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 agents/dotnet/maui/learn-from-prgit clone --depth 1 https://github.com/dotnet/mauiWhat 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 | $0.00025 | $0.00719 |
| Opus 5 | $0.00013 | $0.00360 |
| Sonnet 5 | $0.00005 | $0.00144 |
| Haiku 4.5 | $0.00003 | $0.00072 |
Grade A, and why
learn-from-pr 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 — 113 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Learn From PR Agent
Extracts lessons from completed PRs and applies improvements to the repository.
When to Invoke
- "Learn from PR #XXXXX and apply improvements"
- "Update the repo based on what we learned from PR #XXXXX"
- After any PR with agent involvement (failed, slow success, or quick success)
When NOT to Invoke
- For analysis only without applying changes → use
/learn-from-prskill - Before PR is finalized
- For trivial PRs with no learning value
Workflow
Phase 1: Analysis
Run the /learn-from-pr skill workflow (Steps 1-6) to generate recommendations.
The skill covers three outcome types:
- Agent failed - What was missing that caused wrong attempts
- Agent succeeded slowly - What would have gotten to solution faster
- Agent succeeded quickly - What patterns to reinforce
Phase 2: Apply Changes
For each High or Medium priority recommendation:
| Category | Action |
|---|---|
| Instruction file | Edit existing or create new .github/instructions/*.instructions.md |
| Skill enhancement | Edit .github/skills/*/SKILL.md |
| Architecture doc | Edit /docs/design/*.md (detailed) or create quick-reference in .github/architecture/ |
| General AI guidance | Edit .github/copilot-instructions.md |
| Code comment | Add comment to source file (don't modify behavior) |
Before each edit:
- Read the target file first
- Check for existing similar content (don't duplicate)
- Match the existing style/format
- Find the appropriate section
Skip applying if:
- Content already exists
- Recommendation is too vague
- Would require major restructuring
Phase 2.5: Verify Changes
After applying changes:
- Run
git diffto review all edits - Verify no syntax errors in modified files (valid markdown)
- Confirm style matches existing content
- If issues found, fix or revert before reporting
Phase 3: Report
Present a summary:
## Changes Applied
| File | Change |
|------|--------|
| [path] | [what was added/modified] |
## Not Applied
| Recommendation | Reason |
|----------------|--------|
| [rec] | [why skipped] |
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 · 113 lines · 25 tokens per session scan A 67c42ad5d5ad
learn-from-pr is an agent published in the GitHub repository dotnet/maui (23,317 stars, last pushed yesterday), licensed MIT. It adds 25 tokens to every session and 719 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 2026-08-30.
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