SKILLEMALL.ai
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AC parallel

High-accuracy web search and research via Parallel.ai API. Optimized for AI agents with rich excerpts and citations. Supports agentic mode for token-efficient multi-step reasoning.

ClawHub Agent Skills author: Matt Van Horn v1.2.1 10 files · 1 script body ≈ 485 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process C 52/100 · Has gaps — weak spots: when it triggers, inputs and preconditions, failures and branches

IntegrationAI and agentsInfrastructureResearchtype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
A
92/100
safety, quality, tests
Safety 60%
99
Quality 40%
82
Run on models
none yet
Process rating
C
52/100
Has gaps
Inputs and preconditions w 11
0
Failures and branches w 10
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 · 1

    ✓ No critical or high findings

    Medium and low: 1
    • low Secrets in code secret-high-entropy-token scripts/search.py:14
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      API_KEY = os.environ.get("PARALLEL_API_KEY", "y2s_…jg1")
      quoted

    Files scanned: 10. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "repository"
    • note frontmatter-key unknown frontmatter key "homepage"
    • note frontmatter-key unknown frontmatter key "triggers"

    Process rating: all ten parameters 52/100

    • 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
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 1 mutating operations with no state check
    • 60Tools and files. Uses tools (web, python) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 100Steps. 12 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 485 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

    • +5Description has no quoted example phrases that should trigger the skill
    • +4Description does not say when NOT to use the skill (false activations)
    • -35 of 6 scripts are never mentioned in SKILL.md
    • +2Single-language instructions
    • +3Description length 180: enough signal without eating the budget
    • +4Structure: 7 headings
    • +3Step-by-step instructions: 12 items
    • +3Output format is stated explicitly
    • +4Has examples (3 code blocks)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 82.

    External checks

    ClawHub: suspicious
    The skill is a real Parallel.ai research integration, but it contains under-disclosed high-impact behavior including a hardcoded API key fallback, third-party data transmission, authenticated browsing credential handling, and remote monitor lifecycle actions.
    LLM: suspicious (high) · VirusTotal: suspicious · 10 Sept 2026