Chalk Labs
Need help deploying data-cleaning?
Chalk Labs sets up agents like this inside real teams: connected to your tools, tested on your data, with a human in the loop where it matters.
Install
Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
npx agentmarket install skill/kakamana-data-cleaning
Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.
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About
Use cases: data
Reviews
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What's inside
- Skill name
data-cleaning- Description
- None
- License
- MIT
- Instructions
- 18 lines in SKILL.md · view on GitHub
Agent Skills spec issues
- invalid YAML frontmatter: Nested mappings are not allowed in compact mappings at line 2, column 14:
- name: missing
- description: missing
Files (1)
- SKILL.md
SKILL.md (first lines)
# Data Cleaning Clean a messy dataset: types, missing values, outliers, duplicates, and inconsistent categories. ## When to use Trigger when the user is doing data science / ML work touching Data Cleaning on a dataset or notebook in this project. ## Steps 1. Fix dtypes and parse dates/numbers. 2. Decide and document a strategy per column for missing values. 3. Handle outliers explicitly (cap, drop, or keep with reason). 4. Normalize categorical labels and remove duplicates; keep a cleaning log. ## Output Produce runnable Python (pandas / scikit-learn) plus a short written read-out of findings and decisions. Prefer pipelines over ad-hoc steps, and always state assumptions.
From kakamana/ds-agent-starter, MIT.
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- 0.469score
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model-eval
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Evaluate models honestly with the right metrics, proper validation, and baseline comparison.
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notebook-hygiene
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Keep notebooks reproducible, readable, and ready to graduate into scripts.
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eda
kakamana
No description from the publisher yet.
SkillCFreshness: CClaude Code +9
Signals
- Stars
- 0
- Forks
- 0
- Open issues
- 0
- Last commit (this folder)
- Jun 4, 2026 (5 months ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- MIT
- Repository
- github.com/kakamana/ds-agent-starter
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
More from kakamana
- 0.469score
feature-engineering
kakamana
Create, transform, and select features with leakage-aware discipline.
SkillCFreshness: CClaude Code +9 - 0.469score
model-eval
kakamana
Evaluate models honestly with the right metrics, proper validation, and baseline comparison.
SkillCFreshness: CClaude Code +9 - 0.469score
notebook-hygiene
kakamana
Keep notebooks reproducible, readable, and ready to graduate into scripts.
SkillCFreshness: CClaude Code +9
Done for you
Rather not set it up yourself?
Chalk Labs scopes, builds and runs agents for your team: the task audit, the eval harness, the integrations and the weekly ops report. Fixed scope, priced before we start.
See how we build AI agents →Read the case studies
Chalk Labs is a two-founder AI and growth studio. We build AI agents, CRMs and MVPs for Web3 and AI companies, and have shipped 50+ product and token launches.
- launches shipped
- 50+
- launches shipped
- media placements
- 500+
- media placements
- founders on every account
- 2
- founders on every account
Press we've placed: Forbes · CoinDesk · Decrypt · Cointelegraph · The Block
Rahil Jain, Co-founder, Chalk Labs. Ex-Goldman Sachs engineer. You talk to the person who builds it.
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Cite this page
data-cleaning skill. Agent Mart. https://agentmart.dev/skills/kakamana-data-cleaning (last updated 2026-10-24)