{
"name": "Vishal Kumar Mishra",
"location": "New Delhi, India",
"github": "inquilabee",
"day_job": "Data analysis with Pandas — pipelines, validation, messy real-world tables",
"builds": "Python frameworks so teams stop copying the same boilerplate",
"belief": "One good abstraction beats five config files and a prayer"
}“Who am I?” — still unanswered. What I do is clearer: I write Python, ship small frameworks, and use Pandas most days at work for analysis and data quality.
I’m less interested in one-off scripts that rot in a folder, and more in reusable layers — policy-driven tooling, schema contracts for DataFrames, UI patterns that don’t fight session state. If the same glue shows up twice, it probably belongs in a package.
- 📊 Daily — Pandas, validation, analysis pipelines, turning awkward exports into something trustworthy
- 🧱 Ship — frameworks and libraries (ShipGate, pdschema, streamtabs) instead of repeated boilerplate
- 🔬 Also building — market-research experimentation, document workflows, agent-friendly SDLC tooling (mostly private; themes show up in public packages)
- 📍 Based in New Delhi
- 💬 Happy to talk about DataFrame contracts, quality gates, framework design, or why your
pre-commitconfig has its own ecosystem
Open-source collabs on Python data tooling, framework design, and libraries where the abstraction is real — not a wrapper with a logo.
Installable packages (own repos, not forks):
| Package | What it is | Install |
|---|---|---|
| shipgate | Portable quality-gate orchestrator — one policy, catalog-driven check / format / refactor |
pip install shipgate |
| pdschema | Schema validation and contracts for Pandas DataFrames | pip install pdschema |
| streamtabs | Streamlit tabs with sane session state and data dependencies | pip install streamtabs |
| tablecv | Table extraction from images (OpenCV pipeline) | pip install tablecv |
| browserjquery | jQuery-style queries on a Selenium WebDriver | pip install browserjquery |
| autolinkedin | LinkedIn automation (LinkedinPy on GitHub) | pip install autolinkedin |
Docs for ShipGate: inquilabee.github.io/shipgate
Languages
Data and analysis
Frameworks and Python tooling
Automation and vision
DevOps and workflow
ShipGate 🚢 Framework for Python quality gates — one policy file, metadata-driven catalog, local CI and pre-commit without hand-rolled glue. AST refactor rules included. Python · Typer · libcst · FastAPI Repo · Docs · PyPI |
pdschema 📋 DataFrame schemas with real validation — types, custom checks, decorators for function I/O. The kind of contract Pandas projects need but rarely write twice. Python · Pandas · PyArrow Repo · PyPI |
streamtabs 📑 Small framework for Streamlit tabs — session state and data dependencies without the usual spaghetti. Python · Streamlit Repo · PyPI |
TableCV 📊 Pull tables out of images when the source never wanted to be structured data. Python · OpenCV Repo · PyPI |
More open-source work
| Project | What it does | PyPI |
|---|---|---|
| LinkedinPy | LinkedIn automation in Python — sessions, flows, browser reality | autolinkedin · pip install autolinkedin |
| selenium-tabs | Tab management for Selenium drivers | — |
| BrowserJQuery | jQuery-style DOM queries on WebDriver | browserjquery |
| finvesting | Notes and tooling around Indian equity / NSE data | — |
| python-in-browser | Python in the browser via Pyodide | — |
| python-snippets | Snippets I keep reusing | — |
| Area | Why it sticks |
|---|---|
| Data analysis | Pandas at work; turning exports into validated, explainable tables |
| Framework design | Reusable layers over copy-paste — policy, schemas, UI patterns |
| Market and research data | Experimentation pipelines, calendars, reproducible knowledge — not just backtests |
| Document extraction | PDFs, scans, tables in images — inputs that were never meant to be tidy |
| Quality and structure | Gates, AST refactors, contracts that catch drift before production |
| Agent workflows | Skills, hooks, and CLIs that agents can actually run and verify |
building:
- ShipGate — catalog, refactor rules, report UI
- pdschema and other data-contract tooling
- Framework-shaped packages instead of one-repo glue scripts
at_work:
- Pandas pipelines and analysis most days
- Validation, exploration, and making messy data boring
exploring:
- Reproducible market-research workflows
- Document and table extraction beyond happy-path files
- Agent-friendly CLIs and verification loops
open_to:
- PyPI-packaged Python libraries (data or tooling)
- Framework design conversations
- Issues and PRs on repos above
