Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add awrshift/agent-memory-kit/plugin install memory-kitWrote 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/awrshift/agent-memory-kit/system-audit)<a href="https://agentmods.dev/skills/awrshift/agent-memory-kit/system-audit"><img src="https://agentmods.dev/badge/skills/awrshift/agent-memory-kit/system-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/awrshift/agent-memory-kit/system-audit"><img src="https://agentmods.dev/badge/skills/awrshift/agent-memory-kit/system-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 | $0.00225 | $0.02799 |
| Opus 5.5 | $0.00090 | $0.01120 |
| Sonnet 5.5 | $0.00045 | $0.00560 |
| Haiku 4.5 | $0.00022 | $0.00280 |
Grade A, and why
system-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 21d 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:
- system-audit — 100% identical, 49 lines differ
How it starts
The opening of the file, as written. The whole thing — 167 lines — stays where its author put it; the contents beside it link to each section on GitHub.
System audit — "what works / what drifts / what is missing / what to delete"
An agent system accretes. Good patterns and bad ones, dead rules nobody reads, facts restated in five places with three of them stale, tools that only ran once. This skill is the periodic sweep that keeps it evolving without inflating: it measures which layers actually fire, deletes what doesn't, and reports findings that carry evidence rather than impressions.
The one thing that makes this audit different from a checklist: every claim must be backed by
something you touched with your own hands — a file:line, a command's output, a query result.
An unverified observation is labelled hypothesis and never counted in the verdict.
Step 0 — Scope, depth, delta (2 minutes, always)
- Depth. Pick from what the user asked, default
standard:quick— deterministic collector + lenses 1, 4, 7 inline, no subagents. ~10 min. Use for a routine "how are we doing", or when the user asks mid-work.standard— collector + all 7 lenses, lenses 2-6 fanned out to parallelreconsubagents (model="sonnet"), synthesis + adjudication inline. The default.deep— standard + an independent architecture critique (idea-validator,model="opus") and an external-family second opinion (/memory-kit:second-opinion), + a fresh-check of external facts (pricing, model ids, deprecated APIs) via WebSearch.
- Scope autodetect. Run the collector (step 1) — it reports which layers actually exist in
this repo. A lens whose layer is absent is reported as n/a, never invented. Never audit
node_modules/,.git/, build output, or vendored code. - Delta. Read the newest prior report in
context/audits/(or wherever it lives). For each of its priorities: done · partially · ignored. An audit whose last round was ignored has one finding worth more than all the others — say so first.
Step 1 — The deterministic collector (before any reasoning)
# from the repo being audited; pass a path as $1 to audit a different repo.
# The output path carries the repo name and date: a fixed /tmp name gets overwritten by a
# parallel session auditing another repo, and the first read is then of the wrong repo.
bash "${CLAUDE_PLUGIN_ROOT}/skills/system-audit/scripts/collect.sh" "$PWD" \
> "/tmp/system-audit-facts-$(basename "$PWD")-$(date +%F).md"
What ships with it
6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 21d ago Changed · +7 lines f4e2a1190e07
- 22d ago Changed · +6 lines 89df088507e4
- 1mo ago Changed 3ecf9b061c65
- 1mo ago First seen · 154 lines · 225 tokens per session scan A 2978082a5e67
system-audit is a skill published in the GitHub repository awrshift/agent-memory-kit (34 stars, last pushed 9d ago), licensed MIT. It adds 225 tokens to every session and 2,799 once invoked, about $0.0009 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 2026-08-31.
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