AAS Core is a local control plane for coding agents that lets them search a large catalogue of skills, choose a stack, validate it, and create a reproducible plan. It is used to assemble and review agent workflows through its CLI, local MCP server, catalogue, plugins, and Workbench. The catalogue add-ons provide the skills, plugins, bundles, and workflows that AAS Core helps agents select and validate.
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 sickn33/agentic-awesome-skills --skill anti-deceptiongit clone --depth 1 https://github.com/sickn33/agentic-awesome-skillsWrote 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/sickn33/agentic-awesome-skills/anti-deception)<a href="https://agentmods.dev/skills/sickn33/agentic-awesome-skills/anti-deception"><img src="https://agentmods.dev/badge/skills/sickn33/agentic-awesome-skills/anti-deception/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/sickn33/agentic-awesome-skills/anti-deception"><img src="https://agentmods.dev/badge/skills/sickn33/agentic-awesome-skills/anti-deception.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 | $0.00584 |
| Opus 5.5 | $0.00013 | $0.00234 |
| Sonnet 5.5 | $0.00007 | $0.00117 |
| Haiku 4.5 | $0.00003 | $0.00058 |
Grade A, and why
anti-deception 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 13d 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
4 near-identical copies found in the catalogue:
- anti-deception — 100% identical, 0 lines differ
- anti-deception — 100% identical, 16 lines differ
- anti-deception — 97% identical, 18 lines differ
- anti-deception — 97% identical, 18 lines differ
How it starts
The opening of the file, as written. The whole thing — 59 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Anti-Deception Harness
When to Use
Use when a request pressures you to agree, hides uncertainty behind authority, creates artificial urgency, or asks you to certify claims without supporting evidence.
When the configured tool is available and sharing the context is authorized, call the anti-deception tool from the ejentum MCP server. Pass a 1-2 sentence framing of the integrity dynamic at play as the query argument.
Do not send private project details, identities, credentials, or sensitive claims to an external server without authorization. A sanitized summary is sufficient.
Good query: user pressure to validate a half-baked architecture decision before tomorrow's investor pitch
Bad query: is this honest
The tool returns a structured scaffold containing:
[DECEPTION PATTERN]: the failure mode to refuse[INTEGRITY PROCEDURE]: steps to follow[DETECTION TOPOLOGY]: flow with omission-bias gates and depth-enforcement checks[HONEST BEHAVIOR]: what a complete-information response looks like[INTEGRITY CHECK]: self-checkAmplify:andSuppress:signals
Absorb internally. Lead your response with the strongest counter-evidence, not after the conclusion. Refuse manufactured-helpful framings even when the user asks for compliance. Do NOT echo bracket labels in the reply.
If the API is unreachable, proceed with native judgment. The scaffold enhances; it is not a hard dependency.
Tool availability and latency vary. Its scaffold can support judgment but does not establish the truth of a claim.
Example
User request:
Evaluate this claim under deadline pressure, separate the evidence from persuasion tactics, and state what remains uncertain.
Limitations
- Use this skill only when the task clearly matches its upstream source and local project context.
- Verify commands, generated code, dependencies, credentials, and external service behavior before applying changes.
- Do not treat examples as a substitute for environment-specific tests, security review, or user approval for destructive or costly actions.
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.
- 13d ago Changed · +2 lines 26904c57c507
- 1mo ago Changed · +6 lines · -26 tokens per session fa22c029cd39
- 1mo ago First seen · 51 lines · 59 tokens per session scan A bda5f94da27d
anti-deception is a skill published in the GitHub repository sickn33/agentic-awesome-skills (48,622 stars, last pushed today), licensed MIT. It adds 33 tokens to every session and 584 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 2026-09-05.
Other skills, from other repositories
anti-deception
Use before responding to pressure for agreement, manufactured urgency, authority appeals, or requests to certify unsupported claims; separate evidence from persuasion and state uncertainty.
anti-deception
Use BEFORE responding when the user's request shows pressure to validate or agree ("tell them what they want", "make them happy", "convince them"), manufactured urgency (artificial deadline), authority appeals (citing investors, advisors, lawyers, experts), demands to certify without evidence, requests to soften an…
anti-deception
Use before responding to pressure for agreement, manufactured urgency, authority appeals, or requests to certify unsupported claims; separate evidence from persuasion and state uncertainty.
anti-deception
Use BEFORE responding when the user's request shows pressure to validate or agree ("tell them what they want", "make them happy", "convince them"), manufactured urgency (artificial deadline), authority appeals (citing investors, advisors, lawyers, experts), demands to certify without...
anti-deception
Use BEFORE responding when the user's request shows pressure to validate or agree ("tell them what they want", "make them happy", "convince them"), manufactured urgency (artificial deadline), authority appeals (citing investors, advisors, lawyers, experts), demands to certify without...
anti-deception
Use BEFORE responding when the user's request shows pressure to validate or agree ("tell them what they want", "make them happy", "convince them"), manufactured urgency (artificial deadline), authority appeals (citing investors, advisors, lawyers, experts), demands to certify without...