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 skills add arbazkhan971/godmode --skill nosqlgit clone --depth 1 https://github.com/arbazkhan971/godmodeWrote 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/arbazkhan971/godmode/nosql)<a href="https://agentmods.dev/skills/arbazkhan971/godmode/nosql"><img src="https://agentmods.dev/badge/skills/arbazkhan971/godmode/nosql/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/arbazkhan971/godmode/nosql"><img src="https://agentmods.dev/badge/skills/arbazkhan971/godmode/nosql.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Tool Misuse · line 115 Tool parameters are crafted to achieve unintended or unsafe behavior. Parameter abuse can bypass intended safety checks (e.g. shell=True, --force, dangerous glob patterns).Fix: Validate all tool parameters against an allowlist. Reject dangerous parameter values (shell=True, --force, -rf /) and use safe defaults.
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 5.1 | $0.00015 | $0.00910 |
| Opus 5.5 | $0.00006 | $0.00364 |
| Sonnet 5.5 | $0.00003 | $0.00182 |
| Haiku 4.5 | $0.00002 | $0.00091 |
Grade A, and why
nosql 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 1mo 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 — 125 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Activate When
/godmode:nosql, "mongodb schema", "dynamodb design"- "cassandra model", "neo4j graph", "time-series"
- "which database?", "nosql vs sql?"
Workflow
1. Requirements Assessment
Data model: Document|Key-Value|Wide-Column|Graph|TimeSeries
Access patterns: <list read/write patterns + frequency>
Scale: <expected documents, queries/sec>
Consistency: eventual|strong|tunable
# Check for NoSQL drivers
cat package.json 2>/dev/null | grep -iE "mongo|dynamo|neo4j"
pip list 2>/dev/null | grep -iE "pymongo|boto3|cassandra"
2. Database Selection
IF hierarchical/nested data + flexible schema: MongoDB
IF key-based access, extreme scale: DynamoDB
IF time-series writes, wide rows: Cassandra
IF relationship traversal: Neo4j
IF metrics/IoT at scale: InfluxDB/TimescaleDB
DEFAULT: Start with PostgreSQL, move to NoSQL when
a specific technical requirement demands it.
IF access patterns unknown: do NOT choose NoSQL yet. IF < 1M records and relational: PostgreSQL is better.
3. MongoDB Document Modeling
Embed when: read together, 1:1 or 1:few, bounded size
Reference when: independent, large, shared across docs
IF subdocument > 16MB: must reference (Mongo limit)
IF array grows unbounded: use bucketing pattern
// Compound index (covers multiple query patterns)
db.orders.createIndex({ customer_id: 1, created_at: -1 })
// Partial index (smaller, faster)
db.orders.createIndex(
{ status: 1 }, { partialFilterExpression: { active: true } }
)
4. DynamoDB Single-Table Design
Design for access patterns FIRST, entity model second.
PK: high cardinality, even distribution
SK: enables range queries within partition
GSI: different PK+SK for alternate access patterns
IF partition > 10GB: add write sharding. IF PK cardinality < 100 on high-volume: use composite. IF hot partition > 30% traffic: redistribute.
5. Cassandra Partition Design
One table per query (no JOINs).
Target: < 100MB per partition, < 100K rows.
Use time-bucketed keys for time-series data.
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.
- 1mo ago First seen · 125 lines · 15 tokens per session scan A fb5865a3db41
nosql is a skill published in the GitHub repository arbazkhan971/godmode (27 stars, last pushed 1mo ago), licensed MIT. It adds 15 tokens to every session and 910 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-09-03.
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