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 hmj1026/dhpk --skill dhpk-agent-architecture-auditgit clone --depth 1 https://github.com/hmj1026/dhpkWrote 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/hmj1026/dhpk/dhpk-agent-architecture-audit)<a href="https://agentmods.dev/skills/hmj1026/dhpk/dhpk-agent-architecture-audit"><img src="https://agentmods.dev/badge/skills/hmj1026/dhpk/dhpk-agent-architecture-audit/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/hmj1026/dhpk/dhpk-agent-architecture-audit"><img src="https://agentmods.dev/badge/skills/hmj1026/dhpk/dhpk-agent-architecture-audit.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 | $0.00122 | $0.01974 |
| Opus 5 | $0.00061 | $0.00987 |
| Sonnet 5 | $0.00024 | $0.00395 |
| Haiku 4.5 | $0.00012 | $0.00197 |
Grade A, and why
dhpk-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 7d 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 — 209 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Architecture Audit
Source: oh-my-agent-check.
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
When NOT to Use
- General code debugging — use
agent-introspection-debugging - Code review — use language-specific reviewer agents
- Security scanning — use
change-verdictsecurity mode - Agent performance benchmarking — use
agent-eval - Writing new features — use the appropriate workflow skill
Audit Model
Load references/layers-and-failure-patterns.md before mapping findings to the 12
layers or the common failure patterns. It is intentionally disclosed because most
audits need the workflow first and the full diagnostic vocabulary only during failure
mapping.
Audit Workflow
Phase 1: Scope
Define what you're auditing:
- Target system — what agent application?
- Entrypoints — how do users interact with it?
- Model stack — which LLM(s) and providers?
- Symptoms — what does the user report?
- Time window — when did it start?
- Layers to audit — which of the 12 layers apply?
Phase 2: Evidence Collection
Gather evidence from the codebase:
- Source code — agent loop, tool router, memory admission, prompt assembly
- Logs — historical session traces, tool call records
- Config — prompt templates, tool schemas, provider settings
- Memory files — SOPs, knowledge bases, session archives
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.
- 7d ago First seen · 209 lines · 122 tokens per session scan A db9aec4747df
dhpk-agent-architecture-audit is a skill published in the GitHub repository hmj1026/dhpk (2 stars, last pushed yesterday), licensed MIT. It adds 122 tokens to every session and 1,974 once invoked, about $0.0006 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 2026-09-05.
Other skills, from other repositories
plumb-line-remediate
Use when applying findings from a plumb-line audit report — the builder has a report (or pasted findings) and wants the fixes made. Opt-in and separate from the audit, which is read-only and never fixes.
generator-apply-fixes
Internal Auto-Harness generator skill for QA fix cycles. Use only inside the Generator subagent when it is addressing named defects from QA or retest.
debug
Interactive debugging workflow with hypothesis-driven probe loop. Use when: unknown bugs, script errors, silent failures, troubleshooting. Not for: known bugs (use bug-fix), GitHub issue analysis (use issue-analyze), code understanding (use code-explore). Output: debug report with probe journal + root cause + fix.
anti-patterns
Catalogue of known SDLC anti-patterns that greatcto agents must actively reject when reviewing architecture, plans, code, or post-mortems. Used by architect (pre-impl), pm (planning), senior-dev (impl), l3-support (post-incident).
windows-compat
Audit and harden this Rust repo (code-graph-mcp) for Windows correctness: path-spelling drift between producers, the 32,767-char command-line cap, index-key mismatches, and path predicates that assume one ecosystem's layout. Use whenever touching code that builds, compares, prints, or stores a filesystem path; that…
incremental-analysis
Detect existing workspace, diff current sources against high water mark metadata, classify specs as unchanged/stale/orphaned/new, re-analyze only what changed. Activates automatically when /analyze finds an existing workspace.