Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub · Data Analysis
InfluxDB
Store and query time-series data with proper schema design and retention.
skill openclawclawhub Free
Store and query time-series data with proper schema design and retention.
⬇ 0 downloads ★ 0 stars Unverified but indexed
Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- SKILL.md
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.0
Provenance
- Publisher
- ivangdavila
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
Version Differences
InfluxDB 2.x uses Flux query language, 1.x uses InfluxQL—syntax completely different 2.x: buckets, organizations, tokens; 1.x: databases, retention policies, users Don't mix documentation—check version before copying queries
Tags vs Fields (Critical)
Tags are indexed, fields are not—filter on tags, aggregate on fields Tag values must be strings—numbers as tags work but waste index space Fields support numbers, strings, booleans—store metrics as fields Wrong choice kills query performance—can't change after data written
Cardinality Trap
High-cardinality tags destroy performance—unique user IDs as tags = disaster Cardinality = unique combinations of tag values—grows multiplicatively Check with SHOW CARDINALITY (1.x) or influx bucket inspect (2.x) Rule of thumb: <100K series per measurement; millions = problems
Line Protocol
Format: measurement,tag1=v1,tag2=v2 field1=1,field2="str" timestamp No spaces around = in tags—space separates tags from fields String fields need quotes, tag values don't—field="text" vs tag=text Timestamps in nanoseconds by default—specify precision to avoid mistakes
Timestamps
Default precision is nanoseconds—sending seconds without precision flag = year 2000 data Specify on write: precision=s for seconds, precision=ms for milliseconds Missing timestamp uses server time—usually fine for real-time ingestion Timestamps are UTC—client timezone doesn't matter
Retention and Downsampling
Set retention policy/bucket duration—data older than retention auto-deleted Raw data at 10s intervals for 7 days, downsample to 1min for 30 days, 1h for 1 year 2.x: Tasks for downsampling; 1.x: Continuous Queries Without downsampling, storage grows forever and queries slow down
Flux Query Patterns (2.x)
Always start with from(bucket:) then |> range(start:)—range is required |> filter(fn: (r) => r._measurement == "cpu") for filtering |> aggregateWindow(every: 1h, fn: mean) for time-based aggregation Chain transforms with |> pipe operator—order matters for performance
InfluxQL Patterns (1.x)
SELECT mean("value") FROM "measurement" WHERE time > now() - 1h GROUP BY time(5m) Double quotes for identifiers, single quotes for string literals GROUP BY time() for time-based aggregation—required for most dashboards FILL(none) to skip empty intervals, FILL(previous) to carry forward
Schema Design
Measurement name = table name—one per metric type (cpu, memory, requests) Tag for dimensions you filter/group by—host, region, service Field for values you aggregate—usage_percent, count, latency_ms Avoid encoding data in measurement names—cpu.host1 wrong, cpu + host=host1 right
Write Performance
Batch writes—individual points have HTTP overhead Telegraf for production ingestion—handles batching, buffering, retry Write to localhost if possible—network latency adds up at high throughput async writes in client libraries—don't block on each write
Query Performance
Always include time range—unbounded queries scan everything Filter on tags before fields—tags use index, fields scan data Limit results with LIMIT or |> limit()—dashboard doesn't need 1M points Use GROUP BY / aggregateWindow to reduce data before returning
Common Errors
"partial write: field type conflict"—same field with different types; fix at source "max-values-per-tag limit exceeded"—cardinality too high; redesign schema "database not found"—2.x uses buckets, not databases; check API version Query timeout—add narrower time range or aggregate more aggressively
Category context
Data access, storage, extraction, analysis, reporting, and insight generation.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
1 Docs
- SKILL.md