Claude Code Guide is a reference collection for configuring and using Claude Code, Anthropic’s command-line coding agent. Developers use it to learn commands, skills, agents, MCP, automation, security, integrations, and troubleshooting. Its catalogue add-ons provide many of the documented skills and agents.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/zebbern/claude-code-guide/dataset-quality-auditnpx skills add zebbern/claude-code-guide --skill dataset-quality-auditgit clone --depth 1 https://github.com/zebbern/claude-code-guideWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/zebbern/claude-code-guide/dataset-quality-audit)<a href="https://agentmods.dev/skills/zebbern/claude-code-guide/dataset-quality-audit"><img src="https://agentmods.dev/badge/skills/zebbern/claude-code-guide/dataset-quality-audit.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00081 | $0.00980 |
| Opus 5 | $0.00041 | $0.00490 |
| Sonnet 5 | $0.00016 | $0.00196 |
| Haiku 4.5 | $0.00008 | $0.00098 |
Grade A, and why
dataset-quality-audit scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
1 near-identical copy found in the catalogue:
- dataset-quality-audit — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 111 lines — stays where its author put it; the contents beside it link to each section on GitHub.
dataset-quality-audit
A data quality auditing tool that runs 12-dimension quality checks on tabular data, producing per-dimension scores (0–100), an overall grade, and actionable fix suggestions.
Capabilities
| Dimension | Description |
|---|---|
| Missing Values | Count and percentage of null/NaN values per column |
| Duplicate Rows | Number and percentage of fully duplicated rows |
| Type Consistency | Mixed types within a single column (e.g., numbers mixed with text) |
| Value Range / Outliers | Outlier detection using the IQR method |
| Format Compliance | Consistency of date, email, phone number, and other formatted fields |
| Uniqueness Constraints | Whether ID-type columns contain duplicates |
| Whitespace Issues | Leading/trailing spaces, empty strings, whitespace-only values |
| Constant Columns | Columns with only a single unique value (zero information) |
| Distribution Skewness | Whether numeric columns have excessive skewness |
| Column Naming | Spaces, special characters, or inconsistent casing in column names |
| Cardinality Anomalies | Unusually high or low number of unique values |
| Cross-Column Consistency | Logical checks across columns (e.g., start date before end date) |
Quick Start
# Basic quality check
python3 scripts/data_quality_checker.py data.csv
# Save report as JSON
python3 scripts/data_quality_checker.py data.csv --output report.json
# Specify ID columns (for uniqueness checks)
python3 scripts/data_quality_checker.py users.csv --id-columns "user_id,email"
# Specify date columns (for format checks)
python3 scripts/data_quality_checker.py orders.csv --date-columns "created_at,updated_at"
Detailed Usage
Basic Invocation
python3 scripts/data_quality_checker.py <data-file> [options]
Parameters
| Parameter | Short | Required | Default | Description |
|---|---|---|---|---|
input |
— | Yes | — | Path to input file (CSV/TSV/Excel/JSON) |
--output |
-o |
No | stdout | Path for the JSON report output |
--id-columns |
-id |
No | Auto-detect | Comma-separated column names that should be unique |
--date-columns |
-dc |
No | Auto-detect | Comma-separated column names containing dates |
--sample |
-s |
No | All rows | Number of rows to sample (useful for large files) |
--encoding |
-e |
No | utf-8 | File encoding |
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 5d ago First seen · 111 lines · 81 tokens per session scan A db466ec94266
dataset-quality-audit is a skill published in the GitHub repository zebbern/claude-code-guide (4,600 stars, last pushed today), licensed MIT. It adds 81 tokens to every session and 980 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
cdb-scan
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exam-coach
Quiz and coach the user for the Anthropic Claude certification exams using this repository's blueprints and official exam guides. Use when the user asks to practice, be quizzed, drill a domain, take a mock exam, or prepare for the Associate, Developer, or Architect certifications.
security-claude
Skill "security-claude" from rahozosman/security-claude, covering security architecture & threat modeling intelligence, how this skill is organized (progressive disclosure), 1. pick a mode, 2. core method (applies to every mode) and 3. doing a focused review.
bridger
Coordinate with another Claude Code session over the bridge — discover peers, ask them, answer their questions — INSTEAD of guessing or asking the user. Trigger this the moment the task depends on something another repo's session knows: a dependency/library that changed and this code consumes it, an API or schema…
run-tests
Run the pytest suite, report pass/fail counts and coverage, and identify untested code. Use when the user asks to run tests, check test coverage, or verify that changes didn't break anything.