BC hunt-subdomain
Hunting skill for subdomain takeover vulnerabilities. Includes modern provider fingerprints — Microsoft Azure DevOps `cloudapp.azure.com` regional-pool re-issue (1-click OAuth ATO via wildcard `reply_to`, Binary Security), Zendesk help-desk takeover → email interception → password reset chain (0xprial writeup), Vercel `cname.vercel-dns.com` deleted-project takeover, plus general Fastly CDN service re-attach and S3 dangling-bucket cookie-scope techniques. Use when hunting subdomain takeover — emphasis on ATO-chain primitives (OAuth `redirect_uri`, cookie-domain, email DNS).
Hunting skill for subdomain takeover vulnerabilities.
As a process C 50/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions
How to improve
- The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
- 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 · 1
✓ No critical or high findings
Medium and low: 1
-
low Risky intent
intent-offensive-securitySKILL.md:304Offensive-security / dual-use content (legitimate for authorised testing; review intended use)- **Impact:** Highly-effective phishing campaign exploiting the parent brand. Victims trust the email because every authentication check passes. Credential harvesting, BEC fraud, supply-chain access.
Files scanned: 1. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
body-longSKILL.md body ≈ 6015 tokens (recommended < 5000); move details to references/ - note
frontmatter-keyunknown frontmatter key "sources" - note
frontmatter-keyunknown frontmatter key "report_count" - note
edit-residuethe text marks something as outdated (lines 263, 265, 272, 279): check that old rules are not kept next to new ones — the full check reads the text for contradictions
Process rating: all ten parameters 50/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Progress reporting. Says nothing while it works
- 20When it triggers. No condition that starts the skill
- 55Failures and branches. 1 branches
- 60Tools and files. Uses tools (web) that frontmatter does not declare
- 70Execution cost. Instruction body is 6015 tokens
- 100Steps. 123 steps
- 100Consistency. Name and required fields are in place
- 100Running it twice. Mutating operations check current state
- low 11 top-level sections: this looks like several domains in one skill
- low The response is described with custom markup (5 tags): a typed call is more reliable
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 579: enough signal without eating the budget
- +4Structure: 17 headings
- +3Step-by-step instructions: 123 items
- +4Has examples (10 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 72.