SKILLEMALL.ai

AC architecture-consistency-guardian

Enforce system-wide consistency before code changes. Activate for any task involving: refactoring across files, unifying variable/field/parameter names, consolidating state machines, cleaning legacy paths or fallbacks, aligning configuration sources, unifying database schema, consolidating service entry points, aligning documentation with code, or preventing local-only fixes that ignore global architecture contracts. Trigger signals: "统一一下", "全局改", "别只修当前文件", "检查所有引用", "清理 legacy", "状态机不统一", "配置路径不一致", "多个模块都要改", "架构收口", "重构", "修复兼容层", "把新旧逻辑统一", "unify", "consolidate", "clean up legacy", "align across modules", "single source of truth", "remove fallback", "schema drift". Also activate when a reported bug may stem from contract drift (e.g., mismatched field names, stale fallback paths, dual write targets, or status values not in the canonical set). NOT for: pure greenfield feature development with no cross-file impact, cosmetic lint/format changes, or single-file typo fixes with zero external references.

ClawHub Agent Skills author: upsightx v2.0.1 MIT-0 14 files body ≈ 1 871 tokens Open the sourceclawhub.ai analyzed 3 d ago

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

AnalyzerSoftware developmenttype and topics are labelled automatically from the skill text
JSON
Technical rating
A
99/100
safety, quality, tests
Safety 60%
100
Quality 40%
97
Run on models
none yet
Process rating
C
64/100
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

    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: 13. 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 64/100

    • 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. 5 mutating operations with no state check
    • 50When it triggers. No condition that starts the skill
    • 100Tools and files. No external tools needed
    • 100Steps. 57 steps
    • 100Failures and branches. 6 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1871 tokens
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing

    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

    • +3Description length 1019: 120–800 characters recommended
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 13 example trigger phrases
    • +4Description says when NOT to use the skill
    • +4Structure: 16 headings
    • +3Step-by-step instructions: 57 items
    • +4Has examples (2 code blocks)
    • +4Reference files are cited in the instructions (4 of 4)
    • +3All 3 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 97.

    External checks

    ClawHub: clean
    This skill is a disclosed repository-consistency workflow with local scanning helpers and no evidence of hidden network access, credential use, persistence, or deception.
    LLM: benign (high) · VirusTotal: · 29 May 2026