# hackerone

Parses HackerOne scope tables, runs parallel pentesting agents per asset, and formats verified reports after checking exploit PoCs.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 326 installs · ★ 595
- Author: transilienceai
- License: MIT · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/transilienceai/communitytools/tree/HEAD/skills/hackerone/SKILL.md
- Updated: 2026-07-29
- HTML: https://affpapa.org/en/nevibecode/skills/hackerone

## What it does

This skill automates bug bounty routines for HackerOne engagements. It uses `skills/hackerone/tools/csv_parser.py` to ingest program scope and filters out any asset where `eligible_for_submission` is not true. For each eligible target, the orchestrator spawns an isolated background coordinator. Running ten assets in parallel typically cuts execution down to 2-4 hours instead of 20-40 hours sequentially.

Submission safety relies entirely on the `validate-findings` gate. The skill blocks unverified bugs from reaching submission drafts. Every candidate finding must supply a `poc.py`, execution proof in `poc_output.txt`, manual steps, and raw evidence. The workflow cross-checks CVE data against NVD and CISA KEV, recomputes the CVSS score from the vector, and runs adversarial refutation votes to eliminate false positives.

Rejected findings get permanently filed under `artifacts/false-positives/` and are excluded from all summaries. Once an issue receives a `VALID` or `REPAIRED` verdict, the skill generates a compliant markdown report and checks its structure with `report_validator.py`.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add transilienceai/communitytools --skill hackerone -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/hackerone/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/hackerone/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add transilienceai/communitytools --skill hackerone -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/hackerone/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add transilienceai/communitytools --skill hackerone -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/hackerone/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install transilienceai/communitytools hackerone
```

2. **Manually: put the files in place**
- File: `.github/skills/hackerone/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add transilienceai/communitytools --skill hackerone -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/transilienceai/communitytools --path skills/hackerone
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/hackerone/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add transilienceai/communitytools --skill hackerone -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/transilienceai/communitytools/tree/HEAD/skills/hackerone/SKILL.md

## When to use it
- parse HackerOne scope CSV files and isolate eligible targets
- orchestrate background testing agents across multiple assets in parallel
- eliminate false positives through adversarial validation and CISA KEV checks
- repair broken PoC scripts to prove exploitability
- compile platform-ready markdown reports with recalculated CVSS scores

## Example prompts
- Parse scope.csv and spin up coordinators for all eligible targets
- Run validate-findings on asset output directory with strict mode enabled
- Generate HackerOne submission reports from the validated artifacts

## Source text · SKILL.md · MIT

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BEGIN THIRD-PARTY FILE 764cd937b204fb45
````text
---
name: hackerone
description: HackerOne bug bounty automation - parses scope CSVs, deploys parallel pentesting agents per asset, validates PoCs, and generates platform-ready submission reports.
---

# 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
})
````
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