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/azure/documentdb-agent-kit/data-modelingnpx skills add Azure/documentdb-agent-kit --skill data-modelinggit clone --depth 1 https://github.com/Azure/documentdb-agent-kitWhat 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 | $0.00086 | $0.00533 |
| Opus 5 | $0.00043 | $0.00267 |
| Sonnet 5 | $0.00017 | $0.00107 |
| Haiku 4.5 | $0.00009 | $0.00053 |
Grade A, and why
documentdb-data-modeling 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.
What it actually says
Data Modeling — Azure DocumentDB
Guiding principle: "Data that is accessed together should be stored together."
Each rule follows the same shape — why it matters → incorrect example → correct example → references.
Rules
- model-embed-vs-reference — Embed data accessed together; reference unbounded N-sides.
- model-16mb-limit — Stay well under the 16 MB BSON document limit; plan for steady-state growth.
- model-denormalize-reads — Denormalize for read-heavy workloads; pre-compute aggregates to avoid
$lookup. - model-schema-versioning — Add a
schemaVersionfield and migrate documents lazily. - model-large-field-split — Split a large, low-compressibility field into a side collection keyed by
_idto avoid the PostgreSQL TOAST detoast tax on scans. Companion tool:scripts/toast-split-advisor.sh.
Companion tool (analysis only)
scripts/toast-split-advisor.sh measures
heap vs TOAST bytes on a live local container and reports where a large field
should be split out — it never moves data:
bash scripts/toast-split-advisor.sh --db <name> [--json]
Decision framework
| Relationship | Cardinality | Access pattern | Recommendation |
|---|---|---|---|
| One-to-One | 1:1 | Always together | Embed |
| One-to-Few | 1:N (N < ~100) | Usually together | Embed array |
| One-to-Many | 1:N (N > ~100) | Often separate | Reference |
| Many-to-Many | M:N | Varies | Two-way reference or junction collection |
See each rule file for the full reasoning and code examples.
What ships with it
5 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.
- 2d ago First seen · 41 lines · 86 tokens per session scan A cbc77333f75c
documentdb-data-modeling is a skill published in the GitHub repository Azure/documentdb-agent-kit (5 stars, last pushed 1mo ago), licensed MIT. It adds 86 tokens to every session and 533 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 2026-08-31.
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