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Housekeeping

Free

Streamline your engineering work queues across platforms.

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Free · Opens the source repo

What Housekeeping does

Housekeeping is a skill designed to help engineers manage their work queues efficiently across multiple platforms including Linear, Pylon, and GitHub. It reviews live work items and provides actionable recommendations based on the current status of issues, pull requests, and customer inquiries. This skill operates in a read-only mode until explicit approval is given for any writes, ensuring that engineers maintain control over their tasks while benefiting from automated triage recommendations.

The skill categorizes work items into various action labels such as act-now, quick-win, todo, backlog, waiting, stale-candidate, and no-action, each accompanied by a confidence level. By analyzing the current state of assigned issues, urgent tasks, and pull requests, it helps prioritize work based on urgency and potential impact. For instance, it flags issues that require immediate attention due to data loss risks or customer pain points, while also identifying items that can be deferred or closed.

Housekeeping is particularly useful for engineers who juggle multiple tasks across different systems. By consolidating information and providing clear recommendations, it reduces the cognitive load on engineers, allowing them to focus on high-priority work. The skill is ideal for teams that need to manage customer issues effectively and ensure timely responses to pull requests, enhancing overall productivity and collaboration.

However, it is important to note that this skill is not designed for deep debugging, incident response, or broad roadmap planning. It focuses on triage and prioritization rather than execution of complex tasks, making it a complementary tool rather than a standalone solution for all engineering challenges.

When to use it

Use this skill when you need to triage and prioritize tasks across Linear, Pylon, and GitHub.

When not to use it

Avoid using this skill for in-depth debugging or planning tasks that require detailed execution beyond triage.

What you can build with it

Triage Urgent Issues in Linear

Quickly identify and prioritize urgent issues assigned to you in Linear, ensuring critical tasks are addressed first.

Manage Customer Follow-ups in Pylon

Use Housekeeping to track customer issues in Pylon that require timely responses from your engineering team.

Review Pull Requests in GitHub

Efficiently assess and prioritize pull requests where you are a reviewer, ensuring smooth code integration.

How to install Housekeeping

View source

1. Install with the skills CLI

npx skills add langfuse/langfuse/housekeeping --agent claude-code

2. Or install it manually

Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.

Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs

Inside SKILL.md

Written by langfuse

Housekeeping

Scope

Review live work queues and recommend what the engineer should do next. Stay read-only until the human explicitly approves specific writes.

Use this skill for:

  • Linear issue triage, assigned work, waiting issues, urgent-priority issues, and stale cleanup candidates.
  • Pylon customer issues that need an engineer response, engineering follow-up, linked-ticket check, or stale close recommendation.
  • GitHub pull requests where the engineer is a direct or team reviewer.

Do not use this skill for deep root-cause debugging, implementation, incident response, or broad roadmap planning unless the user explicitly asks to continue from a recommendation into that work.

Required Behavior

Every reviewed item must include an action recommendation. If evidence is insufficient, recommend the next inspection step rather than leaving the action blank.

Use these action labels:

  • act-now: needs attention today.
  • quick-win: likely under 15 minutes.
  • todo: should be picked up in the next 1-2 weeks.
  • backlog: real but can wait months.
  • waiting: next move is outside the engineer or team.
  • stale-candidate: likely safe to close or cancel, but needs human confirmation.
  • no-action: no current action beyond monitoring; explain why.

Include confidence for each recommendation: high, medium, low, or unknown.

Workflow

  1. Identify the current engineer in each system from the available connector, CLI, or authenticated API context. If identity cannot be determined for a system, say so and continue with the other systems.
  2. Gather live data before recommending action. Do not rely on memory for current queues.
  3. Inspect comments, latest activity, linked issues, checks, and review threads when status or ownership is ambiguous.
  4. Cluster duplicates or related items before prioritizing.
  5. Return recommendations ordered by urgency, then quick wins, then cleanup.
  6. Before any write, present a decision table and wait for explicit row IDs and actions.

Linear Review

Fetch:

  • Issues assigned to the engineer in Triage, In Progress, and Waiting.
  • Active urgent-priority issues relevant to the engineer or team.
  • Recent comments for waiting, stale, urgent, or ambiguous issues.

Recommend:

  • act-now for data loss, production regressions, security/privacy risk, billing/cost correctness, repeated customer pain, blocked teammate/release, or reporter waiting on the engineer.
  • todo for bounded fixes with current customer impact.
  • backlog for real but lower-impact work, product-design work, or upstream-dependent work.
  • waiting only when the latest evidence shows the next step is on the customer, upstream, another team, or an external dependency.
  • stale-candidate when the issue has no recent meaningful activity, no clear current customer blocker, and appears superseded, abandoned, solved, or duplicated.

If a triage issue has an obvious low-risk fix, include the quick-fix path and whether it should be handled before broader prioritization.

Pylon Review

Use a Pylon connector, MCP server, or authenticated API only if already available. Do not ask the user to paste secrets into chat. If Pylon is unavailable, report that limitation and continue.

Review open issues assigned to the engineer or their team, plus urgent or high-priority issues where engineering appears to own the next step. Use Pylon's issue states:

  • new: no team response yet.
  • waiting_on_you: next action is on the team.
  • waiting_on_customer: next action is on the customer.
  • on_hold: pending external work, commonly an engineering fix.
  • closed: resolved; include only if it reopened or is linked from an active item.

Inspect:

  • latest customer and internal activity;
  • priority, requester/account, assignee/team, source, and age;
  • linked Linear, GitHub, Jira, or other external issues;
  • whether the linked engineering issue is still open, completed, stale, or missing.

Recommend:

  • act-now when the customer is waiting on the team, priority is urgent/high, an SLA looks at risk, or a linked engineering issue is complete and the customer needs an update.
  • quick-win for a short reply, clarification request, link repair, or status correction.
  • waiting when the latest customer-facing state correctly waits on the customer or an external ticket.
  • todo when engineering owns a real follow-up but it is not same-day urgent.
  • stale-candidate for old waiting_on_customer or on_hold issues with no meaningful recent activity, but never close them without human approval.

For Pylon issues linked to Linear or GitHub, make the recommended action consistent across systems. Example: if a Linear issue is done and Pylon is still on_hold, recommend a customer update and status change instead of more engineering work.

GitHub Review

Find open pull requests where the engineer is requested as a direct reviewer and where one of the engineer's teams is requested. Include PRs across relevant organization repositories, not only the current repository.

Inspect:

  • PR title, repo, age, author, requested reviewer source, mergeability, review decision, and checks;
  • changed files and diff size;
  • unresolved comments, bot findings, requested changes, and author responses;
  • whether failures are code failures or external authorization/noise.

Recommend:

  • quick-win with approve only for small focused diffs, acceptable checks, no unresolved material concerns, and tests or plainly trivial behavior.
  • quick-win with comment for small PRs needing one narrow author action such as rebase, CLA, missing test, or cleanup.
  • act-now for blocked releases, security fixes, production regressions, or PRs where the engineer is the bottleneck.
  • todo for meaningful PRs that need real review soon.
  • stale-candidate for old, conflicting, duplicate, or superseded PRs.
  • no-action when a PR is blocked on the author, failing CLA, merge conflicts, unresolved requested changes, or unrelated team ownership.

Do not approve, request changes, comment, close, merge, or edit a PR without explicit human approval for that PR.

Human Gates

All writes require explicit human confirmation by row ID. This includes:

  • Linear comments, status changes, priority changes, assignee changes, labels, cancellation, or customer-need changes.
  • Pylon replies, internal notes, status changes, assignment, tags, snoozes, closes, or external-issue links.
  • GitHub approvals, comments, requested changes, reviewer changes, closes, merges, labels, or branch actions.

If the user says "do the quick ones", first show the exact proposed writes and ask for confirmation unless they already named the exact row IDs.

Use this table before writes:

IDSystemItemRecommended ActionProposed WriteConfidenceHuman Decision

Output

Return valid Markdown. Keep the overview concise and action-first.

Default structure:

  1. Top Actions: the highest-priority items across all systems.
  2. Quick Wins: items likely under 15 minutes.
  3. Full Queue: grouped by Linear, Pylon, and GitHub.
  4. Decisions Needed: stale closes, cancellations, comments, approvals, or status changes that require approval.

For each item include:

  • system and link or identifier;
  • title or short description;
  • evidence from current data;
  • action recommendation;
  • confidence.

Use concrete dates for age and stale reasoning. Avoid vague phrases like "recently" when exact timestamps are available.

Frequently asked questions about Housekeeping

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