SKILLEMALL.ai

AC clickhouse

Comprehensive ClickHouse skill covering everything you need to work with a ClickHouse analytics database: schema design, query optimization, insert strategies, CLI usage, table creation and migrations, backend integration (Node.js, Python, Go), Redis caching strategy, cluster vs single-node differences, and how to test/debug data in the database. MUST USE whenever the user mentions ClickHouse, asks about analytics tables, high-volume insert pipelines, MergeTree schemas, ORDER BY / PRIMARY KEY design, materialized views, ClickHouse query performance, connecting to ClickHouse from code, or running ClickHouse CLI commands to inspect data.

ClawHub Agent Skills author: EncryptShawn v1.0.0 MIT-0 30 files body ≈ 8 840 tokens Open the sourceclawhub.ai analyzed 4 d ago

As a process C 52/100 · Has gaps — weak spots: result and completion, inputs and preconditions, consistency

IntegrationSoftware developmentInfrastructureData and analyticstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
90/100
safety, quality, tests
Safety 60%
100
Quality 40%
74
Run on models
none yet
Process rating
C
52/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

How to improve

  1. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
For the model run — optional
  • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
  • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

Guard findings · 0

✓ No critical or high findings

Files scanned: 30. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning body-long SKILL.md body ≈ 8840 tokens (recommended < 5000); move details to references/

Process rating: all ten parameters 52/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Progress reporting. Says nothing while it works
  • 30Running it twice. 12 mutating operations with no state check
  • 40Consistency. Frontmatter name (clickhouse) differs from the folder (clickhouse-developer)
  • 40Execution cost. Instruction body is 8840 tokens: crowds the task out of the window
  • 60Tools and files. Uses tools (web, python, node) that frontmatter does not declare
  • 85Steps. 33 steps, 1 vague phrases
  • 100When it triggers. States when to use and when not to
  • 100Failures and branches. 2 branches, has a failure section
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 11 top-level sections: this looks like several domains in one skill

Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

Quality signals

  • +5Description has no quoted example phrases that should trigger the skill
  • +4Description does not say when NOT to use the skill (false activations)
  • +3Output format is not stated: the model decides each time
  • -2localhost URLs: will not work for another user
  • -218 emoji in the instructions: noise for the model
  • +1No license
  • +2Single-language instructions
  • +3Description length 643: enough signal without eating the budget
  • +4Structure: 49 headings
  • +3Step-by-step instructions: 33 items
  • +4Has examples (51 code blocks)
  • +4Reference files are cited in the instructions (6 of 28)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 74.

External checks

ClawHub: clean
This is a documentation-only ClickHouse guide; the main caution is to review any database-changing SQL before running it.
LLM: benign (high) · VirusTotal: · 29 May 2026