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 wrg32786/aigent-os --skill diagnosegit clone --depth 1 https://github.com/wrg32786/aigent-osWrote 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/wrg32786/aigent-os/diagnose)<a href="https://agentmods.dev/skills/wrg32786/aigent-os/diagnose"><img src="https://agentmods.dev/badge/skills/wrg32786/aigent-os/diagnose/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/wrg32786/aigent-os/diagnose"><img src="https://agentmods.dev/badge/skills/wrg32786/aigent-os/diagnose.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 | $0.00057 | $0.01546 |
| Opus 5.5 | $0.00023 | $0.00618 |
| Sonnet 5.5 | $0.00011 | $0.00309 |
| Haiku 4.5 | $0.00006 | $0.00155 |
Grade A, and why
Diagnose 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.
How it starts
The opening of the file, as written. The whole thing — 161 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Diagnose
Most failures in an aigent-OS install aren't bugs in any single component — they're integration failures across layers. /diagnose walks the stack with you so you don't have to.
When to use
- "It used to work, now it doesn't"
- Caddy isn't surfacing a skill you expect
/openproduces incomplete context- Hooks not firing
- Wikilinks not resolving
- AI behavior changed unexpectedly after an update
- Anything you can't immediately attribute to a single component
How to execute
Step 1 — Capture the symptom
Ask the user (or read from their prompt):
- What were you trying to do?
- What happened instead?
- What did you expect?
- What changed recently? (git pull, new skill installed, hook edit, kernel update)
Frame the symptom in one sentence. Example: "Caddy used to surface /channel-audit when I asked about YouTube performance, now it doesn't."
Step 2 — Walk the seven-layer stack
For each layer, ask: could this layer be responsible for the symptom? If yes, run the specific check. If no, move on.
LAYER 7 — Principal Configuration
- system/00_identity.md customized?
- system/14_decision_framework.md reflects current priorities?
- vault/projects/ and vault/people/ populated?
Check first if symptom involves AI giving wrong context or missing your situation.
LAYER 6 — Doctrine (vault/concepts/)
- Has a relevant doctrine note been edited recently?
- Are there standing rules that should be firing but aren't?
Check if symptom is about AI behavior changing without you having edited the kernel.
LAYER 5 — Vault Content
- Wikilinks resolve? (run /system-check Tier 4)
- Heat index up to date?
- Memory layer current?
Check if symptom is "AI doesn't know about X" or "/open missed Y."
LAYER 4 — Daemons
- Heat compute daemon ran recently?
- Semantic search index fresh?
- Caddy reindex daemon firing?
Check if symptom is "skills not surfacing" or "search results stale."
LAYER 3 — Hooks
- Settings.json wired?
- Hook scripts executable?
- Hook output going where you expect?
Check if symptom is "automated behavior stopped."
LAYER 2 — Skills
- skill-index.json valid?
- Trigger keywords match the user's actual phrasing? (run /skill-audit Section 3)
- Skill descriptions match what the skill does?
- Caddy fired correctly? Run /caddy-explain on the recent prompt to see the deterministic scoring.
- Caddy index drift? Run /caddy-audit to see if any indexed skills are missing from disk or vice versa.
Check if symptom is "wrong skill firing" or "no skill firing."
LAYER 1 — Kernel
- All 15 system docs present and unmodified?
- CLAUDE.md loaded?
- Authority matrix consistent with what you've authorized?
Check last — kernel issues are rare but high-blast-radius.
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 · 161 lines · 57 tokens per session scan A 63b57798e2d2
Diagnose is a skill published in the GitHub repository wrg32786/aigent-os (23 stars, last pushed yesterday), licensed MIT. It adds 57 tokens to every session and 1,546 once invoked, about $0.0002 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 2026-09-01.
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