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 commands/dmzoneill/redhat-ai-workflow/learn-fixgit clone --depth 1 https://github.com/dmzoneill/redhat-ai-workflowWrote 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/commands/dmzoneill/redhat-ai-workflow/learn-fix)<a href="https://agentmods.dev/commands/dmzoneill/redhat-ai-workflow/learn-fix"><img src="https://agentmods.dev/badge/commands/dmzoneill/redhat-ai-workflow/learn-fix.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 | $0.00000 | $0.00426 |
| Opus 5 | $0.00000 | $0.00213 |
| Sonnet 5 | $0.00000 | $0.00085 |
| Haiku 4.5 | $0.00000 | $0.00043 |
Grade A, and why
learn-fix 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 3d 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
/learn-fix
Save a tool fix to memory so it's not repeated.
Overview
Save a tool fix to memory so it's not repeated.
Arguments
No arguments required.
Usage
Examples
/learn-fix
Tool: bonfire_deploy
Error: manifest unknown
Cause: Short SHA doesn't exist in Quay
Fix: Use full 40-char SHA
Tool fails → check_known_issues() → debug_tool() → fix → learn_tool_fix() → ✓
↑ |
└────────── remembered for next time ←───────┘
Process Flow
flowchart TD
START([Start]) --> PROCESS[Process Command]
PROCESS --> END([Complete])
style START fill:#6366f1,stroke:#4f46e5,color:#fff
style END fill:#10b981,stroke:#059669,color:#fff
```text
## Details
## Usage
After successfully fixing a tool, run this command to save the learning.
## What You'll Need
1. **Tool name** - The tool that was fixed (e.g., `bonfire_deploy`)
2. **Error pattern** - The error message that triggered the fix
3. **Root cause** - Why it failed
4. **Fix description** - What was changed
## Example
```text
/learn-fix
Tool: bonfire_deploy
Error: manifest unknown
Cause: Short SHA doesn't exist in Quay
Fix: Use full 40-char SHA
```text
## The Learning Loop
```text
Tool fails → check_known_issues() → debug_tool() → fix → learn_tool_fix() → ✓
↑ |
└────────── remembered for next time ←───────┘
Related Commands
/debug-tool- Analyze and fix a failing tool/memory- View all memory including learned fixes/memory-edit- Manually edit memory entries
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.
- 3d ago First seen · 80 lines · 0 tokens per session scan A 7fd66ee2f4d1
learn-fix is a command published in the GitHub repository dmzoneill/redhat-ai-workflow (5 stars, last pushed today), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 426 tokens. 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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