SKILLEMALL.ai

AC manual-to-solution

将软件/系统操作手册转换为专业的系统解决方案建议书。适用场景: (1) 用户上传了一份操作手册/用户手册,要求转写为解决方案/方案建议书/投标方案 (2) 需要从"功能描述"升维为"业务价值+技术架构+实施规划+ROI分析"的完整方案 (3) 制作包含架构图、流程图、路线图等专业配图的方案文档 触发关键词:操作手册转解决方案、手册升级、方案建议书、解决方案文档、转写方案 Convert software/system operation manuals into professional solution proposals. Use cases: (1) User uploads an operation/user manual and needs it rewritten as a solution proposal or bid document (2) Elevating "feature descriptions" into a full proposal with business value, technical architecture, implementation planning, and ROI analysis (3) Producing proposal documents with professional diagrams (architecture, flowcharts, roadmaps, etc.) Trigger keywords: manual to solution, upgrade manual, solution proposal, solution document, rewrite proposal

ClawHub Agent Skills author: kingluu v1.0.2 MIT-0 6 files body ≈ 1 402 tokens Open the sourceclawhub.ai analyzed 5 d ago

As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

GeneratorWordProcurementInfrastructureOperations and projectstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
98/100
safety, quality, tests
Safety 60%
100
Quality 40%
96
Run on models
none yet
Process rating
C
53/100
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: 6. 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 53/100

    • 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
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 1 mutating operations with no state check
    • 60Tools and files. Uses tools (bash) that frontmatter does not declare
    • 100Steps. 23 steps
    • 100Failures and branches. 1 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1402 tokens

    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)
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 2 example trigger phrases
    • +3Description length 739: enough signal without eating the budget
    • +4Structure: 13 headings
    • +3Step-by-step instructions: 23 items
    • +4Has examples (5 code blocks)
    • +4Reference files are cited in the instructions (2 of 2)
    • +3All 2 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 96.

    External checks

    ClawHub: clean
    This appears to be a document-conversion helper with some broad triggers and shell-based setup steps, but no artifact-backed evidence of deception, exfiltration, or destructive behavior.
    LLM: benign (medium) · VirusTotal: · 29 May 2026