GitHub Agentic Workflows is a GitHub CLI extension that lets developers define AI-assisted repository automation in Markdown and run it through GitHub Actions. It is intended for tasks requiring interpretation or reasoning, such as issue triage, pull-request review, CI investigation, documentation maintenance, and dependency analysis. The catalogue entries provide skills and agents for working with these workflows.
Borrowing it
Nothing to install: this file belongs to github/gh-aw. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/github/gh-aw/main/.github/skills/error-recovery-patterns/SKILL.mdgit clone --depth 1 https://github.com/github/gh-awWrote 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/github/gh-aw/error-recovery-patterns)<a href="https://agentmods.dev/skills/github/gh-aw/error-recovery-patterns"><img src="https://agentmods.dev/badge/skills/github/gh-aw/error-recovery-patterns/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/github/gh-aw/error-recovery-patterns"><img src="https://agentmods.dev/badge/skills/github/gh-aw/error-recovery-patterns.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00020 | $0.00674 |
| Opus 5.5 | $0.00008 | $0.00270 |
| Sonnet 5.5 | $0.00004 | $0.00135 |
| Haiku 4.5 | $0.00002 | $0.00067 |
Grade A, and why
error-recovery-patterns 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 1mo 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.
How it starts
The opening of the file, as written. The whole thing — 97 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Error Recovery Patterns Skill
Use this skill for error handling, recovery strategies, and debugging in gh-aw.
Purpose
Implement robust recovery patterns to:
- Reduce retry loops in agent sessions (target: <10% vs current 23%)
- Implement circuit breakers to prevent infinite retry loops
- Add proactive recovery for installation, dependency, and API failures
- Improve debug logging for recovery attempts
When to Use This Skill
Use this skill when:
- Implementing retry logic for network operations, installations, or API calls
- Debugging retry loop issues in workflows or agent sessions
- Adding error recovery patterns to new or existing code
- Understanding transient vs non-transient error classification
- Implementing circuit breakers or exponential backoff
- Adding debug logging for recovery attempts
Key Concepts Covered
1. Circuit Breaker Pattern
- Maximum retry limits (standard: 3 attempts)
- Exponential backoff strategies
- Fail-fast on non-transient errors
- Implementation in JavaScript, Shell, and Go
2. Installation Failure Recovery
- NPM installation with cache clearing and registry fallbacks
- Python pip installation with mirror alternatives
- Docker image pull with retry and rate limit handling
- Copilot CLI installation with network retry
3. API Timeout and Rate Limit Handling
- GitHub API rate limit detection and backoff
- Transient error detection patterns
- Custom retry configuration for different APIs
- Rate limit-specific retry strategies
4. Debug Logging for Recovery
- Logger package usage for retry attempts
- Category naming conventions (pkg:filename)
- DEBUG environment variable patterns
- Zero-overhead logging when disabled
5. Error Categorization
- Transient vs non-transient errors
- Network errors, timeout patterns
- HTTP error codes (502, 503, 504)
- GitHub-specific errors (rate limits, abuse detection)
Anti-Patterns to Avoid
This skill explicitly covers anti-patterns to avoid:
- ❌ Infinite retry loops without maximum limits
- ❌ Retrying validation errors that won't self-correct
- ❌ No backoff delay between attempts
- ❌ Silent retries without logging
- ❌ Retrying non-transient errors
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.
- 1mo ago First seen · 97 lines · 20 tokens per session scan A dee85b04fab2
error-recovery-patterns is a skill published in the GitHub repository github/gh-aw (5,406 stars, last pushed today), licensed MIT. It adds 20 tokens to every session and 674 once invoked, about $0.0001 per session on Opus 5.5. 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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