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AC migration-architect

Zero-downtime migration planning, compatibility validation, and rollback strategy generation. Tools for system, database, and infrastructure migrations with minimal business impact. Use when planning a database migration, infrastructure cutover, system replacement, or any high-risk transition that needs explicit rollback paths.

alirezarezvani/claude-skills Agent Skills author: alirezarezvani MIT 20 files body ≈ 4 085 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Zero-downtime migration planning, compatibility validation, and rollback strategy generation.

As a process C 64/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

AnalyzerTerraformInfrastructuretype and topics are labelled automatically from the skill text
Runs in: 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) OpenClaw Goose OpenCode
JSON
Technical rating
A
93/100
safety, quality, tests
Safety 60%
100
Quality 40%
83
Run on models
none yet
Process rating
C
64/100
Has gaps
Inputs and preconditions w 11
0
When it triggers w 12
20
Result and completion w 14
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: 20. 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    • note edit-residue the text marks something as outdated (lines 94, 101, 103, 177, 193, 201): check that old rules are not kept next to new ones — the full check reads the text for contradictions

    Process rating: all ten parameters 64/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 20When it triggers. No condition that starts the skill
    • 40Result and completion. Does not say what the result is
    • 50Failures and branches. 0 branches, has a failure section
    • 70Execution cost. Instruction body is 4085 tokens
    • 100Tools and files. No external tools needed
    • 100Steps. 195 steps
    • 100Consistency. Name and required fields are in place
    • 100Running it twice. Mutating operations check current state
    • 100Progress reporting. Reports progress
    • low 12 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
    • -43 reference files, but SKILL.md never points to them: the model will not open them
    • +1No license
    • +2Single-language instructions
    • +3Description length 329: enough signal without eating the budget
    • +4Structure: 46 headings
    • +3Step-by-step instructions: 195 items
    • +4Has examples (7 code blocks)
    • +3All 3 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 83.