SKILLEMALL.ai

AB llm-agent

Build a chat, reasoning, or tool-calling agent on top of Runware-hosted LLMs. Use when the user says "make an agent that can call my functions", "let the model use these tools", "wire up an LLM over my API", "chat assistant with function calling", "give the model access to my database/search", or wants multi-step reasoning that runs the user's own code. Covers the OpenAI-compatible chat-completions + tool-calling loop, not the imageInference task shape.

ClawHub Agent Skills author: runware v1.0.0 MIT-0 3 files body ≈ 1 842 tokens Open the sourceclawhub.ai analyzed 2 d ago

Build a chat, reasoning, or tool-calling agent on top of Runware-hosted LLMs.

As a process B 70/100 · Nearly there — weak spots: result and completion, running it twice, progress reporting

GeneratorAI and agentstype 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
B
70/100
Nearly there
Result and completion w 14
0
Progress reporting w 2
0
Running it twice w 4
30
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: 3. 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 70/100

    • 0Result and completion. Does not say what the result is
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 11 mutating operations with no state check
    • 55Failures and branches. 1 branches
    • 70When it triggers. States when to use, but not when not to
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Tools and files. No external tools needed
    • 100Steps. 32 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1842 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
    • +4No input/output examples
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 5 example trigger phrases
    • +3Description length 457: enough signal without eating the budget
    • +4Structure: 8 headings
    • +3Step-by-step instructions: 32 items
    • +4Reference files are cited in the instructions (1 of 1)

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

    External checks

    ClawHub: clean
    This skill is a documentation-only guide for building Runware-hosted LLM agents and does not include hidden execution, persistence, or unrelated data access.
    LLM: benign (high) · VirusTotal: · 18 Jul 2026