Hackerone logo

Hackerone

Organization
transilienceai
hackerone

HackerOne bug bounty automation - parses scope CSVs, deploys parallel pentesting agents per asset, validates PoCs, and generates platform-ready submission reports.

Overview

Publishertransilienceai
Repositorycommunitytools
Skill namehackerone
Stars
565
Forks
81
Bundled files
6
LicenseMIT
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.

  • 6 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by transilienceai on GitHub. Read the source before you install it.

Installation

Install the Hackerone 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/transilienceai/communitytools/tree/main/skills/hackerone
  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/transilienceai/communitytools.git /tmp/communitytools
mkdir -p .claude/skills
cp -r /tmp/communitytools/skills/hackerone .claude/skills/hackerone
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Hackerone 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 Hackerone 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 Hackerone 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.

HackerOne Bug Bounty

Automates: scope parsing → parallel testing per asset → authoritative finding validation → submission reports.

Quick start

  1. Input: HackerOne program URL or scope CSV.
  2. Parse scope and program guidelines.
  3. Spawn one coordinator per eligible asset (parallel).
  4. Each coordinator runs the standard engagement flow (see skills/coordination/SKILL.md).
  5. Run the validate-findings workflow per asset (authoritative submission gate). Generate HackerOne reports from the validated set ONLY — never submit a finding that is not VALID/REPAIRED.

Scope CSV format

Expected columns:

  • identifier — asset URL/domain.
  • asset_type — URL, WILDCARD, API, CIDR.
  • eligible_for_submission — must be true.
  • max_severity — critical / high / medium / low.
  • instruction — asset-specific notes.

Parse with skills/hackerone/tools/csv_parser.py. Filter for eligible_for_submission=true.

Agent deployment

One coordinator per asset, spawned in parallel:

python
coordinator_role = Read("skills/coordination/SKILL.md")
Agent(prompt=f"{coordinator_role}\n\nTARGET: {asset_url}\nSCOPE: {program_guidelines}\nOUTPUT_DIR: ...",
      run_in_background=True)

10 assets → 10 parallel coordinators (~2-4 h vs 20-40 h sequential). Each coordinator follows skills/coordination/SKILL.md and reference/role-matrix.md.

Finding validation (authoritative submission gate)

Every finding requires poc.py (executable exploit), poc_output.txt (timestamped execution proof), manual repro steps, and evidence (screenshots / HTTP captures / video). This is the input the validator consumes.

After each asset's coordinator returns, the orchestrator runs the validate-findings workflow for that asset and submits ONLY findings it marks VALID or REPAIRED:

python
v = Workflow(name="validate-findings", args={
    "output_dir": asset_output_dir,   # the coordinator's OUTPUT_DIR
    "target": asset_url,
    "business_tier": "revenue",       # production/external bug-bounty scope; else "unknown"
    "votes": 3,                       # adversarial refuters per finding (bounty = stricter)
    "repair": True,                   # regenerate broken/absent PoCs so each finding has a runnable evidence script
    "strict": True,                   # one failed gate => REJECTED
})
# v.counts -> {total, valid, repaired, rejected}; v.validated[] / v.rejected[]
# Verdicts at {asset_output_dir}/artifacts/validated/{id}.json (+ false-positives/).
# Asset report at {asset_output_dir}/reports/validation-report.md.

validate-findings is the authoritative gate because it directly preempts the most common HackerOne rejections: it verifies each CVE against NVD + a recomputed CVSS base score (from the vector) + CISA KEV + the vendor advisory, runs (and repairs) every PoC until it emits the evidence that proves the issue, recomputes the risk/severity, corroborates every claim against raw evidence, and kills false positives via adversarial refutation.

The orchestrator is the only layer that runs this workflow (it is top-level; the workflow is one level below — legal nesting). To avoid double work, coordinators may treat their interleaved per-finding validators (run the instant each candidate is materialized) as a provisional self-check; the standalone validate-findings verdict is authoritative for submission. A REJECTED finding is a false positive — it never enters a submission, an appendix, or a count; its artifacts/false-positives/{id}.json is the sole record. Likewise a coordinator-dropped candidate (uncured DEMOTED, in artifacts/dropped/) is never submitted.

The HackerOne PoC contract is a superset of the standard finding contract (skills/coordination/reference/validator-role.md).

Submission report format

Build each report from a validated finding only. Required sections per HackerOne standard:

  1. Summary (2-3 sentences).
  2. Severity (CVSS + business impact; v4.0 primary, v3.1 → v3.0 → v2.0 fallback) — use the score validate-findings recomputed from the vector and the risk bucket it assigned, not a hand-typed number.
  3. Steps to Reproduce (numbered, clear) — mirror the validated verification-script.py.
  4. Visual Evidence (from evidence/validation/).
  5. Impact (realistic attack scenario).
  6. Remediation (actionable fixes).

Validate report format with skills/hackerone/tools/report_validator.py (complements the finding validation).

Output structure

Standard OUTPUT_DIR (skills/coordination/reference/output-discipline.md) plus a per-asset reports/submissions/ containing the platform-ready markdown. validate-findings writes the verdicts under artifacts/validated|false-positives/ and the asset reports/validation-report.md.

{OUTPUT_DIR}/
├── findings/
│   └── finding-NNN/evidence/validation/   # validate-findings proof packages
├── reports/
│   ├── submissions/                        # built ONLY from artifacts/validated/
│   │   ├── H1_CRITICAL_001.md
│   │   └── H1_HIGH_001.md
│   ├── validation-report.md                # validate-findings asset report
│   └── SUBMISSION_GUIDE.md
├── recon/
├── logs/
└── artifacts/
    ├── validated/          # {id}.json — submit these
    └── false-positives/    # {id}.json — never submit these

Program selection

High-value: new programs (< 30 days), fast response (< 24 h), high bounties, large attack surface. Avoid: slow response (> 1 week), low bounties, restrictive scope.

Submission checklist

  • validate-findings verdict = VALID or REPAIRED (artifacts/validated/{id}.json exists). Never submit a REJECTED finding.
  • CVSS recomputed from the vector (v4.0 primary; v3.1 → v3.0 → v2.0 fallback) and matching NVD when a CVE applies (evidence/validation/cve-verification.md).
  • Working PoC with poc_output.txt (the validator re-ran/repaired it and confirmed the evidence token).
  • Step-by-step reproduction.
  • Visual evidence.
  • Realistic impact (risk score/bucket from evidence/validation/risk-assessment.md).
  • Remediation guidance.
  • Sensitive data sanitized.
  • Asset is eligible_for_submission=true.

Common rejections (preempt)

RejectionPrevention
Out of ScopeVerify eligible_for_submission=true and asset-type match
Cannot Reproducevalidate-findings re-runs (and repairs) the PoC 3× and requires a vuln-class evidence token — only reproducible findings pass the gate
DuplicateSearch disclosed reports before submission; submit quickly
Insufficient ImpactDocument realistic attack scenario; validate-findings recomputes risk/severity so the rating is defensible
Incorrect severity / CVSSvalidate-findings recomputes the base score from the vector and cross-checks NVD + KEV; any mismatch is flagged before you submit

Tools

  • skills/hackerone/tools/csv_parser.py — parse HackerOne scope CSVs.
  • validate-findings workflow — authoritative per-asset finding validation (NVD/CVSS-math/KEV/exploit-run/repair/risk/adversarial). The submission gate; run once per asset.
  • skills/hackerone/tools/report_validator.py — validate report format completeness (the 6 submission sections). Complements validate-findings (which validates the finding); run it on each report built from a validated finding.
  • skills/coordination/SKILL.md — coordinator scaffold.

Usage

bash
/hackerone <program_url_or_csv_path>

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Hackerone AI skill do?

HackerOne bug bounty automation - parses scope CSVs, deploys parallel pentesting agents per asset, validates PoCs, and generates platform-ready submission reports.

Why use Hackerone on TypingMind?

Because you install it once and use it with any model. Hackerone 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 Hackerone in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/transilienceai/communitytools/tree/main/skills/hackerone. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Hackerone?

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 Hackerone?

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

Is the Hackerone AI skill free?

Yes. It is published on GitHub by transilienceai under the MIT license. 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇