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 fatihguner/foreman --skill learning-from-failuregit clone --depth 1 https://github.com/fatihguner/foremanWrote 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/fatihguner/foreman/learning-from-failure)<a href="https://agentmods.dev/skills/fatihguner/foreman/learning-from-failure"><img src="https://agentmods.dev/badge/skills/fatihguner/foreman/learning-from-failure.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 134 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00082 | $0.03271 |
| Opus 5 | $0.00041 | $0.01636 |
| Sonnet 5 | $0.00016 | $0.00654 |
| Haiku 4.5 | $0.00008 | $0.00327 |
Grade A, and why
learning-from-failure 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 yesterday.
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 — 203 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Read runtime and advisory rules before applying this skill. Other Foreman layers and the catalog are in ../../content/, relative to this SKILL.md.
Learning from Failure
Consider a thought experiment: what if leaders displayed their failures as prominently as their wins? A trophy case in the corner office, but instead of deal tombstones and framed press clippings, it held the pitch deck that lost the account, the product that customers rejected, the hire who left after three months. The idea feels absurd -- and that absurdity reveals how deeply organisations have internalised the fiction that competent leaders do not fail. They do. The research is unambiguous: around 70% of people react poorly to failure, whether by blaming others, denying the problem, or absorbing disproportionate personal guilt. The difference between leaders who grow and those who stagnate is not the frequency of their failures but the quality of their response.
The Framework
Learning from failure is not a single practice but a constellation of five interlocking disciplines.
Discipline 1: Lose Well
Failure is inevitable; losing badly is optional. "Losing well" is a learnable skill with three distinct strategies:
Pivot the effort. When a project fails, resist the urge to bury it. Step back and ask: what worked in what was tried? Skills, innovations, and ideas developed for a failed initiative rarely lack application -- they simply lack the right context. The creative thinking that produced a failed product may be a perfect fit for a different problem.
Reconnect to purpose. If the work matters for reasons beyond winning, the loss stings less and teaches more. Work is finite -- projects end, companies fold, people leave. But the underlying purpose is not bounded by any single outcome. Leaders who anchor in purpose rather than scoreboard can absorb losses without losing direction.
Share the pain. Losing involves heartbreak, frustration, and sorrow that leaders often feel obligated to suppress. Creating space for the team to acknowledge negative feelings -- to talk about what happened and how it felt -- builds the kind of psychological safety that, paradoxically, enables future risk-taking. Hard times bring people together; ignoring that dynamic wastes its value.
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.
- yesterday First seen · 203 lines · 82 tokens per session scan A 8d603c2529a8
learning-from-failure is a skill published in the GitHub repository fatihguner/foreman (47 stars, last pushed 2d ago), licensed MIT. It adds 82 tokens to every session and 3,271 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 2026-09-06.
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