SKILLEMALL.ai

AB openclaw-vln-planner

Plan the next high-level navigation step for a robot from a user navigation instruction, one current image, and a sequence of historical images. Use when the task is vision-language navigation, closed-loop replanning, multimodal next-action prediction, or converting visual observations into a single structured JSON navigation action for an OpenAI-compatible multimodal gateway and a separate execution bridge.

ClawHub Agent Skills author: TIKTOKDAD v1.0.0 MIT-0 7 files body ≈ 1 550 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process B 76/100 · Nearly there — weak spots: running it twice, progress reporting

IntegrationInfrastructureAI and agentstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
98/100
safety, quality, tests
Safety 60%
100
Quality 40%
94
Run on models
none yet
Process rating
B
76/100
Nearly there
Progress reporting w 2
0
Running it twice w 4
30
Tools and files w 18
60
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: 7. 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 76/100

    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 2 mutating operations with no state check
    • 60Tools and files. Uses tools (python) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 70When it triggers. States when to use, but not when not to
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 76 steps
    • 100Failures and branches. 3 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1550 tokens
    • low 10 top-level sections: this looks like several domains in one skill

    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)
    • +1No license
    • +2Single-language instructions
    • +3Description length 411: enough signal without eating the budget
    • +4Structure: 18 headings
    • +3Step-by-step instructions: 76 items
    • +3Output format is stated explicitly
    • +4Has examples (4 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: 94.

    External checks

    ClawHub: clean
    This is a disclosed robot navigation planner with real privacy and safety cautions, but the artifacts are coherent and do not show hidden or malicious behavior.
    LLM: benign (high) · VirusTotal: · 29 May 2026