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 EthanYoQ/Skill-hub --skill github-desktop-releasegit clone --depth 1 https://github.com/EthanYoQ/Skill-hubWrote 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/ethanyoq/skill-hub/github-desktop-release)<a href="https://agentmods.dev/skills/ethanyoq/skill-hub/github-desktop-release"><img src="https://agentmods.dev/badge/skills/ethanyoq/skill-hub/github-desktop-release/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/ethanyoq/skill-hub/github-desktop-release"><img src="https://agentmods.dev/badge/skills/ethanyoq/skill-hub/github-desktop-release.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 | $0.00048 | $0.03008 |
| Opus 5.5 | $0.00019 | $0.01203 |
| Sonnet 5.5 | $0.00010 | $0.00602 |
| Haiku 4.5 | $0.00005 | $0.00301 |
Grade A, and why
github-desktop-release 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 — 145 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GitHub Desktop Release
Treat a desktop release as a state machine, not a build command. Keep application implementation, platform qualification, artifact identity, and public GitHub Release state as separate evidence boundaries.
electron-development owns Electron macOS build and qualification. This Skill may validate a macOS qualification artifact before promotion, but does not take over that platform's build work.
Pick the smallest justified cloud path
Classify the request before spending runner time. Do not turn a past infrastructure failure into a permanent requirement for every release.
- Compatibility preflight: a read-only or non-promotable check that proves each requested platform can receive the frozen source: runner label availability, locked-install viability, expected architecture/native binding load, and resolved asset plan. It produces no installer and cannot justify publication.
- Standard Windows qualification: the default for ordinary application-source releases when the Windows installer, upgrade path, Electron/Node ABI, signing policy, and installer configuration are unchanged. It builds one installer and proves core installability.
- Deep Windows acceptance: adds upgrade-data, native-ABI, and project-specific migration/installer checks. Use it only when its trigger is present or the user explicitly requests it.
- Promotion/readback: consumes already-qualified bytes. It never builds, installs, or substitutes an artifact.
For a multi-platform release, choose between a staged canary and a parallel formal matrix from the release-risk delta. Ask electron-development to own macOS preflight and qualification work. A staged canary protects expensive minutes when release infrastructure changed; a parallel matrix protects elapsed time when that infrastructure is already proven.
Establish scope
- Read the repository's
AGENTS.md, domain docs, packaging configuration, workflows, release scripts, and tests. Use CodeGraph first when the project is indexed. - Record the repository, default branch, full source SHA, tag, version, requested platforms/architectures, signing policy, expected assets, and authorization boundary.
- Keep project facts in a repository release profile, not in this Skill. Read references/release-profile.md, then validate the selected profile:
What ships with it
28 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- agents/openai.yaml 339 B
- assets/electron-nsis-qualification.yml 40 KB
- assets/github-desktop-release-promotion.yml 5.6 KB
- assets/macos-qualification.yml 18 KB
- assets/release-profile.schema.json 4.8 KB
- evals/evals.json 9.3 KB
- references/agent-orchestration.md 1.4 KB
- references/cloud-execution-strategy.md 6.3 KB
- references/electron-nsis.md 7.5 KB
- references/evidence-contract.md 7.8 KB
- references/failure-classification.md 2.3 KB
- references/failure-playbook.md 4.5 KB
- references/github-release-state-machine.md 5.8 KB
- references/macos-qualification.md 2.2 KB
- references/promotion-preflight.md 2.7 KB
- references/receipt-replay.md 2.7 KB
- references/release-contract.md 4.1 KB
- references/release-profile.md 4.9 KB
- references/stack-adapters.md 4.4 KB
- scripts/classify-actions-failure.mjs 2.6 KB runs code
- scripts/github-desktop-promotion.mjs 39 KB runs code
- scripts/preflight-windows-release.ps1 16 KB runs code
- scripts/replay-release-evidence.mjs 5.7 KB runs code
- scripts/test-github-desktop-promotion.mjs 19 KB runs code
- scripts/test-preflight.ps1 13 KB runs code
- scripts/test-release-tools.mjs 9.3 KB runs code
- scripts/test-workflow-assets.ps1 30 KB runs code
- scripts/validate-release-profile.mjs 11 KB runs code
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 · 145 lines · 48 tokens per session scan A cf312167b6a4
github-desktop-release is a skill published in the GitHub repository EthanYoQ/Skill-hub (11 stars, last pushed yesterday), licensed MIT. It adds 48 tokens to every session and 3,008 once invoked, about $0.0002 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 2026-08-31.
Other skills, from other repositories
github-release-management
Comprehensive GitHub release orchestration with AI swarm coordination for automated versioning, testing, deployment, and rollback management.
agent-github-modes
Agent skill for github-modes - invoke with $agent-github-modes.
factory-ship
Experimental workflow for publishing and completing configured software delivery work. Use when the user or an enabled factory policy asks to ship.
releasepr
Adopt, configure, run, or troubleshoot compozy/releasepr in a consuming repository, including Actions, beta/stable/legacy plans, notes, dry-runs, and publication. Excludes pr-release internals and general versioning advice.
pr-watch
Local PR watcher. Monitors CI status, automatically fixes failing checks by reading failure logs and applying targeted fixes, then optionally merges when all checks pass. Local CLI analog to Claude Code's cloud auto-fix feature.
gitlab-devops
GitLab DevOps operations — issues, merge requests, CI/CD pipelines, repository browsing, labels, milestones, releases, and wiki management. Use when querying GitLab project status, monitoring pipeline executions, browsing repository files, creating issues for network findings, opening merge requests for config…