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/debugging-workflows/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/debugging-workflows)<a href="https://agentmods.dev/skills/github/gh-aw/debugging-workflows"><img src="https://agentmods.dev/badge/skills/github/gh-aw/debugging-workflows/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/debugging-workflows"><img src="https://agentmods.dev/badge/skills/github/gh-aw/debugging-workflows.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Agent Snooping · line 505 Skill accesses MCP server configuration files (mcp.json). MCP configs contain server URLs, authentication tokens, and tool definitions — reading them allows the skill to discover and potentially abuse other tool integrations.Fix: Remove all code or instructions that read MCP configuration files (mcp.json). MCP server details should be managed by the agent runtime, not read by individual skills.
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 | $0.00027 | $0.04827 |
| Opus 5.5 | $0.00011 | $0.01931 |
| Sonnet 5.5 | $0.00005 | $0.00965 |
| Haiku 4.5 | $0.00003 | $0.00483 |
Grade B, and why
debugging-workflows scanned grade B with 1 finding 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 5d 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.
Reads MCP configurationmediumAgent snooping
mcp.json carries server URLs and auth tokens; reading it lets a mod discover and abuse other integrations.
| `gh aw mcp inspect <workflow>` | Inspect MCP configuration | How it starts
The opening of the file, as written. The whole thing — 636 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Workflow Diagnosis and Debugging Evidence
Use this reference to diagnose workflows: download/analyze existing logs, audit runs, and trace failures. These reads are not an active debug loop.
Follow the shared local-first strategy for all reproduction, fixes, uploads, and live tests. This page is an evidence and CLI reference, not a separate execution policy. Respect explicit no-dispatch contexts. Apply its live-outcome table, credential triage and untrusted-evidence rules. Without accessible existing logs, use source/fixtures; never dispatch for evidence.
Require one read-only security-review agent per revision, not multiple judges.
Show the user an annotated overview of changed authored .md sources explaining
their security impacts; ignore .lock.yml diffs in that overview while retaining
generated locks as execution-review evidence. Follow the shared
single-reviewer and
annotated overview rules.
Workflow registration/activation and secret presence, validity, or expiry are runtime readiness checks, not pre-dispatch prerequisites. Do not require workflow or secret inventories or organization-admin metadata access. An otherwise authorized dispatch and the workflow's startup/authentication checks determine readiness; report their failures without automatic enabling, provisioning, or dispatch retries. Missing readiness metadata alone is not a safety finding. Declared credential flows, authorized destinations, permissions, source/lock review, and human live-validation gates remain in scope.
The user may grant explicit session authorization for bounded in-scope debug iterations without repeated approval. Re-review every changed source/lock revision; any compiler security warning invalidates the grant and requires fresh authorization after resolution.
For workflow updates, recommend publishing the reviewed Markdown and generated lock on a feature branch and opening a draft PR, then testing that explicit reviewed ref rather than requiring a merge or simply refusing because the changes are unpublished. Reuse an existing suitable branch or PR. The agent owns authorized preparation, compilation, publication and testing; the user owns the required live-effect authorization. Follow the shared branch-testing path. A branch or PR does not isolate secrets, queue state or outputs, and cannot bypass a security gate, dispatch denial or explicit no-dispatch context. The first accepted producer submit automatically installs Policy and Work; no administrator seed or repository-administration inventory is required. Follow the queue protocol.
Always provide separate short user-visible result sentences for each security
review and dry-run attempt, including failed, blocked, or unavailable outcomes.
Name the artifact/scope, checks actually performed, and material findings or
coverage gaps; do not leave the result only in logs or artifacts or imply live
execution. Follow the shared review result
and dry-run result rules.
When debugging is refused, follow the
blocked-result requirements:
explain each concrete cause, link the reviewed source and lines, distinguish
confirmed defects from incomplete evidence or authorization, and name the
necessary resolution and its owner. Do not report disagreement alone as a code
vulnerability. Include the branch/PR testing path when it resolves unpublished
changes; explain any independent blocker that it cannot resolve.
In dry-run mode, reject enabled dangerously-* entries in workflow Markdown
configuration, including imported configuration,
under the strict security gate.
This filter does not reject implementation-internal flags used by trusted
built-in engines. Review their runtime isolation separately. Link the authored
field responsible for a refusal; do not claim compiler rejection when none occurred.
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.
- 5d ago Changed · +7 lines f386ad3815c2
- 6d ago Changed · +33 lines e180ffa009e4
- 8d ago Changed · +33 lines 07bd48cfd5c0
- 9d ago Changed · +14 lines 8ca2458e59e4
- 10d ago Changed · +19 lines · +7 tokens per session 1bc750513b3b
- 1mo ago First seen · 530 lines · 20 tokens per session scan B 495063fa9c09
debugging-workflows is a skill published in the GitHub repository github/gh-aw (5,406 stars, last pushed today), licensed MIT. It adds 27 tokens to every session and 4,827 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it B with 1 finding (reads mcp configuration). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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.