Borrowing it
Nothing to install: this file belongs to brovar/10x-pentest. 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/brovar/10x-pentest/main/.github/skills/pt-recon/SKILL.mdgit clone --depth 1 https://github.com/brovar/10x-pentestWrote 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/brovar/10x-pentest/pt-recon)<a href="https://agentmods.dev/skills/brovar/10x-pentest/pt-recon"><img src="https://agentmods.dev/badge/skills/brovar/10x-pentest/pt-recon/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/brovar/10x-pentest/pt-recon"><img src="https://agentmods.dev/badge/skills/brovar/10x-pentest/pt-recon.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 | $0.00108 | $0.01567 |
| Opus 5 | $0.00054 | $0.00783 |
| Sonnet 5 | $0.00022 | $0.00313 |
| Haiku 4.5 | $0.00011 | $0.00157 |
Grade A, and why
pt-recon 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 10d 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.
How it starts
The opening of the file, as written. The whole thing — 115 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/pt-recon — Subject Analysis (Stage 1)
Build the authoritative Target Profile and enumerate the attack surface, writing
target-profile.md + attack-surface.md per foundation/target-profile-schema.md. This is the
technology-independence pivot: target_classes (controlled vocab) keys the whole tool/standards
selection downstream, and S-NNN is the traceability root the rest of the engagement references.
Agent-driven and semi-autonomous — it works the codebase and passive OSINT itself, and proposes
commands for the human for anything that touches the target.
When to use, when to skip
- Use after scope is
ready/waived, to profile the target and map its surface. - Skip the active-discovery step when scope forbids target-touching traffic, or when codebase + passive analysis already saturate the surface. Do not skip the whole skill — the threat model needs the profile + surface.
Initial Response
- With
<engagement-id>: proceed to Step 0. - Without: print
Usage: /pt-recon <engagement-id>.and STOP.
Inputs
scope.md— [blocking] (validated perscope-schema§6 before any work).- Source repositories — [optional, present when
repo_access: true] — the basis for Step 1. - Human-provided active-recon outputs — [optional] — ingested in Step 2.
Process
Step 0: Validate scope + resume
Validate scope.md: readiness.status ≠ blocked; today ∈ authorization window. Failure ⇒ STOP with
the reason. Resume: if the two artifacts already exist, amend, don't clobber — new surface
items get new S-NNN (append-only, never renumber); update surface_confidence if more was mapped.
Step 1: Codebase + passive analysis (agent-autonomous, no target traffic)
If repo_access: derive architecture, components, the stack with versions (for SCA/CVE),
dependencies (SBOM-style), external integrations, and the identity/auth model (reconcile with the
scope's U-NNN accounts). Passive OSINT within RoE. Classify target_classes from the vocab and
pick primary_class; ask the operator only when genuinely ambiguous.
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.
- 10d ago First seen · 115 lines · 0 tokens per session scan A 016e0813c47c
pt-recon is a skill published in the GitHub repository brovar/10x-pentest (3 stars, last pushed 28d ago), licensed Apache-2.0. It adds 108 tokens to every session and 1,567 once invoked, about $0.0005 per session on Opus 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 2026-08-31.
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