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 multi-agent-systems-failure-taxonomy/ATLAS --skill assetsgit clone --depth 1 https://github.com/multi-agent-systems-failure-taxonomy/ATLASWrote 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/multi-agent-systems-failure-taxonomy/atlas/assets)<a href="https://agentmods.dev/skills/multi-agent-systems-failure-taxonomy/atlas/assets"><img src="https://agentmods.dev/badge/skills/multi-agent-systems-failure-taxonomy/atlas/assets/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/multi-agent-systems-failure-taxonomy/atlas/assets"><img src="https://agentmods.dev/badge/skills/multi-agent-systems-failure-taxonomy/atlas/assets.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 | $0.00083 | $0.00863 |
| Opus 5 | $0.00042 | $0.00432 |
| Sonnet 5 | $0.00017 | $0.00173 |
| Haiku 4.5 | $0.00008 | $0.00086 |
Grade A, and why
adamast-failure-modes 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 9d 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 — 70 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AdaMAST for Codex
Use AdaMAST as a lightweight runtime discipline while doing the user's task.
Runtime behavior
- Keep the active taxonomy out of startup context unless the user or an AdaMAST command explicitly supplies it.
- At meaningful boundaries, inspect the recent trajectory before continuing:
- finishing a sub-task;
- recovering from a failed tool command;
- switching strategy;
- preparing to submit a final answer.
- Use the reflection order from AdaMAST prompts:
- Observe concrete events or missing expected steps.
- Correlate only evidence-supported causes.
- Map to taxonomy codes only when evidence supports the match.
- Decide whether to make one focused repair or continue.
- Treat
none applyas valid. Do not invent a failure mode or force an edit. - When the SessionStart context says taxonomy selection is pending, show the supplied selector verbatim and do no task work until the user chooses. After selection, resume any held task without asking the user to repeat it.
- When the selector reports
No taxonomy, do not emit AdaMAST checkpoints or describe AdaMAST as active for that conversation. - Before final submission, complete a final AdaMAST gate and only report ready when no unresolved taxonomy-relevant issue remains.
- When runtime context announces an
AdaMAST native taxonomy learningjob, immediately launch exactly one native Codex subagent with the supplied task prompt. Continue the user's main work while it runs. Do not perform the taxonomy job in the main agent, invokecodex exec, request an API key, or recursively launch another taxonomy agent. The subagent must return the supplied receipt envelope exactly; AdaMAST validates and activates it. - After any failed tool call, privately map the evidence to the active taxonomy
and, before the next tool call, emit one concise user-facing progress
checkpoint. Use
none applywhen the failure was intentional or harmless; do not invent a failure code. - Keep routine hook polls silent. Show each learning lifecycle notice once when runtime context requests it, and keep the longer reflection private.
- End each substantive Codex final answer with the compact checkpoint required
by the active runtime context:
Checkpoint,Relevant codes,Evidence, andNext action. Keep the longer Observe/Correlate/Map/Decide reflection internal unless a hook explicitly requests it.
What ships with it
3 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.
- 9d ago First seen · 70 lines · 83 tokens per session scan A 9880ffff7dde
adamast-failure-modes is a skill published in the GitHub repository multi-agent-systems-failure-taxonomy/ATLAS (19 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 83 tokens to every session and 863 once invoked, about $0.0004 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 2026-08-30.
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