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 ihabkhaled/AI-Psychiatry --skill failure-learninggit clone --depth 1 https://github.com/ihabkhaled/AI-PsychiatryWrote 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/ihabkhaled/ai-psychiatry/failure-learning)<a href="https://agentmods.dev/skills/ihabkhaled/ai-psychiatry/failure-learning"><img src="https://agentmods.dev/badge/skills/ihabkhaled/ai-psychiatry/failure-learning/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/ihabkhaled/ai-psychiatry/failure-learning"><img src="https://agentmods.dev/badge/skills/ihabkhaled/ai-psychiatry/failure-learning.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 | $0.00026 | $0.00193 |
| Opus 5 | $0.00013 | $0.00097 |
| Sonnet 5 | $0.00005 | $0.00039 |
| Haiku 4.5 | $0.00003 | $0.00019 |
Grade A, and why
failure-learning 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.
What it actually says
Failure Learning
Core principle
Save reusable conclusions and recovery, never full debugging transcripts or hidden reasoning.
Procedure
- Name the observable symptom.
- State the evidenced root cause.
- Record the evidence source and successful recovery.
- Describe how to detect the pattern earlier.
- Check stability, reuse value, and future token savings.
- Store it in failure-patterns or lessons, or reject promotion.
Required output
Symptom, root cause, evidence, recovery, early signal, and memory destination.
Limits
One concise entry per reusable pattern.
Common mistakes
Do not save temporary errors, speculative diagnoses, giant output, or raw chain-of-thought.
Stop condition
Stop when the reusable lesson is stored concisely or judged too temporary.
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 · 37 lines · 26 tokens per session scan A b70b30c3eb85
failure-learning is a skill published in the GitHub repository ihabkhaled/AI-Psychiatry (3 stars, last pushed 25d ago), licensed MIT. It adds 26 tokens to every session and 193 once invoked, about $0.0001 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.
Other skills, from other repositories
semantix
Install and use the semantix memory kernel as a middleware in your agent: extract user preferences / workflows / experience from past sessions, retrieve and inject them on demand. One binary + your agent's own tools.
continuous-learning
Use when a mistake, correction, or surprise taught the workspace something that must stick — a retro or postmortem, the same agent error corrected twice, a resolved bug's root cause, scattered notes-to-self — and route that lesson to the durable surface that fires next time. NOT a forward choice with alternatives…
context-budget
Use when a long-horizon task is filling the context window and you must decide what to keep, offload, drop, or hand off to a fresh window — when to compact, what the summary must preserve, and whether to isolate a read-heavy subtask in a subagent. NOT dollar spend or caps (that is cost-tracking), NOT finding context…
self-improvement
Captures lessons and promotes recurring patterns.
evolve
Use this skill when extracting session patterns into reusable learnings. Three modes: analyze (extract from session history), review (edit/manage existing learnings), list (display active learnings). Manages .orchestrator/metrics/learnings.jsonl.
memory-cleanup
Use this skill when performing manual memory consolidation (Dream-equivalent). Reviews, consolidates, and prunes memory files under /.claude/projects//memory/. Run after major refactors, every 5+ sessions, or when memory quality degrades (broken links, stale references, contradictions, MEMORY.md > 200 lines). Invoke…