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AB sql-object-impact-analysis

Before modifying a table, column, stored procedure, view, or trigger in a legacy codebase, trace every place that references it — across other SQL objects (procs, views, triggers) AND application code (C#, Angular/TypeScript, JS, or any language in the repo) — and produce a structured blast-radius report so you know what breaks before you change it. Use when the user asks 'what uses this column/table/procedure', 'is it safe to change X', 'what depends on this', 'impact of renaming/dropping X', or before any schema/proc modification in an unfamiliar or legacy codebase. Not for privacy/PII exposure analysis (see data-breach-blast-radius) or query performance tuning (see sql-optimization).

github/awesome-copilot Agent Skills author: github MIT 1 file body ≈ 1 234 tokens Open the sourcegithub.com↗ analyzed 5 d ago

Before modifying a table, column, stored procedure, view, or trigger in a legacy codebase, trace every place that references it — across other SQL objects…

As a process B 71/100 · Nearly there — weak spots: inputs and preconditions, failures and branches, progress reporting

ProcedureSoftware developmenttype and topics are labelled automatically from the skill text
Runs in: GitHub Copilot / VS Code Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) OpenClaw Goose OpenCode
JSON
Technical rating
A
95/100
safety, quality, tests
Safety 60%
100
Quality 40%
87
Run on models
none yet
Process rating
B
71/100
Nearly there
Inputs and preconditions w 11
0
Failures and branches w 10
0
Progress reporting w 2
0
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: 1. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note edit-residue the text marks something as outdated (lines 4, 10): check that old rules are not kept next to new ones — the full check reads the text for contradictions

    Process rating: all ten parameters 71/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 60Result and completion. Output format stated, no completion criterion
    • 100Tools and files. No external tools needed
    • 100Steps. 33 steps
    • 100When it triggers. States when to use and when not to
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1234 tokens
    • 100Running it twice. Mutating operations check current state

    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
    • +4No input/output examples
    • +1No license
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +3Description length 695: enough signal without eating the budget
    • +4Structure: 5 headings
    • +3Step-by-step instructions: 33 items
    • +3Output format is stated explicitly

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 87.