SKILLEMALL.ai

AC value-chain-analysis

Use when analyzing where profit concentrates across an industry or within a firm, decomposing business activities into primary and support functions to find competitive advantage. Triggers on "value chain", "margin analysis by activity", "where is the profit", "which activities create value", "价值链分析", "利润在哪个环节", "哪个环节利润最高", "分析企业价值活动".

ClawHub Agent Skills author: panlm v1.0.0 MIT-0 2 files body ≈ 1 506 tokens Open the sourceclawhub.ai analyzed 2 d ago

Use when analyzing where profit concentrates across an industry or within a firm, decomposing business activities into primary and support functions to find…

As a process C 57/100 · Has gaps — weak spots: result and completion, inputs and preconditions, failures and branches

AnalyzerMarketingtype and topics are labelled automatically from the skill text
JSON
Technical rating
A
96/100
safety, quality, tests
Safety 60%
100
Quality 40%
89
Run on models
none yet
Process rating
C
57/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
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: 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 57/100

    • 0Result and completion. Does not say what the result is
    • 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
    • 30Running it twice. 5 mutating operations with no state check
    • 70When it triggers. States when to use, but not when not to
    • 100Tools and files. No external tools needed
    • 100Steps. 31 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1506 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 7 example trigger phrases
    • +3Description length 337: enough signal without eating the budget
    • +4Structure: 18 headings
    • +3Step-by-step instructions: 31 items
    • +4Has examples (3 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 89.

    External checks

    ClawHub: clean
    This is a plain business-analysis skill for value chain strategy work and does not request code execution, account access, persistence, or data mutation.
    LLM: benign (high) · VirusTotal: · 29 May 2026