SKILLEMALL.ai

BC tavily

use this when the user asks to search the web, look up recent information, check current events, gather online sources, or research a topic using tavily search.

ClawHub Agent Skills author: Göktuğ Ceyhan v1.0.0 MIT-0 4 files · 1 script body ≈ 112 tokens Open the sourceclawhub.ai↗ analyzed 6 d ago

Use this skill for web search and lightweight research through the Tavily Search API.

As a process C 51/100 · Has gaps — weak spots: result and completion, failures and branches, consistency

ProcedureSoftware developmenttype and topics are labelled automatically from the skill text
JSON
Technical rating
B
93/100
safety, quality, tests
Safety 60%
100
Quality 40%
83
Run on models
none yet
Process rating
C
51/100
Has gaps
Result and completion w 14
0
Failures and branches w 10
0
Progress reporting w 2
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: 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 51/100

    • 0Result and completion. Does not say what the result is
    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 40Consistency. Frontmatter name (tavily) differs from the folder (tavily-web-search-for-openclaw)
    • 60Tools and files. Uses tools (bash, web, python) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 75Steps. 3 steps
    • 100Execution cost. Instruction body is 112 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)
    • +3Output format is not stated: the model decides each time
    • +4No input/output examples
    • +1No license
    • +2Single-language instructions
    • +3Description length 160: enough signal without eating the budget
    • +4Structure: 3 headings
    • +3Step-by-step instructions: 3 items
    • +3All 1 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 83.

    In the sandbox Скрипты не запустились

    The skill's scripts were run in a throwaway machine: no network, fake keys in the home directory, a tracer watching. We wrote down what they did. Reaching for the network or for secrets caps the technical grade at C; a quiet run adds no points.

    Запущено 1 скрипт; каждому дали двадцать секунд, поддельный домашний каталог с ключами и сеть, в которой ничего нет.

    Ни один не дошёл до работы — им не хватило зависимостей, аргументов или файлов. Это не отзыв о поведении: наблюдать было не за чем.

    scripts/tavily_search.pyне запустился: tavily_search.py: error: the following arguments are required: --query

    25 Sept 2026

    External checks

    ClawHub: clean
    This is a straightforward Tavily web-search skill; it sends search queries to Tavily using a local API key, with disclosure and documentation gaps but no hidden or destructive behavior found.
    LLM: benign (high) · VirusTotal: · 29 May 2026