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AC cm-airflow-dag-analyzer

Analyze Apache Airflow DAG definitions for quality, reliability, and operational best practices. Checks task dependencies, SLA compliance, retry policies, resource allocation, sensor timeouts, trigger rules, pool usage, and DAG complexity. Use when asked to review Airflow DAGs, audit DAG quality, check Airflow best practices, analyze task dependencies, optimize DAG performance, review Airflow configuration, or troubleshoot DAG failures. Triggers on "airflow", "DAG", "airflow dag", "dag review", "airflow audit", "task dependencies", "airflow best practices", "dag quality", "airflow optimization", "dag analysis", "airflow troubleshoot", "dag performance".

ClawHub Agent Skills author: charlie-morrison v1.0.1 MIT-0 2 files body ≈ 4 513 tokens Open the sourceclawhub.ai↗ analyzed 4 d ago

As a process C 58/100 · Has gaps — weak spots: inputs and preconditions, consistency, running it twice

AnalyzerInfrastructureSecurityCustomer supporttype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
A
97/100
safety, quality, tests
Safety 60%
100
Quality 40%
92
Run on models
none yet
Process rating
C
58/100
Has gaps
Inputs and preconditions w 11
0
Running it twice w 4
30
Consistency w 8
40
the three weakest of ten parameters · all ten

How to improve

    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: 2. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 58/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 30Running it twice. 2 mutating operations with no state check
    • 40Consistency. Frontmatter name (cm-airflow-dag-analyzer) differs from the folder (airflow-dag-analyzer)
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (python) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 70When it triggers. States when to use, but not when not to
    • 70Execution cost. Instruction body is 4513 tokens
    • 100Steps. 22 steps
    • 100Progress reporting. Reports progress

    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

    • +4Description does not say when NOT to use the skill (false activations)
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 11 example trigger phrases
    • +3Description length 661: enough signal without eating the budget
    • +4Structure: 17 headings
    • +3Step-by-step instructions: 22 items
    • +3Output format is stated explicitly
    • +4Has examples (16 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 92.

    External checks

    ClawHub: clean
    This is a read-only Airflow DAG review skill whose file access is expected for its purpose, though users should point it only at intended DAG paths.
    LLM: benign (high) · VirusTotal: · 29 May 2026