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 MingyiSecLab/Mingyi-Atlas --skill bounty-huntinggit clone --depth 1 https://github.com/MingyiSecLab/Mingyi-AtlasWrote 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/mingyiseclab/mingyi-atlas/bounty-hunting)<a href="https://agentmods.dev/skills/mingyiseclab/mingyi-atlas/bounty-hunting"><img src="https://agentmods.dev/badge/skills/mingyiseclab/mingyi-atlas/bounty-hunting/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/mingyiseclab/mingyi-atlas/bounty-hunting"><img src="https://agentmods.dev/badge/skills/mingyiseclab/mingyi-atlas/bounty-hunting.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 | $0.00035 | $0.01301 |
| Opus 5 | $0.00017 | $0.00651 |
| Sonnet 5 | $0.00007 | $0.00260 |
| Haiku 4.5 | $0.00003 | $0.00130 |
Grade A, and why
bounty-hunting scanned grade A with 1 finding 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 12d 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.
Runs shell commandslowCapability
Expected in a hook, worth knowing in a rule or an instructions file.
- `exec()`, `eval()`, `spawn()`, `subprocess.run()`, `os.system()` This is a copy
91% identical to bounty-hunting-methodology — 5 lines 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 — 131 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Bug Bounty Hunting Methodology
You are not scanning. You are reading code, mapping architecture, and proving exploitability. Volume is the enemy — signal is the metric. Every report must survive triage by an experienced security engineer.
Target Assessment
Before committing iteration budget, evaluate the target:
- Impact surface: downloads/week, GitHub stars, dependency depth. A vuln in lodash or React Router has 10-100x the impact of a vuln in a 200-star project.
- Trust boundary complexity: Does the app load config from untrusted sources? Handle plugins? Parse user-controlled serialized data? Multi-tenant auth? Complex trust boundaries = more attack surface.
- Security advisory history: Check
github.com/advisories?query=<package>. Projects that accept and credit researchers will work with you. Projects with zero advisories are either very secure or don't have a disclosure process. - Reward program: HackerOne, Bugcrowd, Immunefi, GitHub Security Advisories, Google VRP. Check scope, excluded vuln classes, and reward tiers.
Record the assessment as a node:
kg_add_node("repo", "<name>", props={"stars": N, "downloads_weekly": N,
"has_security_policy": true, "advisory_count": N, "bounty_program": "hackerone"})
White-Box Methodology
This is the core loop. Fork. Read. Trace. Prove.
Step 1 — Map the project
find /workspace/target -name 'package.json' -o -name 'pyproject.toml' \
-o -name 'go.mod' -o -name 'Cargo.toml' -o -name 'composer.json' | head -20
Identify: language, framework, entry points, config loading, auth middleware.
Step 2 — Map trust boundaries
Where does untrusted input enter the system? Trace these sources:
- HTTP request params, headers, body
- Environment variables and
.envfiles - Config files from current directory (
.gemini/settings.json,.vscode/settings.json) - Plugin/extension loading paths
- Deserialization of user-controlled data (pickle, YAML, JSON with class hints)
- IPC channels, WebSocket messages, MCP tool inputs
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.
- 12d ago First seen · 131 lines · 35 tokens per session scan A 317b91979b7e
bounty-hunting is a skill published in the GitHub repository MingyiSecLab/Mingyi-Atlas (11 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 35 tokens to every session and 1,301 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (runs shell commands). It is 91% identical to bounty-hunting-methodology, differing in 5 lines, and is treated as a copy.
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