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 Bilal140202/the-lord-of-the-skills --skill fmarzochi__egcgit clone --depth 1 https://github.com/Bilal140202/the-lord-of-the-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/bilal140202/the-lord-of-the-skills/fmarzochi__egc)<a href="https://agentmods.dev/skills/bilal140202/the-lord-of-the-skills/fmarzochi__egc"><img src="https://agentmods.dev/badge/skills/bilal140202/the-lord-of-the-skills/fmarzochi__egc/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/bilal140202/the-lord-of-the-skills/fmarzochi__egc"><img src="https://agentmods.dev/badge/skills/bilal140202/the-lord-of-the-skills/fmarzochi__egc.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 | $0.00070 | $0.02297 |
| Opus 5.5 | $0.00028 | $0.00919 |
| Sonnet 5.5 | $0.00014 | $0.00459 |
| Haiku 4.5 | $0.00007 | $0.00230 |
Grade A, and why
agent-architecture-audit 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.
This is a copy
95% identical to agent-architecture-audit — 100 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 257 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Architecture Audit
A diagnostic workflow for agent systems that hide failures behind wrapper layers, stale memory, retry loops, or transport/rendering mutations.
When to Activate
MANDATORY for:
- Releasing any agent or LLM-powered application to production
- Shipping features with tool calling, memory, or multi-step workflows
- Agent behavior degrades after adding wrapper layers
- User reports "the agent is getting worse" or "tools are flaky"
- Same model works in playground but breaks inside your wrapper
- Debugging agent behavior for more than 15 minutes without finding root cause
Especially critical when:
- You've added new prompt layers, tool definitions, or memory systems
- Different agents in your system behave inconsistently
- The model was fine yesterday but is hallucinating today
- You suspect hidden repair/retry loops silently mutating responses
Do not use for:
- General code debugging: use
agent-introspection-debugging - Code review: use language-specific reviewer agents
- Security scanning: use
security-revieworsecurity-review/scan - Agent performance benchmarking: use
agent-eval - Writing new features: use the appropriate workflow skill
The 12-Layer Stack
Every agent system has these layers. Any of them can corrupt the answer:
| # | Layer | What Goes Wrong |
|---|---|---|
| 1 | System prompt | Conflicting instructions, instruction bloat |
| 2 | Session history | Stale context injection from previous turns |
| 3 | Long-term memory | Pollution across sessions, old topics in new conversations |
| 4 | Distillation | Compressed artifacts re-entering as pseudo-facts |
| 5 | Active recall | Redundant re-summary layers wasting context |
| 6 | Tool selection | Wrong tool routing, model skips required tools |
| 7 | Tool execution | Hallucinated execution: claims to call but doesn't |
| 8 | Tool interpretation | Misread or ignored tool output |
| 9 | Answer shaping | Format corruption in final response |
| 10 | Platform rendering | Transport-layer mutation (UI, API, CLI mutates valid answers) |
| 11 | Hidden repair loops | Silent fallback/retry agents running second LLM pass |
| 12 | Persistence | Expired state or cached artifacts reused as live evidence |
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 · 257 lines · 70 tokens per session scan A 65f81b8684fa
agent-architecture-audit is a skill published in the GitHub repository Bilal140202/the-lord-of-the-skills (6 stars, last pushed 1mo ago), licensed MIT. It adds 70 tokens to every session and 2,297 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 95% identical to agent-architecture-audit, differing in 100 lines, and is treated as a copy.
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