Individual skill
browser-auth-flow
Probe a site's authentication flow for redirect leaks, missing CSRF, weak session cookies, and OAuth misconfiguration; produces an auth findings.md
plugins/ruflo-browser/skills/browser-auth-flow/SKILL.md
Source commit 722232566ad1fc4e73ba4fefc46d77d380ce61ba
What was checked
- Matches the observed repository HEAD
- Yes
- Scanner rules are current
- Yes
- Repository scan complete
- No
- Skill scan complete
- Yes
- Observed behavior
- Network or broad shell
- Last checked
- Nov 6, 2026
These are observations at the recorded commit, not a guarantee of safety. Review the source before running it.
Install this skill
Choose an agent in your Stack or copy a command below. Commands fetch the pinned commit and copy only this skill directory. Existing destinations are refused. Copying a command does not install anything.
An install command is unavailable: current rules, complete repository and skill scans, an eligible behavior level and a permitted license are required.
Repository context
- Repository stars (not skill ratings)
- 81,159
- Repository last updated
- Nov 6, 2026
- License
- MIT
Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.
- swarm Repository topic
- agentic-ai Repository topic
- agentic-framework Repository topic
- agentic-workflow Repository topic
- autonomous-agents Repository topic
- codex Repository topic
- mcp-server Repository topic
- multi-agent Repository topic
- ai-assistant Repository topic
- multi-agent-systems Repository topic
- security Derived category
- security-application Derived category
Files and excerpt
File metadata and permitted, redacted excerpts come from the recorded scan. Source links are pinned to that commit.
Browse files
SKILL.mdcore · 3,809 bytes
Read excerpt
# Browser Auth Flow
Adversarial probe of a site's authentication. Drives the login flow once, records the trajectory, then runs a configurable set of probes against the captured artifacts and live page. Output is a structured `findings.md` inside the RVF container.
## When to use
- Pre-deployment audit of a new auth flow.
- Investigating a suspected token leak or redirect issue.
- Establishing a baseline for ongoing regression checks.
## Steps
1. **Open a recorded session** via `browser-record`.
2. **Drive the auth flow** as in `browser-login` (credentials come from `--credentials <handle>` referencing `browser-cookies` if the run is a re-auth probe).