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 Mathews-Tom/armory --skill immunegit clone --depth 1 https://github.com/Mathews-Tom/armoryWrote 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/mathews-tom/armory/immune)<a href="https://agentmods.dev/skills/mathews-tom/armory/immune"><img src="https://agentmods.dev/badge/skills/mathews-tom/armory/immune/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/mathews-tom/armory/immune"><img src="https://agentmods.dev/badge/skills/mathews-tom/armory/immune.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 | $0.00073 | $0.03342 |
| Opus 5.5 | $0.00029 | $0.01337 |
| Sonnet 5.5 | $0.00015 | $0.00668 |
| Haiku 4.5 | $0.00007 | $0.00334 |
Grade A, and why
immune 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 — 351 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Immune System v3 — Hybrid Cheatsheet + Immune
You operate a hybrid adaptive system with two complementary memories:
- Cheatsheet (positive patterns): domain-specific strategies injected BEFORE generation to improve output quality
- Immune (negative patterns): antibodies that detect known errors and discover new threats AFTER generation
Both memories use Hot/Cold tiering to keep context lean.
Input Parsing
The user invokes with content to scan. Parse these parameters:
- input: The text/code/content to scan (required — either inline or from context)
- domain: One of: fitness, code, writing, research, strategy, webdesign, _global (default: auto-detect)
- domains: Array of domains (overrides single domain). Example:
domains=fitness,code - constraints: Any specific requirements the output should satisfy (optional)
- mode:
full(cheatsheet + scan, default) |scan-only(skip cheatsheet) |cheatsheet-only(return cheatsheet, no scan)
If no inline text is provided, scan the last substantive output in the conversation.
Domain auto-detection: Read config.yaml (co-located with this skill) and match content against domain_keywords. If no strong match, use ["_global"]. If single domain string provided, wrap in array: domains = [domain].
Task-Conditioned Retrieval (v3.1.0+)
Antibodies and cheatsheet strategies may carry an optional triggers field for
per-task filtering. This implements the read phase of the Memento-Skills
reflective loop (arXiv 2603.18743) — entries are ranked by lexical overlap
between the current task and their historical contexts.
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.
- 1mo ago First seen · 351 lines · 73 tokens per session scan A ef04d5b84808
immune is a skill published in the GitHub repository Mathews-Tom/armory (333 stars, last pushed today), licensed MIT. It adds 73 tokens to every session and 3,342 once invoked, about $0.0003 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 2026-08-30.
Other skills, from other repositories
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memory-cleanup
Finds and, on the owner's word, kills process tails that hold a machine's memory for nothing — idle Claude Code desktop sessions, leftovers of closed apps, orphaned workers, zombies. Use on "memory", "swap", "laptop is slow", "zombie processes", "kill the tails", "clean up memory", and after closing heavy apps.
session-cleanup
Backup old AnkaLoop sessions by renaming with execution date, then clean and compact sessions and memory.
doris-debug-resource-isolation
Use for Doris Workload Group / resource tag queue starvation, CPU/memory isolation leaks, and workload policy debugging. Commands: SHOW WORKLOAD GROUPS, EXPLAIN resource.
mm-doctor
A diagnostic workflow for checking the health of the mm system, including its configuration, project links, note vault, passports, session archive, and planning consistency.
search-sessions
Search preserved Claude and Codex conversations locally for debugging history, prior solutions, and decision context.