plaid-fintech
Expert patterns for Plaid API integration including Link token flows, transactions sync, identity verification, Auth for ACH, balance checks, webhook handling, and fintech compliance best practices...
Best use case
plaid-fintech is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Expert patterns for Plaid API integration including Link token flows, transactions sync, identity verification, Auth for ACH, balance checks, webhook handling, and fintech compliance best practices...
Teams using plaid-fintech should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/plaid-fintech/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How plaid-fintech Compares
| Feature / Agent | plaid-fintech | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Expert patterns for Plaid API integration including Link token flows, transactions sync, identity verification, Auth for ACH, balance checks, webhook handling, and fintech compliance best practices...
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Plaid Fintech ## Patterns ### Link Token Creation and Exchange Create a link_token for Plaid Link, exchange public_token for access_token. Link tokens are short-lived, one-time use. Access tokens don't expire but may need updating when users change passwords. ### Transactions Sync Use /transactions/sync for incremental transaction updates. More efficient than /transactions/get. Handle webhooks for real-time updates instead of polling. ### Item Error Handling and Update Mode Handle ITEM_LOGIN_REQUIRED errors by putting users through Link update mode. Listen for PENDING_DISCONNECT webhook to proactively prompt users. ## Anti-Patterns ### ❌ Storing Access Tokens in Plain Text ### ❌ Polling Instead of Webhooks ### ❌ Ignoring Item Errors ## ⚠️ Sharp Edges | Issue | Severity | Solution | |-------|----------|----------| | Issue | critical | See docs | | Issue | high | See docs | | Issue | high | See docs | | Issue | high | See docs | | Issue | medium | See docs | | Issue | medium | See docs | | Issue | medium | See docs | | Issue | medium | See docs | ## When to Use This skill is applicable to execute the workflow or actions described in the overview.
Related Skills
semgrep-rule-variant-creator
Creates language variants of existing Semgrep rules. Use when porting a Semgrep rule to specified target languages. Takes an existing rule and target languages as input, produces independent rule+test directories for each language.
searchnews
当用户要求"搜索新闻"、"查询AI新闻"、"整理新闻"、"获取某天的新闻",或提到需要搜索、整理、汇总指定日期的AI行业新闻时,应使用此技能。
search-specialist
Expert web researcher using advanced search techniques and
scorecard-marketing
Build quiz and assessment funnels that generate qualified leads at 30-50% conversion. Use when the user mentions "lead magnet", "quiz funnel", "assessment tool", "lead generation", or "score-based segmentation". Covers question design, dynamic results by tier, and automated follow-up sequences. For landing page conversion, see cro-methodology. For full marketing plans, see one-page-marketing.
scikit-learn
Machine learning in Python with scikit-learn. Use when working with supervised learning (classification, regression), unsupervised learning (clustering, dimensionality reduction), model evaluation, hyperparameter tuning, preprocessing, or building ML pipelines. Provides comprehensive reference documentation for algorithms, preprocessing techniques, pipelines, and best practices.
scholar-evaluation
Systematically evaluate scholarly work using the ScholarEval framework, providing structured assessment across research quality dimensions including problem formulation, methodology, analysis, and writing with quantitative scoring and actionable feedback.
sarif-parsing
Parses and processes SARIF files from static analysis tools like CodeQL, Semgrep, or other scanners. Triggers on "parse sarif", "read scan results", "aggregate findings", "deduplicate alerts", or "process sarif output". Handles filtering, deduplication, format conversion, and CI/CD integration of SARIF data. Does NOT run scans — use the Semgrep or CodeQL skills for that.
kaizen:root-cause-tracing
Use when errors occur deep in execution and you need to trace back to find the original trigger - systematically traces bugs backward through call stack, adding instrumentation when needed, to identify source of invalid data or incorrect behavior
rice
RICE prioritization scoring initiatives by Reach, Impact, Confidence, and Effort. Use for feature prioritization, roadmap planning, or when comparing initiatives objectively.
retro
Start-Stop-Continue retrospective identifying what to Start doing, Stop doing, and Continue doing. Use for sprint retros, personal reflection, team process reviews, or habit audits.
fpf:reset
Reset the FPF reasoning cycle to start fresh
research
Conduct preliminary research on a topic and generate research outline. For academic research, benchmark research, technology selection, etc.