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Security Research

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code-yeongyu
security-research

Team Mode security research skill. Orchestrates 3 vulnerability hunters and 2 PoC engineers to audit a codebase in parallel, prove exploitability, classify root causes, and calibrate severity by actual exploitability. Use for security review, vulnerability research, exploitability audit, pre-release security check, threat model validation, and `/security-research`. Triggers: 'security-research', 'security research', 'security review', 'vulnerability audit', 'exploitability audit', '보안 리뷰', '취약점 감사'.

Overview

Publishercode-yeongyu
Repositoryoh-my-openagent
Skill namesecurity-research
Stars
70K
Forks
5.7K
Bundled files
Instructions only
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by code-yeongyu on GitHub. Read the source before you install it.

Installation

Install the Security Research AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/code-yeongyu/oh-my-openagent/tree/dev/.agents/skills/security-research
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/code-yeongyu/oh-my-openagent.git /tmp/oh-my-openagent
mkdir -p .claude/skills
cp -r /tmp/oh-my-openagent/.agents/skills/security-research .claude/skills/security-research
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Security Research in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Security Research on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Security Research is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Security Research - Team Mode Vulnerability Audit

Use this skill to run a parallel security audit that separates real exploitability from generic concern. The team has 3 vulnerability hunters and 2 PoC engineers.

Hard Preconditions

Before starting, verify:

  1. team_* tools are available. If not, stop and tell the user: security-research requires team-mode. Set team_mode.enabled: true in your oh-my-openagent config, restart opencode, then retry.
  2. You are in the main session, not a background subagent.
  3. You have a concrete target: repository, diff range, PR, release candidate, path list, or threat surface.

If the user provided no target, audit the current repository and current branch diff against its upstream or merge base. If there is no diff, audit the security-sensitive surfaces in the working tree.

Severity Standard

Use these references as the scoring frame:

Rules:

  • No severity without an attack path.
  • No critical or high finding without concrete exploit preconditions and impact.
  • Keep CWE category separate from severity.
  • Prefer a small, reproducible PoC over theoretical language.
  • Never run destructive exploits against real services or third-party systems.
  • Use local fixtures, toy payloads, dry runs, or static proof when real execution would be unsafe.

Team Roster

Create one Team Mode run with these 5 members:

MemberKindCategoryRole
surface-huntercategorydeep-lowMap entry points, trust boundaries, and reachable attack surfaces.
auth-data-huntercategoryultrabrainHunt auth, authorization, data isolation, injection, and secret handling flaws.
runtime-supply-huntercategoryunspecified-highHunt filesystem, subprocess, archive, dependency, hook, MCP, and config risks.
poc-engineer-acategoryunspecified-highBuild minimal PoCs for the strongest candidate findings.
poc-engineer-bcategorydeep-highIndependently reproduce, falsify, or downgrade candidate findings.

Call team_create with an inline spec:

typescript
team_create({
  inline_spec: {
    name: "security-research",
    description: "Parallel exploitability-driven security research team.",
    members: [
      {
        name: "surface-hunter",
        kind: "category",
        category: "deep-low",
        prompt: "You map attack surface. Enumerate entry points, trust boundaries, attacker-controlled inputs, data sinks, privilege transitions, and sensitive assets. Return evidence with file paths and exact functions. Do not assign severity unless you can name an attack path."
      },
      {
        name: "auth-data-hunter",
        kind: "category",
        category: "ultrabrain",
        prompt: "You hunt auth, authorization, tenant/data isolation, injection, SSRF, credential exposure, and confused-deputy flaws. Reason from attacker capability to impact. Return only findings with concrete exploit preconditions, CWE candidates, and verification steps."
      },
      {
        name: "runtime-supply-hunter",
        kind: "category",
        category: "unspecified-high",
        prompt: "You hunt filesystem, subprocess, archive extraction, dependency, hook execution, MCP, config, and environment-variable risks. Check path traversal, command injection, unsafe downloads, permission boundaries, and supply-chain assumptions. Cite file paths and commands used."
      },
      {
        name: "poc-engineer-a",
        kind: "category",
        category: "unspecified-high",
        prompt: "You build minimal safe PoCs for candidate findings. Use toy inputs and local-only execution. Your job is to prove or disprove exploitability, not to broaden scope. Report exact reproduction steps and expected output."
      },
      {
        name: "poc-engineer-b",
        kind: "category",
        category: "deep-high",
        prompt: "You independently reproduce candidate findings and try to falsify them. Downgrade anything without a working path. If a PoC is unsafe to run, design a safe static or dry-run proof and explain the limit."
      }
    ]
  }
})

If a category is unavailable, retry once by replacing only that category with unspecified-high. Do not reduce the team below 5 members.

Workflow

Phase 0: Scope and Baseline

Collect:

  • Target scope and reason for audit.
  • Branch, base ref, diff, and changed files if this is a change review.
  • Security-sensitive directories and files if this is a full-repo audit.
  • Existing tests and commands that exercise relevant surfaces.
  • Any user-stated constraints, such as no network calls or no destructive tests.

Use rg, git diff, git log, LSP, and existing tests before assigning work.

Phase 1: Independent Hunter Pass

Send one prompt to the 3 hunters:

text
Audit target:
{target summary}

Context:
{diff, file list, security-sensitive paths, known constraints}

Task:
Find candidate vulnerabilities in your assigned role. For each candidate include:
- title
- affected file/function
- attacker capability
- attack path
- impact
- CWE candidate
- exact evidence
- safe verification idea

Reject generic hardening advice. Return only candidates with a plausible path.

Wait for all hunters.

Phase 2: PoC Pass

Deduplicate hunter candidates. Send the strongest candidates to both PoC engineers.

Each PoC engineer must return:

  • Reproduced, falsified, or unsafe-to-run.
  • Exact commands, fixtures, or static proof.
  • Observed output or reason it fails.
  • Severity recommendation using exploitability and impact.
  • Downgrade rationale for anything not reproduced.

Phase 3: Cross-Check

Send the PoC results back to all 5 members.

Ask every member:

  • Which findings survive?
  • Which findings should be downgraded or removed?
  • What remediation is smallest and specific?
  • What regression test would prevent recurrence?

Phase 4: Final Report

Produce this report:

markdown
## Security Research Result

### Verdict
PASS | PASS WITH FINDINGS | BLOCK

### Scope
- Target:
- Base/diff:
- Commands run:

### Findings
| Severity | Title | CWE | Exploitability | Impact | PoC | Fix |
|----------|-------|-----|----------------|--------|-----|-----|

### Finding Details
For each finding:
- Evidence:
- Attack path:
- PoC:
- Severity rationale:
- Minimal fix:
- Regression check:

### Downgraded or Rejected Candidates
| Candidate | Reason |
|-----------|--------|

### Residual Risk
- What was not tested and why.

Output Rules

  • Lead with the verdict.
  • Do not bury blocking issues.
  • Do not report speculative findings as vulnerabilities.
  • Do not claim CVSS precision unless you actually scored the metrics.
  • Include exact file paths and commands for every surviving finding.
  • If no findings survive PoC, say that plainly and list residual risk.

Frequently asked questions

What does the Security Research AI skill do?

Team Mode security research skill. Orchestrates 3 vulnerability hunters and 2 PoC engineers to audit a codebase in parallel, prove exploitability, classify root causes, and calibrate severity by actual exploitability. Use for security review, vulnerability research, exploitability audit, pre-release security check, threat model validation, and `/security-research`. Triggers: 'security-research', 'security research', 'security review', 'vulnerability audit', 'exploitability audit', '보안 리뷰', '취약점 감사'.

Why use Security Research on TypingMind?

Because you install it once and use it with any model. Security Research is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Security Research in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/code-yeongyu/oh-my-openagent/tree/dev/.agents/skills/security-research. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Security Research?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Security Research?

As many as you like. As long as a model supports skills, you can use Security Research with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Security Research AI skill free?

It is published on GitHub by code-yeongyu. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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