SKILLEMALL.ai

AC lockpicker

Guide a user through capturing and analyzing a HAR file from their own logged-in browser session, extracting the minimum auth material needed, mapping the exact request chain behind a website action, and turning that known-good browser workflow into a reusable local script. Use when a user wants to reverse-engineer a legitimate action they are already authorized to perform on a website, such as upload, publish, schedule, or queue operations, especially when browser automation is flaky and a direct authenticated web-request workflow is preferred.

ClawHub Agent Skills author: Stanislav Stankovic v1.0.1 MIT-0 11 files body ≈ 1 418 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process C 60/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

AnalyzerInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
A
96/100
safety, quality, tests
Safety 60%
100
Quality 40%
91
Run on models
none yet
Process rating
C
60/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
When it triggers w 12
20
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: 11. 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 60/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 6 mutating operations with no state check
    • 70Failures and branches. 4 branches
    • 100Tools and files. No external tools needed
    • 100Steps. 83 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1418 tokens
    • 100Progress reporting. Reports progress
    • low 12 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)
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +3Description length 551: enough signal without eating the budget
    • +4Structure: 17 headings
    • +3Step-by-step instructions: 83 items
    • +4Has examples (4 code blocks)
    • +4Reference files are cited in the instructions (5 of 5)
    • +3All 4 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 91.

    External checks

    ClawHub: suspicious
    The skill is transparent about analyzing a user's own browser session, but it stores and reuses live session credentials in ways that deserve careful review.
    LLM: suspicious (high) · VirusTotal: · 29 May 2026