flux AnalysisK-Dense-AI/scientific-agent-skills | 48k | 1 repos | ~3.2k | Automated safety check: Pass | MIT | |
| Python Performance Optimizationwshobson/agents | 40k | 12 repos | ~814 | Automated safety check: Pass | MIT | |
| UAV Trajectory Overlay from VideoXXLiu-HNU/visualize_uav_trajectory | 242 | — | ~535 | Automated safety check: Pass | GPL-3.0 |
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Related
Questions about Obspy Data API
What does Obspy Data API do?
An overview of the core data API of ObsPy, a Python framework for processing seismological data. Obspy Data API is an agent skill from benchflow-ai/skillsbench. An overview of the core data API of ObsPy, a Python framework for processing seismological data.
How do I install Obspy Data API in Claude Code?
Run `npx skills add benchflow-ai/skillsbench --skill obspy-data-api -a claude-code`. Or copy the skill folder (tasks/seismic-phase-picking/environment/skills/obspy-data-api in benchflow-ai/skillsbench) into .claude/skills/obspy-data-api in your project. Claude Code loads it when a task matches its description.
How do I install Obspy Data API in Codex?
Run `npx skills add benchflow-ai/skillsbench --skill obspy-data-api -a codex`. Or copy the skill folder (tasks/seismic-phase-picking/environment/skills/obspy-data-api in benchflow-ai/skillsbench) into .agents/skills/obspy-data-api in your project. Codex loads it when a task matches its description.
Can I use Obspy Data API in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add benchflow-ai/skillsbench --skill obspy-data-api -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/obspy-data-api, .gemini/skills/obspy-data-api, .github/skills/obspy-data-api and .opencode/skills/obspy-data-api in your project.
What does Obspy Data API need to run?
SKILL.md names no scripts, command-line tools or credentials: Obspy Data API is instructions for the agent only. Our summary lists: Python 3.
Does Obspy Data API access the network?
SKILL.md names 3 domains. As links in the text: examples.obspy.org, quake.ethz.ch and fdsn.org. This is read from the text; nothing was executed.
Is Obspy Data API safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Obspy Data API use?
Obspy Data API is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Obspy Data API use?
About 1.4k tokens (SKILL.md is roughly 5.4k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Obspy Data API?
Skills that share tags, products or a category with Obspy Data API: Tushare Data (zillionare/zillionare, 318 stars), FAISS Similarity Search (Orchestra-Research/AI-Research-SKILLs, 13k stars), 13C Metabolic Flux Analysis (K-Dense-AI/scientific-agent-skills, 48k stars) and Python Performance Optimization (wshobson/agents, 40k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Obspy Data API?
benchflow-ai (a GitHub organization) maintains it in benchflow-ai/skillsbench, which has 1,832 GitHub stars. The repository holds 180 skills in this directory. The repository was last updated on July 23, 2026.
Source: benchflow-ai/skillsbench on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.