PentestGPT is an AI-driven framework for penetration testing and capture-the-flag challenges. It guides staged workflows such as reconnaissance, asset discovery, vulnerability identification, exploitation, and reporting, using large language models to operate tools and reason about findings. The catalogue includes skills and instructions for using it.
Borrowing it
Nothing to install: this file belongs to GreyDGL/PentestGPT. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/GreyDGL/PentestGPT/main/.agents/skills/to-issues/SKILL.mdgit clone --depth 1 https://github.com/GreyDGL/PentestGPTWrote 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/greydgl/pentestgpt/to-issues)<a href="https://agentmods.dev/skills/greydgl/pentestgpt/to-issues"><img src="https://agentmods.dev/badge/skills/greydgl/pentestgpt/to-issues/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/greydgl/pentestgpt/to-issues"><img src="https://agentmods.dev/badge/skills/greydgl/pentestgpt/to-issues.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00031 | $0.00721 |
| Opus 5.5 | $0.00012 | $0.00288 |
| Sonnet 5.5 | $0.00006 | $0.00144 |
| Haiku 4.5 | $0.00003 | $0.00072 |
Grade A, and why
to-issues scanned grade A with 0 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
How it starts
The opening of the file, as written. The whole thing — 85 lines — stays where its author put it; the contents beside it link to each section on GitHub.
To Issues
Break a plan into independently-grabbable issues using vertical slices (tracer bullets).
The issue tracker and triage label vocabulary should have been provided to you — run /setup-matt-pocock-skills if not.
Process
1. Gather context
Work from whatever is already in the conversation context. If the user passes an issue reference (issue number, URL, or path) as an argument, fetch it from the issue tracker and read its full body and comments.
2. Explore the codebase (optional)
If you have not already explored the codebase, do so to understand the current state of the code. Issue titles and descriptions should use the project's domain glossary vocabulary, and respect ADRs in the area you're touching.
Look for opportunities to prefactor the code to make the implementation easier. "Make the change easy, then make the easy change."
3. Draft vertical slices
Break the plan into tracer bullet issues. Each issue is a thin vertical slice that cuts through ALL integration layers end-to-end, NOT a horizontal slice of one layer.
- Each slice delivers a narrow but COMPLETE path through every layer (schema, API, UI, tests)
- A completed slice is demoable or verifiable on its own
- Any prefactoring should be done first
4. Quiz the user
Present the proposed breakdown as a numbered list. For each slice, show:
- Title: short descriptive name
- Blocked by: which other slices (if any) must complete first
- User stories covered: which user stories this addresses (if the source material has them)
Ask the user:
- Does the granularity feel right? (too coarse / too fine)
- Are the dependency relationships correct?
- Should any slices be merged or split further?
Iterate until the user approves the breakdown.
5. Publish the issues to the issue tracker
For each approved slice, publish a new issue to the issue tracker. Use the issue body template below. These issues are considered ready for AFK agents, so publish them with the correct triage label unless instructed otherwise.
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 · 85 lines · 31 tokens per session scan A f8a72a5bc88a
to-issues is a skill published in the GitHub repository GreyDGL/PentestGPT (16,114 stars, last pushed 3mo ago), licensed MIT. It adds 31 tokens to every session and 721 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
meeting-action-items
Turn meeting notes into cited decisions, owners, tickets.
company-employee
The protocol every employee of a PenguinHarness organization follows — read the handbook first, act on [orgtrigger] work runs, schedule tickets from the desk session and do the work in ticket sessions, block instead of idling, ask the board in the all-hands channel before anything heavy, costly, irreversible or…
company-ceo
Run a PenguinHarness organization as its CEO — turn the mission into a ticket tree, hire HR and finance first, partition the shared workspace, schedule the calendar, open a channel per stream, review tickets and report to the board in the all-hands channel.
orchestrate
Toolbox for driving HumanLayer from the CLI - log in, pick a host, launch tasks in their own worktrees, launch sessions, list tasks and their PRs, and copy, move, or upload task artifacts.
company-mirror
Run a PenguinHarness organization that mirrors a real company — one digital twin per real colleague, each twin's desk bound to that colleague's chat bot, twins answering what the handbook already knows and relaying the rest to one another instead of hiring, scheduling and filing tickets.
batch-all-issues
Resolve every open issue one at a time: fact-check each with web research, close the ones that need no action, and run the goal-pr skill to fix, review, and merge the ones that do — repeating until no actionable issues remain.