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/kevinzai/commander/ccc-datanpx skills add KevinZai/commander --skill ccc-datagit clone --depth 1 https://github.com/KevinZai/commanderWhat 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 | $0.00035 | $0.00859 |
| Opus 5 | $0.00017 | $0.00430 |
| Sonnet 5 | $0.00007 | $0.00172 |
| Haiku 4.5 | $0.00003 | $0.00086 |
Grade A, and why
ccc-data 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 2d 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.
How it starts
The opening of the file, as written. The whole thing — 80 lines — stays where its author put it; the contents beside it link to each section on GitHub.
$ccc-data — Data domain hub
Load ONE skill. Get the entire data domain. 8 skills in one.
Sub-Skills
| # | Skill | Focus |
|---|---|---|
| 1 | data-pipeline | ETL/ELT pipelines — Airflow, dbt, Dagster, incremental loads |
| 2 | sql-optimization | SQL optimization — query analysis, indexes, execution plans |
| 3 | data-visualization | Charts and dashboards — D3, Chart.js, Tremor, Recharts |
| 4 | machine-learning | ML model development — scikit-learn, PyTorch, TensorFlow |
| 5 | data-quality | Data validation, schema enforcement, quality monitoring |
| 6 | analytics-setup | Analytics implementation — PostHog, Mixpanel, GA4 |
| 7 | reporting | Automated report generation and scheduling |
| 8 | vector-search | Vector database — Pinecone, pgvector, Qdrant with semantic search |
No expressed intent? Present the top 3 sub-skills + "More…" as an AskUserQuestion picker (≤4 options).
Routing Matrix
| Your Intent | Route To |
|---|---|
| "Data pipeline" / "ETL" / "ELT" | data-pipeline |
| "Slow queries" / "SQL optimization" | sql-optimization |
| "Charts" / "Dashboard" / "Visualization" | data-visualization |
| "ML model" / "Train a model" | machine-learning |
| "Data quality" / "Validation" | data-quality |
| "Analytics" / "Tracking events" | analytics-setup |
| "Automated reports" | reporting |
| "Semantic search" / "Vector search" / "Embeddings" | vector-search |
Files API Integration
For large datasets and data files, the Files API can ingest CSVs, JSON, Parquet, and other formats directly — avoiding token limits for bulk data analysis. Use data-ingestion from ccc-research for document-scale inputs.
Campaign Templates
Analytics Stack Setup
analytics-setup→ PostHog/Mixpanel/GA4 event trackingdata-pipeline→ sync analytics data to warehousedata-visualization→ build dashboards from warehouse datareporting→ automate periodic reports
ML Feature Build
data-quality→ validate and clean training datasql-optimization→ optimize feature extraction queriesmachine-learning→ model development + evaluationdata-visualization→ model performance chartsvector-search→ if feature requires semantic similarity
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.
- 2d ago First seen · 80 lines · 35 tokens per session scan A 7354a46b87af
ccc-data is a skill published in the GitHub repository KevinZai/commander (6 stars, last pushed 3d ago), licensed MIT. It adds 35 tokens to every session and 859 once invoked, about $0.0002 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 2026-08-31.
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agent-memory
../../../engineering/agent-memory/skills/agent-memory/SKILL.md.
agile-product-owner
../../../product-team/agile-product-owner/skills/agile-product-owner/SKILL.md.
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