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/leolin990405/r-analytics-skill/mongolitenpx skills add LeoLin990405/r-analytics-skill --skill mongolitegit clone --depth 1 https://github.com/LeoLin990405/r-analytics-skillWrote 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/leolin990405/r-analytics-skill/mongolite)<a href="https://agentmods.dev/skills/leolin990405/r-analytics-skill/mongolite"><img src="https://agentmods.dev/badge/skills/leolin990405/r-analytics-skill/mongolite.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 5.1 | $0.00023 | $0.00947 |
| Opus 5 | $0.00012 | $0.00474 |
| Sonnet 5 | $0.00005 | $0.00189 |
| Haiku 4.5 | $0.00002 | $0.00095 |
Grade A, and why
mongolite scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 179 lines — stays where its author put it; the contents beside it link to each section on GitHub.
mongolite
MongoDB client for R.
Connection
library(mongolite)
# Connect to local MongoDB
con <- mongo(collection = "mycollection", db = "mydb")
# Connect with URI
con <- mongo(
collection = "mycollection",
db = "mydb",
url = "mongodb://user:password@host:27017"
)
# With options
con <- mongo(
collection = "mycollection",
db = "mydb",
url = "mongodb://localhost",
options = ssl_options()
)
Insert
# Insert data frame
con$insert(df)
# Insert single document
con$insert('{"name": "John", "age": 30}')
# Insert multiple documents
con$insert(list(
list(name = "John", age = 30),
list(name = "Jane", age = 25)
))
Query
# Find all
df <- con$find()
# Find with query
df <- con$find('{"age": {"$gt": 25}}')
# Select fields
df <- con$find(fields = '{"name": 1, "age": 1, "_id": 0}')
# Sort
df <- con$find(sort = '{"age": -1}')
# Limit
df <- con$find(limit = 10)
# Skip (pagination)
df <- con$find(skip = 10, limit = 10)
# Combined
df <- con$find(
query = '{"status": "active"}',
fields = '{"name": 1, "email": 1}',
sort = '{"created": -1}',
limit = 100
)
Update
# Update one
con$update('{"name": "John"}', '{"$set": {"age": 31}}')
# Update many
con$update('{"status": "pending"}', '{"$set": {"status": "active"}}', multiple = TRUE)
# Upsert
con$update('{"name": "New"}', '{"$set": {"age": 20}}', upsert = TRUE)
# Replace document
con$replace('{"name": "John"}', '{"name": "John", "age": 32, "city": "NYC"}')
Delete
# Remove one
con$remove('{"name": "John"}', just_one = TRUE)
# Remove many
con$remove('{"status": "inactive"}')
# Remove all
con$remove('{}')
# Drop collection
con$drop()
Aggregation
# Aggregation pipeline
result <- con$aggregate('[
{"$match": {"status": "active"}},
{"$group": {"_id": "$category", "count": {"$sum": 1}}},
{"$sort": {"count": -1}}
]')
# With multiple stages
result <- con$aggregate('[
{"$match": {"date": {"$gte": {"$date": "2024-01-01"}}}},
{"$project": {"year": {"$year": "$date"}, "amount": 1}},
{"$group": {"_id": "$year", "total": {"$sum": "$amount"}}}
]')
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 · 179 lines · 23 tokens per session scan A 3bcb1ff62b91
mongolite is a skill published in the GitHub repository LeoLin990405/r-analytics-skill (5 stars, last pushed 5mo ago), licensed MIT. It adds 23 tokens to every session and 947 once invoked, about $0.0001 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.
Other skills, from other repositories
databricks-live-unity-catalog-grant-guard-at-azure
Mutating-runtime live guard for Unity Catalog privilege management on Azure Databricks. Executes exactly ONE GRANT or REVOKE of a single privilege on a single Unity Catalog securable (schema, table, or volume) to a single principal — with explicit written human approval, dry-run preflight, prior-state capture, and a…
alibaba-live-rds-polardb-mutation-guard
Gate RDS/PolarDB instance deletion, spec downgrade, and backup policy removal — database deletion without verified backup is permanently destructive.
alibaba-waf-reliability-review
Assess Alibaba Cloud workload reliability: multi-AZ ECS topology, SLB/ALB/NLB load balancing, Auto Scaling health policies, RDS/PolarDB HA failover, backup and cross-region DR, and Cloud Monitor/ARMS observability coverage.
alibaba-analyticdb-realtime
Operate AnalyticDB for MySQL and PostgreSQL, Hologres real-time OLAP analytics, and DAS real-time diagnostics for sub-second interactive analytics workloads.
alibaba-polardb-rds-dba
Operate PolarDB (MySQL/PG/Oracle) clusters and RDS instances — DAS diagnostics, database proxy, Global Database Network, backup strategy, and performance tuning.
azure-cosmosdb-application-developer
Use this skill for Azure Cosmos DB application development work, especially NoSQL data modeling, document structure, partition-aware access patterns, point reads, query design, SDK usage, transactional batch scope, consistency-aware reads, change feed integration, and Cosmos DB development guidance.