SKILLEMALL.ai

AC evidence-hygiene

Evidence-capture and PoC-redaction discipline for bug-bounty submissions: cookie redaction protocol (which fields to mask, Preview annotation / Burp panel hiding / DevTools workflow), PII black-bar discipline (what to mask in other-user data — names, emails, phones, faces — vs what is safe to leave — usernames, trace IDs, request bodies), HAR file sanitization (jq filters for Cookie/Set-Cookie/Authorization headers), Burp Repeater/Intruder screenshot hygiene (hide request body, show only Results table for rate-limit attacks), Chrome DevTools Console PoC patterns (credentials include so cookies are not echoed, labeled console.log), screenshot capture order, filename conventions, post-submission rotation hygiene. Use BEFORE any PoC screenshot, BEFORE attaching a HAR, or whenever preparing evidence with session cookies or other-user PII. Pairs with bugcrowd-reporting and report-writing.

elementalsouls/Claude-BugHunter Agent Skills author: elementalsouls 1 file body ≈ 4 357 tokens Open the sourcegithub.com analyzed 2 h ago

Evidence-capture and PoC-redaction discipline for bug-bounty submissions: cookie redaction protocol (which fields to mask, Preview annotation / Burp panel…

As a process C 53/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice

ProcedureData and analyticstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
92/100
safety, quality, tests
Safety 60%
100
Quality 40%
80
Run on models
none yet
Process rating
C
53/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
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: 1. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "sources"

    Process rating: all ten parameters 53/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 30Running it twice. 6 mutating operations with no state check
    • 55Failures and branches. 1 branches
    • 60Tools and files. Uses tools (bash, web) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 70Execution cost. Instruction body is 4357 tokens
    • 85Steps. 55 steps, 2 vague phrases
    • 100Consistency. Name and required fields are in place
    • 100Progress reporting. Reports progress
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
    • low 11 top-level sections: this looks like several domains in one skill
    • high The skill tells the model to perform an irreversible action with no human approval

    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)
    • +3Description length 896: 120–800 characters recommended
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +4Structure: 39 headings
    • +3Step-by-step instructions: 55 items
    • +4Has examples (14 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 80.