SKILLEMALL.ai

AD aisa-twitter-api-command-center

Run Twitter/X research, monitoring, watchlists, and OAuth-gated posting through AIsa. Use when: the user needs one flagship Twitter skill for trend tracking, competitor monitoring, or publish-ready workflows. Supports search, watchlists, and approved posting.

ClawHub Agent Skills author: AIsa v1.0.3 MIT-0 6 files body ≈ 445 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process D 47/100 · Unfinished process — weak spots: result and completion, inputs and preconditions, failures and branches

IntegrationInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
A
92/100
safety, quality, tests
Safety 60%
100
Quality 40%
81
Run on models
none yet
Process rating
D
47/100
Unfinished process
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

The same skill appears in 1 more place: ClawHub

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

    • note frontmatter-key unknown frontmatter key "homepage"
    • note frontmatter-key unknown frontmatter key "source"
    • note frontmatter-key unknown frontmatter key "primaryEnv"
    • note frontmatter-key unknown frontmatter key "requires"

    Process rating: all ten parameters 47/100

    • 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. 4 mutating operations with no state check
    • 50When it triggers. No condition that starts the skill
    • 60Tools and files. Uses tools (python) that frontmatter does not declare
    • 100Steps. 19 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 445 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

    • +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
    • -41 reference files, but SKILL.md never points to them: the model will not open them
    • +2Single-language instructions
    • +3Description length 259: enough signal without eating the budget
    • +4Structure: 7 headings
    • +3Step-by-step instructions: 19 items
    • +4Has examples (0 code blocks)
    • +3All 2 scripts are documented
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 81.

    External checks

    ClawHub: suspicious
    The skill does what it says, but it exposes the AIsa API key in normal command output, which can leak a sensitive credential into logs or agent transcripts.
    LLM: suspicious (high) · VirusTotal: · 29 May 2026