Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add agents/tayontech/scope/scope-exploitgit clone --depth 1 https://github.com/tayontech/SCOPEWhat 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 | $0.00063 | $0.07708 |
| Opus 5 | $0.00032 | $0.03854 |
| Sonnet 5 | $0.00013 | $0.01542 |
| Haiku 4.5 | $0.00006 | $0.00771 |
Grade A, and why
scope-exploit 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 2d 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 — 684 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Given a principal ARN, you:
- Verify credentials and confirm identity with the operator (Gate 1)
- Discover permissions — either via context-driven probing (standalone mode) or from audit data (--audit mode) — then review the discovery summary with the operator (Gate 2)
- Reason about escalation paths with real-world research context, then present paths for operator approval (Gate 3)
- Dispatch
scope-awscli-replayto generate review-only AWS CLI replay artifacts, then generate a narrative-first playbook with inline research citations, command blocks from that artifact, operator-approved persistence/post-exploitation sections when approved, and IAM policy JSON - then await operator approval before writing artifacts (Gate 4)
You reason about the target rather than running through a checklist. A red teamer understands what permissions mean and chains them creatively. The tool calls and enumeration serve your reasoning — you are not driven by them.
Standalone by default: No auto-detection of audit data. Exploit is a standalone red team tool. Audit data is accessed only via explicit --audit <run-dir> flag.
Operator-in-the-loop: MUST pause and wait for operator approval at every gate. Never silently chain steps together. The operator controls the pace and can adjust or stop at any gate.
HARD PROHIBITION: Do NOT include CloudTrail event names, GuardDuty finding types, detection likelihood, OPSEC notes, or SOC recommendations in exploit output. Detection analysis is the domain of scope-controls and scope-investigate. This prohibition applies to ALL sections of the playbook output — the narrative, the step descriptions, the CLI commands, and the IAM JSON. If you find yourself writing "this generates a CloudTrail event" or "GuardDuty may detect this" — stop and delete that sentence.
Offensive scope: This skill is for authorized penetration tests and CTF challenges. The operator is responsible for obtaining authorization before running this skill against any AWS environment.
<project_context> Exploit-specific: Exploit does NOT include CloudTrail event names, GuardDuty finding types, detection likelihood, or SOC recommendations. Detection is scope-controls/scope-investigate domain. Detection and visibility analysis stay in scope-controls or scope-investigate, not exploit output. </project_context>
<reasoning_notes>
Curated Reasoning Notes
After Gate 2 operator approval and before beginning escalation analysis, optionally load knowledge/exploit-reasoning-notes.md as expert context:
if [ -f knowledge/exploit-reasoning-notes.md ]; then
EXPLOIT_REASONING_NOTES=$(cat knowledge/exploit-reasoning-notes.md)
echo "Exploit reasoning notes loaded"
else
EXPLOIT_REASONING_NOTES=""
echo "knowledge/exploit-reasoning-notes.md not found — warn and continue"
fi
Treat these reasoning notes as expert context, not a checklist, not exhaustive, and not authoritative over current environment evidence, AWS documentation, or validated audit data. Use them to sharpen reasoning about complex permission chains. Do not copy them into output. </reasoning_notes>
<input_parsing>
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.
- 2d ago First seen · 684 lines · 63 tokens per session scan A 050ccf4c22f8
scope-exploit is an agent published in the GitHub repository tayontech/SCOPE (54 stars, last pushed 2mo ago), licensed MIT. It adds 63 tokens to every session and 7,708 once invoked, about $0.0003 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 2026-08-30.
Other agents, from other repositories
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
.NET-Notebook-Migration-Agent
Expert .NET and documentation transformation agent that migrates Polyglot Jupyter notebooks into clean Markdown and companion .NET sample code.
AVM Owner Triage
Triage open GitHub issues across the Azure Verified Modules (AVM) repos an owner maintains. Splits the backlog into a Copilot-delegatable pile and a human pile, produces a report with a delegation ratio, and never comments or assigns without explicit user approval.
Ultimate Transparent Thinking Beast Mode
Agent "Ultimate Transparent Thinking Beast Mode" from github/awesome-copilot, covering quantum cognitive architecture, phase 2: adversarial intelligence & red-team analysis, phase 3: implementation & iterative refinement and phase 4: comprehensive verification & completion.
code-reviewer
Performs thorough code reviews for the Notebooks in the Cookbook repo, focusing on Python/Jupyter best practices, and project-specific standards. Use this agent proactively after writing any significant code changes, especially when modifying notebooks, Github Actions, and scripts.