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 agentmods add skills/nolane-x/forge-os/learning-from-agent-failuresnpx skills add Nolane-x/forge-os --skill learning-from-agent-failuresgit clone --depth 1 https://github.com/Nolane-x/forge-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/nolane-x/forge-os/learning-from-agent-failures)<a href="https://agentmods.dev/skills/nolane-x/forge-os/learning-from-agent-failures"><img src="https://agentmods.dev/badge/skills/nolane-x/forge-os/learning-from-agent-failures.svg" alt="Measured on agentmods" 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 | $0.00034 | $0.00186 |
| Opus 5 | $0.00017 | $0.00093 |
| Sonnet 5 | $0.00007 | $0.00037 |
| Haiku 4.5 | $0.00003 | $0.00019 |
Grade A, and why
learning-from-agent-failures 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 6d 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.
What it actually says
Learning from Agent Failures
Core principle
Cluster failures by invariant, rationalization, missing context, and tool boundary. Propose a skill or code change without auto-promoting it. The runtime owns deterministic scope, coverage, policy, and evidence checks; the agent owns only the judgment that cannot be reduced safely to code.
Do not activate when
- the failure is caused by a known code bug
- one isolated failure has no reproducible pattern
What ships with it
9 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.
- 6d ago First seen · 23 lines · 34 tokens per session scan A 1bfd53cf255b
learning-from-agent-failures is a skill published in the GitHub repository Nolane-x/forge-os (10 stars, last pushed 1mo ago), licensed MIT. It adds 34 tokens to every session and 186 once invoked, about $0.0002 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 2026-08-31.
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