What does this skill do, and when should you use it?
This skill provides a structured methodology for auditing the backlog of Engram-based projects (or any GitHub repository). It guides the user through fetching all open issues and PRs, classifying each item into a predefined disposition (merge, request changes, close, needs design, approve issue, reject issue), and understanding the maintainer's actual philosophy by analyzing their comments. It then generates a triage report with suggested comments, enabling maintainers to quickly act on the backlog.
Fetches issues and PRs as JSON using GitHub CLI, including labels, comments, and review states. Classifies each item based on a set of binary questions and assigns one of six dispositions. Infers maintainer philosophy from maintainer comments to calibrate triage decisions. Prioritizes within disposition buckets and produces a Markdown triage report with summary statistics, tables, and copyable suggested comments.
- A project maintainer wants to quickly review a large number of open issues and PRs to determine what needs attention and what can be closed.
- An open-source contributor wants to understand maintainer preferences and avoid common pitfalls before submitting a PR.
- A team lead wants to establish a consistent triage process for their issue tracker, ensuring all contributors follow the same standards.
- A developer looking to automate backlog cleanup by using this as a manual process or integrating it into a CI script.
How do you install this skill?
- Confirm write permissions on target repository before execution; use least-privilege tokens.
- Skill depends on GitHub CLI and GitHub access, which may be restricted in mainland China; consider using a proxy or CI environment.
- Skill lacks error handling and failure feedback; abnormal input may cause debugging difficulty.
- Static review did not verify actual execution; test yourself before use.
- Shell / CLI
- Network access
GitHub CLI
This is a skill package, not a standalone install. To use it, place the SKILL.md file into a skills directory in an Agent Skills-compatible client (e.g., Claude Code). No scripts are included with this skill.
tmp="$(mktemp -d)"
git clone --depth 1 https://github.com/Gentleman-Programming/engram.git "$tmp"
mkdir -p ~/.claude/skills
cp -R "$tmp/skills/backlog-triage" ~/.claude/skills/
rm -rf "$tmp"Generated from the source repository and skill path; it copies only this skill's folder. If the author's install steps above differ, follow those first. To scope it to one project, replace ~/.claude/skills with that project's .claude/skills.
How do you use this skill?
Once installed, send your agent any of these to trigger it:
- Triage the backlog for repo owner/repo
Once installed in the skills directory, trigger the skill, e.g., by asking 'Triage the backlog for repo owner/repo'. The skill will guide you through GitHub CLI commands to fetch the backlog, then follow its process to classify and report. It relies on GitHub CLI (gh) being installed and authenticated.
What are this skill's strengths and limitations?
- Provides a clear, actionable disposition classification covering all common triage outcomes.
- Grounds decisions in actual maintainer comments, not just written philosophy, leading to more accurate triage.
- Includes copyable suggested comments, saving time and ensuring consistency.
- Comes with a reusable prompt template adaptable to any repository.
- Heavily relies on GitHub CLI, meaning it's GitHub-only and requires pre-installation and authentication.
- Classification is inherently subjective and may produce inconsistent results depending on the prompt.
- No automated tests or validation are included; relies entirely on the user executing steps correctly.
- Described as targeting Engram's workflow—labels like status:approved, type:* may not match other repositories and would need adjustment.
How does this skill compare with similar options?
Side by side with related skills; every score comes from the same FSRS standard.
| Skill | FS score | Stars | Last updated | License |
|---|---|---|---|---|
| Engram Backlog Triage this page | 45 · Not recommended | ★ 7.1k | 4d ago | MIT |
| PR Babysitter: Watch Pull Requests Until Merge | 47 · Use with care | ★ 98k | 3d ago | Apache-2.0 |
| RenderCV Issue Triage | 54 · Use with care | ★ 18k | 6mo ago | MIT |
| Poll GitHub Work | 52 · Use with care | ★ 29k | 1d ago | MIT |
| Maintainer Response (agent-service-toolkit) | 69 · Recommended | ★ 4.5k | 7d ago | MIT |
How did FollowSkills review this skill?
The skill itself is a static document with no executable code or system modification, low risk. But it requires executing gh commands on the repository (reading and modifying issues/PRs) without explicit authorization confirmation or least-privilege explanation. Security policy and license exist, but attribution is unverified. Therefore, deductions for unclear permission control and attribution verification.
Provides clear commands and classification criteria; happy path reproducible if gh CLI available. But static review did not execute tests, and it depends on external tool (gh) and network, no error handling inside skill. CI exists, but skill-specific key paths not directly tied to tests. Thus, static calibration caps reliability at 10, giving 8.
Clearly defines target audience (maintainers) and triggers (auditing issues/PRs, classifying, inferring ideology). Considering FollowSkills serves Chinese users, skill does not mention overseas service dependency; core works with local gh CLI, GitHub access may be limited in mainland China, but skill doesn't rely on remote services, so adaptability high. Deduction for undefined boundary conditions.
Clear frontmatter, description, when-to-use, and step-by-step operations. Version and license metadata exist, but no changelog or maintenance responsibility. Publisher unverified, but SECURITY.md and contributing guide exist, possibly showing maintenance. Deduction for insufficient versioning governance and limitation disclosure.
Provides complete report structure, output directly usable, but depends on external tool execution; static review cannot verify representative outputs. Thus, static calibration caps effectiveness at 7, giving 5.
Contains real CI workflows and test commands, but not testing the skill itself. Internally auditable logic, but limited third-party execution evidence. Thus, static calibration caps verifiability at 5, giving 5.
Open a dimension to read why it scored that way
Evidence confidence：Low — Mostly static review, author material or a limited demo; useful for discovery, not high-risk decisions.
See the full review method →