SKILLEMALL.ai

AD competitor-monitor

Monitor competitors' websites, social media, pricing, and product changes automatically. Use when the user wants to track competitor activity, detect website changes, monitor pricing updates, track new features or blog posts, get alerts on competitor moves, or conduct ongoing competitive intelligence. Supports scheduled checks with configurable alert channels.

ClawHub Agent Skills author: John Wang v1.0.0 MIT-0 4 files body ≈ 589 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process D 40/100 · Unfinished process — weak spots: steps, result and completion, inputs and preconditions

AnalyzerData and analyticsCommerceWriting and documentstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
95/100
safety, quality, tests
Safety 60%
100
Quality 40%
88
Run on models
none yet
Process rating
D
40/100
Unfinished process
Steps w 15
0
Result and completion w 14
0
Inputs and preconditions w 11
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: 4. 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 40/100

    • 0Steps. Prose only: no discrete steps
    • 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
    • 40Consistency. Frontmatter name (competitor-monitor) differs from the folder (competitor-intel-monitor)
    • 70When it triggers. States when to use, but not when not to
    • 100Tools and files. No external tools needed
    • 100Execution cost. Instruction body is 589 tokens
    • 100Running it twice. No mutating operations

    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)
    • +3No numbered steps or checklist
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +3Description length 362: enough signal without eating the budget
    • +4Structure: 12 headings
    • +4Has examples (9 code blocks)
    • +4Reference files are cited in the instructions (1 of 1)
    • +3All 1 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 88.

    External checks

    ClawHub: suspicious
    This appears to be a real competitor website monitor, but it needs review because it overstates its features and allows broad network fetching and persistent local writes with weak safeguards.
    LLM: suspicious (high) · VirusTotal: · 29 May 2026