Decepticon is an autonomous red-team agent that coordinates AI agents, security tools, sandboxes, and supporting services for authorized cybersecurity assessments. Security researchers and red teams can run it through its Docker stack, cloud service, command-line interface, or Python SDK, with the catalogue entries representing its available skills.
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 skills add PurpleAILAB/Decepticon --skill oracle-manipulationgit clone --depth 1 https://github.com/PurpleAILAB/DecepticonWrote 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/purpleailab/decepticon/oracle-manipulation)<a href="https://agentmods.dev/skills/purpleailab/decepticon/oracle-manipulation"><img src="https://agentmods.dev/badge/skills/purpleailab/decepticon/oracle-manipulation/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/purpleailab/decepticon/oracle-manipulation"><img src="https://agentmods.dev/badge/skills/purpleailab/decepticon/oracle-manipulation.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 | $0.00033 | $0.01713 |
| Opus 5 | $0.00016 | $0.00856 |
| Sonnet 5 | $0.00007 | $0.00343 |
| Haiku 4.5 | $0.00003 | $0.00171 |
Grade A, and why
oracle-manipulation 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 9d 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
1 near-identical copy found in the catalogue:
- oracle-manipulation — 92% identical, 5 lines differ
How it starts
The opening of the file, as written. The whole thing — 156 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Oracle Manipulation Playbook
DeFi protocols that read a price from an on-chain source are vulnerable when the source can be moved within a single transaction or block. Classic vectors:
- Spot-price AMM oracle —
reserve1 / reserve0of a Uniswap V2 pool. Anyone with enough capital (or a flash loan) can push the price for one block. - Manipulable TWAP — short window TWAP, or TWAP over a low-liquidity pool.
- Single Chainlink feed without staleness check — feed returns 0 / stale → uses 0 in math.
- Custom oracle reading from manipulable storage — e.g., a "fair-price" oracle that reads
totalSupply()of an LP token alongside reserves. - L2 sequencer offline — L2 oracles need a "is sequencer up?" check or attacker can exploit when sequencer downs and feeds freeze.
Audit steps
1. Locate price-reading code
# Common patterns
grep -rn 'getReserves\|getAmountsOut\|getPriceFromSqrtPriceX96\|latestAnswer\|latestRoundData' src/
# Custom oracle reads
grep -rn 'IPriceOracle\|getPrice\|consult' src/
2. Trace each price use
For each call:
- Is the price read from a Uniswap V2 / Sushi / Camelot pool's reserves? → spot price = manipulable
- Is the price read from a Uniswap V3 pool's
slot0.sqrtPriceX96? → manipulable - Is it a Uniswap V3 TWAP via
OracleLibrary.consult? → check the secondsAgo window (>= 1800s = 30 min is the safe minimum) - Is it a Chainlink
latestRoundData()call? → check: isupdatedAtvalidated? IsansweredInRound >= roundId? Isanswer > 0? Are L2 sequencer feeds checked?
3. Validate staleness handling
// MISSING — vulnerable
(, int256 price, , , ) = priceFeed.latestRoundData();
// GOOD — explicit staleness + sequencer
(uint80 roundId, int256 price, , uint256 updatedAt, uint80 answeredInRound) = priceFeed.latestRoundData();
require(price > 0, "ORACLE_NEGATIVE");
require(updatedAt > block.timestamp - MAX_DELAY, "ORACLE_STALE");
require(answeredInRound >= roundId, "ORACLE_OLD_ROUND");
// On L2: also check sequencer uptime feed (L2 SequencerUptimeFeed)
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.
- 9d ago First seen · 156 lines · 33 tokens per session scan A b79358522f2f
oracle-manipulation is a skill published in the GitHub repository PurpleAILAB/Decepticon (5,491 stars, last pushed 13d ago), licensed Apache-2.0. It adds 33 tokens to every session and 1,713 once invoked, about $0.0002 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 2026-09-03.
Other skills, from other repositories
langchain-fundamentals
Create LangChain agents with createagent, define tools, and use middleware for human-in-the-loop and error handling.
langsmith-evaluator
INVOKE THIS SKILL when building evaluation pipelines for LangSmith. Covers three core components: (1) Creating Evaluators - LLM-as-Judge, custom code; (2) Defining Run Functions - how to capture outputs and trajectories from your agent; (3) Running Evaluations - locally with evaluate() or auto-run via LangSmith. Uses…
devloop
Goal-driven development loop — define objective, write rules with key-results, verify visually, sync to issue tracker.
interactive-dashboard
Interactive web dashboards: stock trackers, sector heatmaps, portfolio monitors — served via preview URL.
skill0
Root index of x-cmd skill0 sub-skills. Defines the OKR-style agent workflow (goal → rule-verified results → execute), skill discovery, and agent tooling preferences. Style: principle-first, concise, delegate specifics to authoritative external sources.
idea-generation
Stock screening and idea generation: quantitative screens, thematic analysis, shortlist.