Creator · WenjieDu
Last updated · Sep 1, 2026
A Python toolkit/library for reality-centric machine/deep learning & data mining on partially-observed time series, with 50+ SOTA neural network models for scientific analysis tasks (imputation, classification, clustering, forecasting, anomaly detection, cleaning) on incomplete industrial irregularly-sampled multivariate TS with NaN missing values
Creator · WenjieDu
Last updated · Sep 1, 2026
A Python toolkit/library for reality-centric machine/deep learning & data mining on partially-observed time series, with 50+ SOTA neural network models for scientific analysis tasks (imputation, classification, clustering, forecasting, anomaly detection, cleaning) on incomplete industrial irregularly-sampled multivariate TS with NaN missing values
Creator · WenjieDu
Last updated · Sep 1, 2026
A Python toolkit/library for reality-centric machine/deep learning & data mining on partially-observed time series, with 50+ SOTA neural network models for scientific analysis tasks (imputation, classification, clustering, forecasting, anomaly detection, cleaning) on incomplete industrial irregularly-sampled multivariate TS with NaN missing values
Creator · WenjieDu
Last updated · Sep 1, 2026
A Python toolkit/library for reality-centric machine/deep learning & data mining on partially-observed time series, with 50+ SOTA neural network models for scientific analysis tasks (imputation, classification, clustering, forecasting, anomaly detection, cleaning) on incomplete industrial irregularly-sampled multivariate TS with NaN missing values
Review then install
Install targets
Codex install prompt
Install the "PyPOTS" agent skill from https://github.com/WenjieDu/PyPOTS. Read its SKILL.md or equivalent instructions first, install only the files needed for this workspace, and summarize any required setup before using it. Skill purpose: A Python toolkit/library for reality-centric machine/deep learning & data mining on partially-observed time series, with 50+ SOTA neural network models for scientific analysis tasks (imputation, classification, clustering, forecasting, anomaly detection, cleaning) on incomplete industrial irregularly-sampled multivariate TS with NaN missing values After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {"event_id":"install_<unique-id>","skill_slug":"wenjiedu-pypots","task":"Install PyPOTS","agent":"codex","outcome":"success","install_used":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes.Supply asset profile
CSV, SQL, notebooks, dashboards, data pipelines, BI, ETL, and spreadsheet analysis.
Scenario
Data analysis
I need my agent to analyze CSV data, produce insights, and explain trends.
Agent fit
Claude Code + CLI + Codex
Codex, Claude Code, Cursor, CLI, or custom agents.
Install
Ready
npx skills add WenjieDu/PyPOTS
Maintenance
active
2mo since push
Risk
Needs review
Financial research output is not financial advice; require human review before any live investment decision
GitHub quality
2.0K
100/100 Quality · 88/100 Trust
Coverage tags
Review notes
Financial research output is not financial advice; require human review before any live investment decision · Financial research output is not financial advice; require human review before any live investment decision.
Agent adoption scorecard
These scores combine public repository metadata, OpenAgentSkill review signals, maintenance freshness, and install readiness. They are a shortlist signal, not a replacement for human review.
Quality
ExcellentHigh-confidence pick with strong adoption and healthy maintenance signals.
Trust
Review then installGood shortlist signal, but the agent should review audit notes, install policy, and outcome evidence before running it.
Audit
Needs reviewA machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
OpenAgentSkill Trust Score v5
Use as the primary candidate after human or sandbox review.
Stars
2.0K GitHub stars
Repo activity
2.0K stars, 187 forks
Maintenance
2mo since push
License
BSD-3-Clause
Install
npx skills add WenjieDu/PyPOTS
Install safety
Agent-readable metadata
Use this block or the embedded JSON to decide whether an agent should install this skill, choose an alternative, or ask for human review first.
Suited tasks
Suited agents
Install decision
Trust and risk
Outcome loop
Do not use when
Agent safety v2
Usable candidate, but the agent should surface permission and audit notes before installation.
Require human approval before installing into a real workspace.
medium
Skill likely fetches remote pages, APIs, repositories, or external services.
medium
Skill may read or write project files, documents, generated artifacts, or local workspace state.
Agent resolve plan
The Resolve API returns the selected skill, alternatives, safety policy, audit notes, install target, and copy-paste prompt an agent can follow without scraping this page.
Open JSON
/api/agent/resolve?task=Use%20PyPOTS%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve text
/api/agent/resolve?task=Use%20PyPOTS%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Install handoff
/api/skills/wenjiedu-pypots/install
Agent should check
Copy prompt
Task: Use PyPOTS in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20PyPOTS%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/wenjiedu-pypots/install
Install command: npx skills add WenjieDu/PyPOTS
Before running it, summarize audit warnings, required permissions, and the fallback skill if install is risky.Agent handoff
Use the public install endpoint to fetch the command, safety checklist, target prompts, and canonical links for this skill.
Install handoff
/api/skills/wenjiedu-pypots/install
LLM text format
/api/skills/wenjiedu-pypots/install?format=text
Find alternatives
/api/skills/search?q=PyPOTS&limit=3
Agent prompt
Use PyPOTS for this task. Review https://www.openagentskill.com/api/skills/wenjiedu-pypots/install, then install with: npx skills add WenjieDu/PyPOTSRegistry metadata
This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.
Manifest
/api/registry/manifest/wenjiedu-pypots
LLM text
/api/registry/manifest/wenjiedu-pypots?format=text
Install alias
/api/registry/install/wenjiedu-pypots
Recommend
/api/registry/recommend?task=Use%20PyPOTS%20in%20an%20agent%20workflow&limit=3
Agent fit
Workflow automation
Use-case tags
Platforms
Python, Data Analysis, Claude Code
Audit report
A machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
Agent decision cockpit
Use this as a leading candidate, then validate the README and install path in your own agent stack.
Role in stack
Primary pick
Primary fit
Workflow automation
Trust label
Production-ready
Install path
Command ready
Use when
Evidence
review first
Implementation path
Trust profile
Good shortlist signal, but the agent should review audit notes, install policy, and outcome evidence before running it.
GitHub adoption
PASS2.0K GitHub stars
Stars/forks activity
INFO2.0K stars, 187 forks; issue activity unavailable in current metadata
Recent maintenance
PASS2mo since push
License clarity
PASSBSD-3-Clause
Good signals
Review before install
Recommended action
Use as the primary candidate after human or sandbox review.
Quality profile
High-confidence pick with strong adoption and healthy maintenance signals.
Workflow fit
Automate repeated work
I need my agent to automate a repeated workflow across tools and files.
Manage repositories
I need my agent to triage GitHub issues, review pull requests, and summarize repository changes.
Analyze datasets
I need my agent to analyze CSV data, produce insights, and explain trends.
Workflow fit
Turn skills into distribution
A workflow for turning newly indexed skills into SEO briefs, social drafts, comparison pages, and reusable publishing workflows.
Inspect, patch, and verify code
A workflow for software agents that inspect repositories, review pull requests, generate tests, and turn findings into shippable patches.
Ingest, retrieve, and cite
A workflow for document-heavy agents that ingest files, create searchable knowledge, retrieve relevant context, and answer with grounded sources.
Alternative shortlist
Similar skills that may fit this task.
Apache ECharts is a powerful, interactive charting and data visualization library for browser
Bring data to life with SVG, Canvas and HTML. :bar_chart::chart_with_upwards_trend::tada:
Turn any AI agent into an AI Scientist. The #1 Agent Skills library for science, used by 160,000+ scientists worldwide. 140 ready-to-use skills plus 100+ scientific databases covering biology, chemistry, medicine, and drug discovery. Compatible with Cursor, Claude Code, Codex, Pi, Antigravity, and the open Agent Skills standard.
Apache Superset is a Data Visualization and Data Exploration Platform
A Python toolkit/library for reality-centric machine/deep learning & data mining on partially-observed time series, with 50+ SOTA neural network models for scientific analysis tasks (imputation, classification, clustering, forecasting, anomaly detection, cleaning) on incomplete industrial irregularly-sampled multivariate TS with NaN missing values
Imported by the skill-only GitHub discovery pipeline because it matches agent skill, automation, domain workflow, RAG, document-processing, data, finance, security, or developer-tool signals. Protocol-server projects are excluded from automated imports.
Frameworks & tools
Decision snapshot
2,037 GitHub stars
Audit
Install and adoption review
Agent-proven evidence
Outcome reports after resolve, review, install, and one narrow run.
No agent outcome data yet. The first agent run can report success, setup needs, risk blocks, failure, or not-relevant through /api/agent/outcome.
Install
Free and open source. Review the report before installing into production agents.
Growth loop
Scenario-led draft for PyPOTS, ready for a manual X post.
PyPOTS: A Python toolkit/library for reality-centric machine/deep learning & data mining on partially... 2.0K stars https://www.openagentskill.com/skills/wenjiedu-pypots?ref=x
Listing + install path for PyPOTS: https://www.openagentskill.com/skills/wenjiedu-pypots?ref=x Install: npx skills add WenjieDu/PyPOTS
Listing source
This listing was indexed from public sources and is not marked official until a maintainer claim is approved.
Attribution links to the public repository or creator profile. Creators can claim the listing to update ownership signals.
Claim this skillOwner claim
This Community indexed listing is attributed to WenjieDu but is not marked official yet. Claim it to add a verified owner signal and make future launch, install, and audit updates easier to trust.
Creator backlink kit
Show the canonical listing, current trust and audit signals, and real Agent-Proven evidence where developers evaluate the repository.
[](https://www.openagentskill.com/skills/wenjiedu-pypots?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[](https://www.openagentskill.com/skills/wenjiedu-pypots?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[](https://www.openagentskill.com/skills/wenjiedu-pypots/audit)
[](https://www.openagentskill.com/skills/wenjiedu-pypots?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)WenjieDu✓
@wenjiedu
Share whether this skill looks useful for your agent workflow. Aggregated feedback improves rankings over time.
Review then install
Echarts
Apache ECharts is a powerful, interactive charting and data visualization library for browser
67.2K StarsD3
Bring data to life with SVG, Canvas and HTML. :bar_chart::chart_with_upwards_trend::tada:
113.1K StarsScientific Agent Skills
Turn any AI agent into an AI Scientist. The #1 Agent Skills library for science, used by 160,000+ scientists worldwide. 140 ready-to-use skills plus 100+ scientific databases covering biology, chemistry, medicine, and drug discovery. Compatible with Cursor, Claude Code, Codex, Pi, Antigravity, and the open Agent Skills standard.
33.5K StarsSuperset
Apache Superset is a Data Visualization and Data Exploration Platform
74.7K StarsReview then install
Install targets
Codex install prompt
Install the "PyPOTS" agent skill from https://github.com/WenjieDu/PyPOTS. Read its SKILL.md or equivalent instructions first, install only the files needed for this workspace, and summarize any required setup before using it. Skill purpose: A Python toolkit/library for reality-centric machine/deep learning & data mining on partially-observed time series, with 50+ SOTA neural network models for scientific analysis tasks (imputation, classification, clustering, forecasting, anomaly detection, cleaning) on incomplete industrial irregularly-sampled multivariate TS with NaN missing values After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {"event_id":"install_<unique-id>","skill_slug":"wenjiedu-pypots","task":"Install PyPOTS","agent":"codex","outcome":"success","install_used":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes.Supply asset profile
CSV, SQL, notebooks, dashboards, data pipelines, BI, ETL, and spreadsheet analysis.
Scenario
Data analysis
I need my agent to analyze CSV data, produce insights, and explain trends.
Agent fit
Claude Code + CLI + Codex
Codex, Claude Code, Cursor, CLI, or custom agents.
Install
Ready
npx skills add WenjieDu/PyPOTS
Maintenance
active
2mo since push
Risk
Needs review
Financial research output is not financial advice; require human review before any live investment decision
GitHub quality
2.0K
100/100 Quality · 88/100 Trust
Coverage tags
Review notes
Financial research output is not financial advice; require human review before any live investment decision · Financial research output is not financial advice; require human review before any live investment decision.
Agent adoption scorecard
These scores combine public repository metadata, OpenAgentSkill review signals, maintenance freshness, and install readiness. They are a shortlist signal, not a replacement for human review.
Quality
ExcellentHigh-confidence pick with strong adoption and healthy maintenance signals.
Trust
Review then installGood shortlist signal, but the agent should review audit notes, install policy, and outcome evidence before running it.
Audit
Needs reviewA machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
OpenAgentSkill Trust Score v5
Use as the primary candidate after human or sandbox review.
Stars
2.0K GitHub stars
Repo activity
2.0K stars, 187 forks
Maintenance
2mo since push
License
BSD-3-Clause
Install
npx skills add WenjieDu/PyPOTS
Install safety
Agent-readable metadata
Use this block or the embedded JSON to decide whether an agent should install this skill, choose an alternative, or ask for human review first.
Suited tasks
Suited agents
Install decision
Trust and risk
Outcome loop
Do not use when
Agent safety v2
Usable candidate, but the agent should surface permission and audit notes before installation.
Require human approval before installing into a real workspace.
medium
Skill likely fetches remote pages, APIs, repositories, or external services.
medium
Skill may read or write project files, documents, generated artifacts, or local workspace state.
Agent resolve plan
The Resolve API returns the selected skill, alternatives, safety policy, audit notes, install target, and copy-paste prompt an agent can follow without scraping this page.
Open JSON
/api/agent/resolve?task=Use%20PyPOTS%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve text
/api/agent/resolve?task=Use%20PyPOTS%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Install handoff
/api/skills/wenjiedu-pypots/install
Agent should check
Copy prompt
Task: Use PyPOTS in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20PyPOTS%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/wenjiedu-pypots/install
Install command: npx skills add WenjieDu/PyPOTS
Before running it, summarize audit warnings, required permissions, and the fallback skill if install is risky.Agent handoff
Use the public install endpoint to fetch the command, safety checklist, target prompts, and canonical links for this skill.
Install handoff
/api/skills/wenjiedu-pypots/install
LLM text format
/api/skills/wenjiedu-pypots/install?format=text
Find alternatives
/api/skills/search?q=PyPOTS&limit=3
Agent prompt
Use PyPOTS for this task. Review https://www.openagentskill.com/api/skills/wenjiedu-pypots/install, then install with: npx skills add WenjieDu/PyPOTSRegistry metadata
This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.
Manifest
/api/registry/manifest/wenjiedu-pypots
LLM text
/api/registry/manifest/wenjiedu-pypots?format=text
Install alias
/api/registry/install/wenjiedu-pypots
Recommend
/api/registry/recommend?task=Use%20PyPOTS%20in%20an%20agent%20workflow&limit=3
Agent fit
Workflow automation
Use-case tags
Platforms
Python, Data Analysis, Claude Code
Audit report
A machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
Agent decision cockpit
Use this as a leading candidate, then validate the README and install path in your own agent stack.
Role in stack
Primary pick
Primary fit
Workflow automation
Trust label
Production-ready
Install path
Command ready
Use when
Evidence
review first
Implementation path
Trust profile
Good shortlist signal, but the agent should review audit notes, install policy, and outcome evidence before running it.
GitHub adoption
PASS2.0K GitHub stars
Stars/forks activity
INFO2.0K stars, 187 forks; issue activity unavailable in current metadata
Recent maintenance
PASS2mo since push
License clarity
PASSBSD-3-Clause
Good signals
Review before install
Recommended action
Use as the primary candidate after human or sandbox review.
Quality profile
High-confidence pick with strong adoption and healthy maintenance signals.
Workflow fit
Automate repeated work
I need my agent to automate a repeated workflow across tools and files.
Manage repositories
I need my agent to triage GitHub issues, review pull requests, and summarize repository changes.
Analyze datasets
I need my agent to analyze CSV data, produce insights, and explain trends.
Workflow fit
Turn skills into distribution
A workflow for turning newly indexed skills into SEO briefs, social drafts, comparison pages, and reusable publishing workflows.
Inspect, patch, and verify code
A workflow for software agents that inspect repositories, review pull requests, generate tests, and turn findings into shippable patches.
Ingest, retrieve, and cite
A workflow for document-heavy agents that ingest files, create searchable knowledge, retrieve relevant context, and answer with grounded sources.
Alternative shortlist
Similar skills that may fit this task.
Apache ECharts is a powerful, interactive charting and data visualization library for browser
Bring data to life with SVG, Canvas and HTML. :bar_chart::chart_with_upwards_trend::tada:
Turn any AI agent into an AI Scientist. The #1 Agent Skills library for science, used by 160,000+ scientists worldwide. 140 ready-to-use skills plus 100+ scientific databases covering biology, chemistry, medicine, and drug discovery. Compatible with Cursor, Claude Code, Codex, Pi, Antigravity, and the open Agent Skills standard.
Apache Superset is a Data Visualization and Data Exploration Platform
A Python toolkit/library for reality-centric machine/deep learning & data mining on partially-observed time series, with 50+ SOTA neural network models for scientific analysis tasks (imputation, classification, clustering, forecasting, anomaly detection, cleaning) on incomplete industrial irregularly-sampled multivariate TS with NaN missing values
Imported by the skill-only GitHub discovery pipeline because it matches agent skill, automation, domain workflow, RAG, document-processing, data, finance, security, or developer-tool signals. Protocol-server projects are excluded from automated imports.
Frameworks & tools
Decision snapshot
2,037 GitHub stars
Audit
Install and adoption review
Agent-proven evidence
Outcome reports after resolve, review, install, and one narrow run.
No agent outcome data yet. The first agent run can report success, setup needs, risk blocks, failure, or not-relevant through /api/agent/outcome.
Install
Free and open source. Review the report before installing into production agents.
Growth loop
Scenario-led draft for PyPOTS, ready for a manual X post.
PyPOTS: A Python toolkit/library for reality-centric machine/deep learning & data mining on partially... 2.0K stars https://www.openagentskill.com/skills/wenjiedu-pypots?ref=x
Listing + install path for PyPOTS: https://www.openagentskill.com/skills/wenjiedu-pypots?ref=x Install: npx skills add WenjieDu/PyPOTS
Listing source
This listing was indexed from public sources and is not marked official until a maintainer claim is approved.
Attribution links to the public repository or creator profile. Creators can claim the listing to update ownership signals.
Claim this skillOwner claim
This Community indexed listing is attributed to WenjieDu but is not marked official yet. Claim it to add a verified owner signal and make future launch, install, and audit updates easier to trust.
Creator backlink kit
Show the canonical listing, current trust and audit signals, and real Agent-Proven evidence where developers evaluate the repository.
[](https://www.openagentskill.com/skills/wenjiedu-pypots?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[](https://www.openagentskill.com/skills/wenjiedu-pypots?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[](https://www.openagentskill.com/skills/wenjiedu-pypots/audit)
[](https://www.openagentskill.com/skills/wenjiedu-pypots?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)WenjieDu✓
@wenjiedu
Share whether this skill looks useful for your agent workflow. Aggregated feedback improves rankings over time.
Review then install
Echarts
Apache ECharts is a powerful, interactive charting and data visualization library for browser
67.2K StarsD3
Bring data to life with SVG, Canvas and HTML. :bar_chart::chart_with_upwards_trend::tada:
113.1K StarsScientific Agent Skills
Turn any AI agent into an AI Scientist. The #1 Agent Skills library for science, used by 160,000+ scientists worldwide. 140 ready-to-use skills plus 100+ scientific databases covering biology, chemistry, medicine, and drug discovery. Compatible with Cursor, Claude Code, Codex, Pi, Antigravity, and the open Agent Skills standard.
33.5K StarsSuperset
Apache Superset is a Data Visualization and Data Exploration Platform
74.7K StarsReview then install
Install targets
Codex install prompt
Install the "PyPOTS" agent skill from https://github.com/WenjieDu/PyPOTS. Read its SKILL.md or equivalent instructions first, install only the files needed for this workspace, and summarize any required setup before using it. Skill purpose: A Python toolkit/library for reality-centric machine/deep learning & data mining on partially-observed time series, with 50+ SOTA neural network models for scientific analysis tasks (imputation, classification, clustering, forecasting, anomaly detection, cleaning) on incomplete industrial irregularly-sampled multivariate TS with NaN missing values After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {"event_id":"install_<unique-id>","skill_slug":"wenjiedu-pypots","task":"Install PyPOTS","agent":"codex","outcome":"success","install_used":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes.Supply asset profile
CSV, SQL, notebooks, dashboards, data pipelines, BI, ETL, and spreadsheet analysis.
Scenario
Data analysis
I need my agent to analyze CSV data, produce insights, and explain trends.
Agent fit
Claude Code + CLI + Codex
Codex, Claude Code, Cursor, CLI, or custom agents.
Install
Ready
npx skills add WenjieDu/PyPOTS
Maintenance
active
2mo since push
Risk
Needs review
Financial research output is not financial advice; require human review before any live investment decision
GitHub quality
2.0K
100/100 Quality · 88/100 Trust
Coverage tags
Review notes
Financial research output is not financial advice; require human review before any live investment decision · Financial research output is not financial advice; require human review before any live investment decision.
Agent adoption scorecard
These scores combine public repository metadata, OpenAgentSkill review signals, maintenance freshness, and install readiness. They are a shortlist signal, not a replacement for human review.
Quality
ExcellentHigh-confidence pick with strong adoption and healthy maintenance signals.
Trust
Review then installGood shortlist signal, but the agent should review audit notes, install policy, and outcome evidence before running it.
Audit
Needs reviewA machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
OpenAgentSkill Trust Score v5
Use as the primary candidate after human or sandbox review.
Stars
2.0K GitHub stars
Repo activity
2.0K stars, 187 forks
Maintenance
2mo since push
License
BSD-3-Clause
Install
npx skills add WenjieDu/PyPOTS
Install safety
Agent-readable metadata
Use this block or the embedded JSON to decide whether an agent should install this skill, choose an alternative, or ask for human review first.
Suited tasks
Suited agents
Install decision
Trust and risk
Outcome loop
Do not use when
Agent safety v2
Usable candidate, but the agent should surface permission and audit notes before installation.
Require human approval before installing into a real workspace.
medium
Skill likely fetches remote pages, APIs, repositories, or external services.
medium
Skill may read or write project files, documents, generated artifacts, or local workspace state.
Agent resolve plan
The Resolve API returns the selected skill, alternatives, safety policy, audit notes, install target, and copy-paste prompt an agent can follow without scraping this page.
Open JSON
/api/agent/resolve?task=Use%20PyPOTS%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve text
/api/agent/resolve?task=Use%20PyPOTS%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Install handoff
/api/skills/wenjiedu-pypots/install
Agent should check
Copy prompt
Task: Use PyPOTS in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20PyPOTS%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/wenjiedu-pypots/install
Install command: npx skills add WenjieDu/PyPOTS
Before running it, summarize audit warnings, required permissions, and the fallback skill if install is risky.Agent handoff
Use the public install endpoint to fetch the command, safety checklist, target prompts, and canonical links for this skill.
Install handoff
/api/skills/wenjiedu-pypots/install
LLM text format
/api/skills/wenjiedu-pypots/install?format=text
Find alternatives
/api/skills/search?q=PyPOTS&limit=3
Agent prompt
Use PyPOTS for this task. Review https://www.openagentskill.com/api/skills/wenjiedu-pypots/install, then install with: npx skills add WenjieDu/PyPOTSRegistry metadata
This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.
Manifest
/api/registry/manifest/wenjiedu-pypots
LLM text
/api/registry/manifest/wenjiedu-pypots?format=text
Install alias
/api/registry/install/wenjiedu-pypots
Recommend
/api/registry/recommend?task=Use%20PyPOTS%20in%20an%20agent%20workflow&limit=3
Agent fit
Workflow automation
Use-case tags
Platforms
Python, Data Analysis, Claude Code
Audit report
A machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
Agent decision cockpit
Use this as a leading candidate, then validate the README and install path in your own agent stack.
Role in stack
Primary pick
Primary fit
Workflow automation
Trust label
Production-ready
Install path
Command ready
Use when
Evidence
review first
Implementation path
Trust profile
Good shortlist signal, but the agent should review audit notes, install policy, and outcome evidence before running it.
GitHub adoption
PASS2.0K GitHub stars
Stars/forks activity
INFO2.0K stars, 187 forks; issue activity unavailable in current metadata
Recent maintenance
PASS2mo since push
License clarity
PASSBSD-3-Clause
Good signals
Review before install
Recommended action
Use as the primary candidate after human or sandbox review.
Quality profile
High-confidence pick with strong adoption and healthy maintenance signals.
Workflow fit
Automate repeated work
I need my agent to automate a repeated workflow across tools and files.
Manage repositories
I need my agent to triage GitHub issues, review pull requests, and summarize repository changes.
Analyze datasets
I need my agent to analyze CSV data, produce insights, and explain trends.
Workflow fit
Turn skills into distribution
A workflow for turning newly indexed skills into SEO briefs, social drafts, comparison pages, and reusable publishing workflows.
Inspect, patch, and verify code
A workflow for software agents that inspect repositories, review pull requests, generate tests, and turn findings into shippable patches.
Ingest, retrieve, and cite
A workflow for document-heavy agents that ingest files, create searchable knowledge, retrieve relevant context, and answer with grounded sources.
Alternative shortlist
Similar skills that may fit this task.
Apache ECharts is a powerful, interactive charting and data visualization library for browser
Bring data to life with SVG, Canvas and HTML. :bar_chart::chart_with_upwards_trend::tada:
Turn any AI agent into an AI Scientist. The #1 Agent Skills library for science, used by 160,000+ scientists worldwide. 140 ready-to-use skills plus 100+ scientific databases covering biology, chemistry, medicine, and drug discovery. Compatible with Cursor, Claude Code, Codex, Pi, Antigravity, and the open Agent Skills standard.
Apache Superset is a Data Visualization and Data Exploration Platform
A Python toolkit/library for reality-centric machine/deep learning & data mining on partially-observed time series, with 50+ SOTA neural network models for scientific analysis tasks (imputation, classification, clustering, forecasting, anomaly detection, cleaning) on incomplete industrial irregularly-sampled multivariate TS with NaN missing values
Imported by the skill-only GitHub discovery pipeline because it matches agent skill, automation, domain workflow, RAG, document-processing, data, finance, security, or developer-tool signals. Protocol-server projects are excluded from automated imports.
Frameworks & tools
Decision snapshot
2,037 GitHub stars
Audit
Install and adoption review
Agent-proven evidence
Outcome reports after resolve, review, install, and one narrow run.
No agent outcome data yet. The first agent run can report success, setup needs, risk blocks, failure, or not-relevant through /api/agent/outcome.
Install
Free and open source. Review the report before installing into production agents.
Growth loop
Scenario-led draft for PyPOTS, ready for a manual X post.
PyPOTS: A Python toolkit/library for reality-centric machine/deep learning & data mining on partially... 2.0K stars https://www.openagentskill.com/skills/wenjiedu-pypots?ref=x
Listing + install path for PyPOTS: https://www.openagentskill.com/skills/wenjiedu-pypots?ref=x Install: npx skills add WenjieDu/PyPOTS
Listing source
This listing was indexed from public sources and is not marked official until a maintainer claim is approved.
Attribution links to the public repository or creator profile. Creators can claim the listing to update ownership signals.
Claim this skillOwner claim
This Community indexed listing is attributed to WenjieDu but is not marked official yet. Claim it to add a verified owner signal and make future launch, install, and audit updates easier to trust.
Creator backlink kit
Show the canonical listing, current trust and audit signals, and real Agent-Proven evidence where developers evaluate the repository.
[](https://www.openagentskill.com/skills/wenjiedu-pypots?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[](https://www.openagentskill.com/skills/wenjiedu-pypots?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[](https://www.openagentskill.com/skills/wenjiedu-pypots/audit)
[](https://www.openagentskill.com/skills/wenjiedu-pypots?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)WenjieDu✓
@wenjiedu
Share whether this skill looks useful for your agent workflow. Aggregated feedback improves rankings over time.
Review then install
Echarts
Apache ECharts is a powerful, interactive charting and data visualization library for browser
67.2K StarsD3
Bring data to life with SVG, Canvas and HTML. :bar_chart::chart_with_upwards_trend::tada:
113.1K StarsScientific Agent Skills
Turn any AI agent into an AI Scientist. The #1 Agent Skills library for science, used by 160,000+ scientists worldwide. 140 ready-to-use skills plus 100+ scientific databases covering biology, chemistry, medicine, and drug discovery. Compatible with Cursor, Claude Code, Codex, Pi, Antigravity, and the open Agent Skills standard.
33.5K StarsSuperset
Apache Superset is a Data Visualization and Data Exploration Platform
74.7K StarsReview then install
Install targets
Codex install prompt
Install the "PyPOTS" agent skill from https://github.com/WenjieDu/PyPOTS. Read its SKILL.md or equivalent instructions first, install only the files needed for this workspace, and summarize any required setup before using it. Skill purpose: A Python toolkit/library for reality-centric machine/deep learning & data mining on partially-observed time series, with 50+ SOTA neural network models for scientific analysis tasks (imputation, classification, clustering, forecasting, anomaly detection, cleaning) on incomplete industrial irregularly-sampled multivariate TS with NaN missing values After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {"event_id":"install_<unique-id>","skill_slug":"wenjiedu-pypots","task":"Install PyPOTS","agent":"codex","outcome":"success","install_used":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes.Supply asset profile
CSV, SQL, notebooks, dashboards, data pipelines, BI, ETL, and spreadsheet analysis.
Scenario
Data analysis
I need my agent to analyze CSV data, produce insights, and explain trends.
Agent fit
Claude Code + CLI + Codex
Codex, Claude Code, Cursor, CLI, or custom agents.
Install
Ready
npx skills add WenjieDu/PyPOTS
Maintenance
active
2mo since push
Risk
Needs review
Financial research output is not financial advice; require human review before any live investment decision
GitHub quality
2.0K
100/100 Quality · 88/100 Trust
Coverage tags
Review notes
Financial research output is not financial advice; require human review before any live investment decision · Financial research output is not financial advice; require human review before any live investment decision.
Agent adoption scorecard
These scores combine public repository metadata, OpenAgentSkill review signals, maintenance freshness, and install readiness. They are a shortlist signal, not a replacement for human review.
Quality
ExcellentHigh-confidence pick with strong adoption and healthy maintenance signals.
Trust
Review then installGood shortlist signal, but the agent should review audit notes, install policy, and outcome evidence before running it.
Audit
Needs reviewA machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
OpenAgentSkill Trust Score v5
Use as the primary candidate after human or sandbox review.
Stars
2.0K GitHub stars
Repo activity
2.0K stars, 187 forks
Maintenance
2mo since push
License
BSD-3-Clause
Install
npx skills add WenjieDu/PyPOTS
Install safety
Agent-readable metadata
Use this block or the embedded JSON to decide whether an agent should install this skill, choose an alternative, or ask for human review first.
Suited tasks
Suited agents
Install decision
Trust and risk
Outcome loop
Do not use when
Agent safety v2
Usable candidate, but the agent should surface permission and audit notes before installation.
Require human approval before installing into a real workspace.
medium
Skill likely fetches remote pages, APIs, repositories, or external services.
medium
Skill may read or write project files, documents, generated artifacts, or local workspace state.
Agent resolve plan
The Resolve API returns the selected skill, alternatives, safety policy, audit notes, install target, and copy-paste prompt an agent can follow without scraping this page.
Open JSON
/api/agent/resolve?task=Use%20PyPOTS%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve text
/api/agent/resolve?task=Use%20PyPOTS%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Install handoff
/api/skills/wenjiedu-pypots/install
Agent should check
Copy prompt
Task: Use PyPOTS in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20PyPOTS%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/wenjiedu-pypots/install
Install command: npx skills add WenjieDu/PyPOTS
Before running it, summarize audit warnings, required permissions, and the fallback skill if install is risky.Agent handoff
Use the public install endpoint to fetch the command, safety checklist, target prompts, and canonical links for this skill.
Install handoff
/api/skills/wenjiedu-pypots/install
LLM text format
/api/skills/wenjiedu-pypots/install?format=text
Find alternatives
/api/skills/search?q=PyPOTS&limit=3
Agent prompt
Use PyPOTS for this task. Review https://www.openagentskill.com/api/skills/wenjiedu-pypots/install, then install with: npx skills add WenjieDu/PyPOTSRegistry metadata
This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.
Manifest
/api/registry/manifest/wenjiedu-pypots
LLM text
/api/registry/manifest/wenjiedu-pypots?format=text
Install alias
/api/registry/install/wenjiedu-pypots
Recommend
/api/registry/recommend?task=Use%20PyPOTS%20in%20an%20agent%20workflow&limit=3
Agent fit
Workflow automation
Use-case tags
Platforms
Python, Data Analysis, Claude Code
Audit report
A machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
Agent decision cockpit
Use this as a leading candidate, then validate the README and install path in your own agent stack.
Role in stack
Primary pick
Primary fit
Workflow automation
Trust label
Production-ready
Install path
Command ready
Use when
Evidence
review first
Implementation path
Trust profile
Good shortlist signal, but the agent should review audit notes, install policy, and outcome evidence before running it.
GitHub adoption
PASS2.0K GitHub stars
Stars/forks activity
INFO2.0K stars, 187 forks; issue activity unavailable in current metadata
Recent maintenance
PASS2mo since push
License clarity
PASSBSD-3-Clause
Good signals
Review before install
Recommended action
Use as the primary candidate after human or sandbox review.
Quality profile
High-confidence pick with strong adoption and healthy maintenance signals.
Workflow fit
Automate repeated work
I need my agent to automate a repeated workflow across tools and files.
Manage repositories
I need my agent to triage GitHub issues, review pull requests, and summarize repository changes.
Analyze datasets
I need my agent to analyze CSV data, produce insights, and explain trends.
Workflow fit
Turn skills into distribution
A workflow for turning newly indexed skills into SEO briefs, social drafts, comparison pages, and reusable publishing workflows.
Inspect, patch, and verify code
A workflow for software agents that inspect repositories, review pull requests, generate tests, and turn findings into shippable patches.
Ingest, retrieve, and cite
A workflow for document-heavy agents that ingest files, create searchable knowledge, retrieve relevant context, and answer with grounded sources.
Alternative shortlist
Similar skills that may fit this task.
Apache ECharts is a powerful, interactive charting and data visualization library for browser
Bring data to life with SVG, Canvas and HTML. :bar_chart::chart_with_upwards_trend::tada:
Turn any AI agent into an AI Scientist. The #1 Agent Skills library for science, used by 160,000+ scientists worldwide. 140 ready-to-use skills plus 100+ scientific databases covering biology, chemistry, medicine, and drug discovery. Compatible with Cursor, Claude Code, Codex, Pi, Antigravity, and the open Agent Skills standard.
Apache Superset is a Data Visualization and Data Exploration Platform
A Python toolkit/library for reality-centric machine/deep learning & data mining on partially-observed time series, with 50+ SOTA neural network models for scientific analysis tasks (imputation, classification, clustering, forecasting, anomaly detection, cleaning) on incomplete industrial irregularly-sampled multivariate TS with NaN missing values
Imported by the skill-only GitHub discovery pipeline because it matches agent skill, automation, domain workflow, RAG, document-processing, data, finance, security, or developer-tool signals. Protocol-server projects are excluded from automated imports.
Frameworks & tools
Decision snapshot
2,037 GitHub stars
Audit
Install and adoption review
Agent-proven evidence
Outcome reports after resolve, review, install, and one narrow run.
No agent outcome data yet. The first agent run can report success, setup needs, risk blocks, failure, or not-relevant through /api/agent/outcome.
Install
Free and open source. Review the report before installing into production agents.
Growth loop
Scenario-led draft for PyPOTS, ready for a manual X post.
PyPOTS: A Python toolkit/library for reality-centric machine/deep learning & data mining on partially... 2.0K stars https://www.openagentskill.com/skills/wenjiedu-pypots?ref=x
Listing + install path for PyPOTS: https://www.openagentskill.com/skills/wenjiedu-pypots?ref=x Install: npx skills add WenjieDu/PyPOTS
Listing source
This listing was indexed from public sources and is not marked official until a maintainer claim is approved.
Attribution links to the public repository or creator profile. Creators can claim the listing to update ownership signals.
Claim this skillOwner claim
This Community indexed listing is attributed to WenjieDu but is not marked official yet. Claim it to add a verified owner signal and make future launch, install, and audit updates easier to trust.
Creator backlink kit
Show the canonical listing, current trust and audit signals, and real Agent-Proven evidence where developers evaluate the repository.
[](https://www.openagentskill.com/skills/wenjiedu-pypots?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[](https://www.openagentskill.com/skills/wenjiedu-pypots?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[](https://www.openagentskill.com/skills/wenjiedu-pypots/audit)
[](https://www.openagentskill.com/skills/wenjiedu-pypots?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)WenjieDu✓
@wenjiedu
Share whether this skill looks useful for your agent workflow. Aggregated feedback improves rankings over time.
Review then install
Echarts
Apache ECharts is a powerful, interactive charting and data visualization library for browser
67.2K StarsD3
Bring data to life with SVG, Canvas and HTML. :bar_chart::chart_with_upwards_trend::tada:
113.1K StarsScientific Agent Skills
Turn any AI agent into an AI Scientist. The #1 Agent Skills library for science, used by 160,000+ scientists worldwide. 140 ready-to-use skills plus 100+ scientific databases covering biology, chemistry, medicine, and drug discovery. Compatible with Cursor, Claude Code, Codex, Pi, Antigravity, and the open Agent Skills standard.
33.5K StarsSuperset
Apache Superset is a Data Visualization and Data Exploration Platform
74.7K Starsstandard package or runtime install path
Permission surface
filesystem or document access, network or browser access
Agent outcomes
No agent outcome data yet
Docs
Strong README/SKILL.md context
Risk summary
Install readiness
standard package or runtime install path
Permission surface
filesystem or document access, network or browser access
Agent outcomes
No agent outcome data yet
Docs
Strong README/SKILL.md context
Risk summary
Install readiness
standard package or runtime install path
Permission surface
filesystem or document access, network or browser access
Agent outcomes
No agent outcome data yet
Docs
Strong README/SKILL.md context
Risk summary
Install readiness
standard package or runtime install path
Permission surface
filesystem or document access, network or browser access
Agent outcomes
No agent outcome data yet
Docs
Strong README/SKILL.md context
Risk summary
Install readiness