360 feedback system
Explore 360 feedback system as a data & databases workflow from sickn33/agentic-awesome-skills. Compare its source, maintenance and review signals before choosing it for your agent.
Embedded analytical SQL for Python tasks that need ClickHouse functions without operating a separate database server.
Editorial review checked 2026-10-29 against this instruction snapshot. Supporting scripts and runtime behavior remain outside this editorial review.
The instruction file distinguishes a one-off query, a persistent Session and a DB-API connection. Its examples combine local files, Python data and remote table functions. References cover query parameters, output formats and connectors; this is an application workflow rather than server administration.
SQL-oriented analysis of Parquet or CSV files, small reproducible analytical pipelines, and experiments that need ClickHouse expressions inside a Python process.
Installing a skill does not install or validate chDB. The source declares Python and platform requirements. Queries against remote services can move sensitive data, and persistent session directories need an explicit lifecycle. Parameter binding should replace string interpolation for values.
Requires Python 3.9+, macOS or Linux. pip install chdb.
Inspect the pinned source snapshot and its supporting files first. The command selects this skill through the open-source skills CLI. Node.js and agent-specific setup may be required. Installation can download code; this catalog never runs it.
npx skills add https://github.com/ClickHouse/agent-skills/tree/69a8354b05b5942e5cea77b6c87be7788303fc18/skills/chdb-sql --skill chdb-sqlReview pinned skill folder ↗ · CLI documentation ↗
After source review, use npx skills check and npx skills update to inspect and apply upstream updates. Re-check changed instructions before running them.
Static review only. This is not a security audit or a guarantee of safety.
SKILL.md text only; scripts, dependencies and runtime behavior not audited.
Allowed tools declaration: Unknown. Runtime permissions and network behavior: Unknown.
86/100 · Metadata coverage 100% · Model 1.0
Scores summarize observable discovery signals. Popularity and repository activity apply to the whole repository. Unknown runtime safety earns no certification.
Bounded source inspection, not a runtime security audit. No source code was executed.
2 text files inspected at this exact repository snapshot. 5 files outside the inspection budget or type allowlist. Dependencies were not installed or independently audited.
Absence of a pattern is not proof of safety. Permission needs and runtime behavior remain Unknown.
18545d877089af62b36cf1f8724bd6580d723340ee182838bd11bf0834af7c201a2bc97f089f7c95bca771deb8362e531f32aa36147b7f1466e840223e300d91Prove a parameterized query on a local fixture first. Select the simplest API that provides the required state, then introduce external data only after reviewing connector permissions.
Verify output schema, exact aggregates and behavior on missing files. For persistent sessions, reopen the database and confirm expected state; for remote joins, check transfer volume and credential handling separately.
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A Python DataFrame workflow for exploring files and joining tabular sources through the embedded ClickHouse engine.
A workload-first decision aid for ClickHouse ingestion, enrichment, time-series storage and changing data.
A structured review of ClickHouse schema, query and insertion choices with links to focused rules.
Editorial combinations of complementary roles; review each source independently.
Choose an embedded DataFrame or SQL workflow, then turn checked calculations into an editable report.