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.
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 github/gh-aw --skill error-recoverygit 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)<a href="https://agentmods.dev/skills/github/gh-aw/error-recovery"><img src="https://agentmods.dev/badge/skills/github/gh-aw/error-recovery/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"><img src="https://agentmods.dev/badge/skills/github/gh-aw/error-recovery.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 | $0.00024 | $0.00785 |
| Opus 5.5 | $0.00010 | $0.00314 |
| Sonnet 5.5 | $0.00005 | $0.00157 |
| Haiku 4.5 | $0.00002 | $0.00078 |
Grade A, and why
error-recovery 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.
Copies of this mod
8 near-identical copies found in the catalogue:
- error-recovery — 100% identical, 0 lines differ
- error-recovery — 100% identical, 0 lines differ
- error-recovery — 100% identical, 0 lines differ
- error-recovery — 100% identical, 0 lines differ
- error-recovery — 100% identical, 0 lines differ
- error-recovery — 100% identical, 0 lines differ
- error-recovery — 100% identical, 0 lines differ
- error-recovery — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 100 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Error Recovery Patterns
Standard recovery patterns for all squad agents. When something fails, adapt — don't just report the failure.
1. Retry with Backoff
When: Transient failures — API timeouts, rate limits, network errors, temporary service unavailability.
Pattern:
- Wait briefly, then retry (start at 2s, double each attempt)
- Maximum 3 retries before escalating
- Log each attempt with the error received
Example: API call returns 429 Too Many Requests → wait 2s → retry → wait 4s → retry → wait 8s → retry → escalate if still failing.
2. Fallback Alternatives
When: Primary tool or approach fails and an alternative exists.
Pattern:
- Attempt primary approach
- On failure, identify alternative tool/method
- Try the alternative with the same intent
- Document which alternative was used and why
Example: Primary CLI tool fails → fall back to direct API call for the same operation.
3. Diagnose-and-Fix
When: Build failures, test failures, linting errors — structured errors with actionable output.
Pattern:
- Read the full error output carefully
- Identify the root cause from error messages
- Attempt a targeted fix
- Re-run to verify the fix
- Maximum 3 fix-retry cycles before escalating
Example: Build fails with a type error → check for missing import → add it → rebuild.
4. Escalate with Context
When: Recovery attempts have been exhausted, or the failure requires human judgment.
Pattern:
- Summarize what was attempted and what failed
- Include the exact error messages
- State what you believe the root cause is
- Suggest next steps or who might be able to help
- Hand off to the coordinator or the appropriate specialist
Example: After 3 failed build attempts → "Build fails on line 42 with null reference. Tried X, Y, Z. Likely a design issue in the Foo module. Recommend the code owner review."
5. Graceful Degradation
When: A non-critical step fails but the overall task can still deliver 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.
- 1mo ago First seen · 100 lines · 24 tokens per session scan A a6bc62ea076c
error-recovery is a skill published in the GitHub repository github/gh-aw (5,421 stars, last pushed today), licensed MIT. It adds 24 tokens to every session and 785 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 2026-09-03.
Other skills, from other repositories
Flaky Test Doctor
Diagnose flaky test failures from Playwright reports, traces, and rerun history. Classify each failure as product, test, environment, data, or unknown with cited evidence and a proposed fix. Never auto-modifies code without opt-in.
GitHub CI Fix Debugging
Debug and fix failing GitHub PR checks by inspecting GitHub Actions logs, summarizing failure context, drafting fix plans, and implementing fixes after approval.
fixing
Diagnose why a PR's CI failed — compare the CI definition against the sandbox, classify the failure, and make the minimal repair. Use when a PR is red and you must work out why before changing anything.
argot-refresh
Refresh Argot's committed fit snapshot safely — first diagnose why maintenance is recommended, re-audit corpus scope and structural path changes, review stale mutes and policy entries with the user, then fit locally, verify, and prepare the reviewed .argot/ update. Use when argot status, argot check, MCP, or CI…
Systematic Debugging
Reproduce a bug, isolate the cause, and fix it only after evidence identifies the failure path.
Error Analysis
Cluster failures and explain model or router error patterns.