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1-triage-issues

Prioritize open LinkedIn integration issues and pull requests, or proposed code changes, using report completeness, severity, reach, and readiness checks. Produce ranked lists, identify missing evidence, and recommend concrete next steps for maintainers.

中文简介正在准备中。当前显示英语简介。

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Triage backlog, scan open issues, rank what to tackle first, or which PRs are production-ready.

集成工具LinkedInGitHub
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适用场景

  • Prioritizing a backlog of open issues
  • Separating reports that need more evidence
  • Comparing pull requests for production readiness
  • Planning the next maintenance tasks

包含的文件(1)

  • SKILL.md7.4 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

Triage open issues and PRs

Turn the live state of stickerdaniel/linkedin-mcp-server into a maintainer priority list. Read-only. No checkout, no labels, no comments, no closure, no reproduction.

Reproduction belongs in /2-repro-issue. PR verification belongs in /3-verify-pr-fix.

Inputs

$ARGUMENTS is optional. If set, treat as a label name (bug, enhancement) or freetext filter. Otherwise scan everything open.

1. Gather

REPO=stickerdaniel/linkedin-mcp-server

# Issues  (gh calls the reactions field `reactionGroups`, not `reactions`)
gh issue list --repo $REPO --state open --limit 200 \
  --json number,title,labels,createdAt,updatedAt,author,comments,reactionGroups,body

# PRs
gh pr list --repo $REPO --state open --limit 100 \
  --json number,title,labels,createdAt,updatedAt,author,mergeable,mergeStateStatus,statusCheckRollup,additions,deletions,changedFiles,reviewDecision,isDraft,headRepositoryOwner

# Cross-link issues ↔ PRs via body references
gh search prs --repo $REPO --state open --json number,body,title --limit 100

Build a map issue_number → [referencing_pr_numbers] by scanning PR bodies for Closes #, Fixes #, Resolves #, plain #N.

If $ARGUMENTS is given, filter both lists before scoring.

Read each issue body and its comments, including linked canonical issues.

2. Packet admission

Before severity, age, reach, or contributor ranking, classify every open issue.

Packet stateMaintainer action
Material evidence missingRecommend needs more info with the specific missing fields. Exclude it from the reproduction shortlist. Existing labels alone do not prove completeness.
Evidence supplied after a requestRead the new evidence. Recommend removing needs more info only when it resolves the material gap. A new comment does not automatically clear the label.
Same underlying report as a canonical issueNote unique reviewed evidence for the canonical issue. Recommend a Duplicate of #N comment and a separate close, each with an explicit yes. Do not post or close here.
Evaluable bug or featureContinue to scoring. Completeness is not a promise of implementation.
Source evidence supports a narrower claim than the reporter's explanationState that narrower finding. Ask for targeted missing evidence only if the next decision needs it.

This step is complete when every scanned issue is either evaluable, missing-evidence with a field list, or a named canonical duplicate. Incomplete packets never enter the reproduction shortlist.

3. Score each evaluable issue

For every evaluable open issue, compute four signals. Cite the evidence inline.

  • Severity (1 to 5): 5 = data loss / broken-for-all-users / security; 4 = core tool unavailable (e.g. get_person_profile returning empty); 3 = degraded output for a subset (locale, edge-case profile); 2 = annoyance / cosmetic; 1 = question / docs. Read the body + labels (bug, critical, security, regression).
  • Reach (1 to 5): comment count, distinct commenters, +1 reactions, references from other issues.
  • Age vs activity: createdAt → days old; updatedAt → days since last activity. Stale-but-high-severity ranks higher than fresh-but-cosmetic.
  • Has fix in flight: did anyone open a PR (look up in cross-link map)? Is that PR ready to merge?

4. Score each PR

For every open PR, on top of its linked-issue score:

  • Mergeability: mergeable: MERGEABLE + mergeStateStatus: CLEAN + statusCheckRollup all green → ✓. Otherwise note what blocks (CI red, conflicts, requested changes, draft).
  • Scope: additions + deletions and changedFiles. Flag scope creep. Cross-check gh pr diff <N> --name-only.
  • Locale + DOM safety audit: do gh pr diff <N> | grep -E "['\"](Connect|Follow|Message|Pending|1st|2nd|3rd)['\"]" (matches both Python-style 'Connect' and JS/Go-style "Connect"). Any string match on locale-dependent button text is a red flag per the AGENTS.md LinkedIn Page Rules. Also flag class-name selectors (.entity-result__item). Minimal generic selectors only.
  • One-section-one-navigation: if the PR touches PERSON_SECTIONS / COMPANY_SECTIONS in linkedin/fields.py, check that each entry still maps to exactly one URL.
  • Test coverage: does the diff add coverage beside the canonical owner from docs/linkedin-architecture.md? Most owners use tests/linkedin/test_<owner>.py; fields, identifiers, and link_metadata use tests/test_fields.py, tests/test_identifiers.py, and tests/test_link_metadata.py. Facade-only changes belong in tests/linkedin/test_facade_*.py. Mandatory for new tool surfaces, strongly preferred for bug fixes.
  • Contributor audit:
    gh search prs --repo $REPO --author <login> --state merged --json number,createdAt,mergedAt,additions,deletions --limit 20
    gh search prs --repo $REPO --author <login> --state closed --json number,closedAt,state --limit 10
    
    Compute: prior merged PRs in this repo, average time-to-merge, ratio of merged-vs-closed-unmerged. First-time contributors are not penalised. A contributor whose previous PRs were all closed-unmerged with maintainer pushback is a yellow flag on a large diff. Cite specific PR numbers.

For very large or architecturally-loaded PRs (changes spanning canonical page workflow owners or the generated graph in docs/linkedin-architecture.md, client/, or session/auth code), spawn an Explore subagent to deep-dive how the PR integrates with the codebase and report integration risk. Treat linkedin/extractor.py as the thin facade, not the default workflow owner. Use this sparingly: only for PRs over ~200 LOC or PRs touching core paths.

5. Rank and report

Two ranked tables, a missing-evidence list, and a short verdict.

## Issues: top 10 evaluable

| # | Issue | Severity | Reach | Age | PR? | Why this rank |
|---|-------|----------|-------|-----|-----|---------------|
| 1 | #366  | 4        | 8     | 12d | #366 ready | Core tool broken, contributor inactive |

## Needs more info (not a reproduction shortlist)

| Issue | Missing fields |
|-------|----------------|
| #986  | UI language, region, product, redacted compose capture |

## PRs: production-readiness ranking

| # | PR | Linked issue | Mergeable | Scope | Locale-safe | Tests | Contributor | Verdict |
|---|----|--------------|-----------|-------|-------------|-------|-------------|---------|
| 1 | #386 | #385       | ✓         | +120/-40, 3 files | ✓ | ✓ | 5 prior merged | Ready to merge |

## Recommended order this week

1. Merge #386. Clean fix, ready.
2. Request the missing fields on #986. Do not send it to /2-repro-issue yet.
3. Investigate #389 from packet and source (→ `/2-repro-issue 389`).

End with one paragraph naming the next concrete action, and which downstream skill applies. Only evaluable items go to /2-repro-issue. /3-verify-pr-fix is for candidate PRs.

Non-negotiables

  • Read-only. Do not check out, start the MCP server, call its LinkedIn tools, apply labels, comment, close, or assign.
  • Cite evidence inline (PR/issue numbers, file paths, contributor PR history).
  • Locale-dependence and DOM-class-selector usage are hard fails.
  • Never recommend "merge as-is" for a PR that has not passed the locale/test/scope checks.

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同一仓库的技能

Investigate LinkedIn integration bugs by comparing issue reports with source code. Separate established facts from unverified claims, identify missing evidence, and optionally check reproduction in a live environment after explicit approval.

中文简介正在准备中。当前显示英语简介。

  • Investigating a reported LinkedIn integration bug
  • Identifying evidence missing from an issue report
  • Checking live reproduction after approval
stickerdaniel/linkedin-mcp-server39652026年10月20日 更新

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中文简介正在准备中。当前显示英语简介。

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中文简介正在准备中。当前显示英语简介。

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中文简介正在准备中。当前显示英语简介。

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中文简介正在准备中。当前显示英语简介。

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中文简介正在准备中。当前显示英语简介。

  • Deduplicating feedback from multiple reviewers
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stickerdaniel/linkedin-mcp-server39652026年10月20日 更新

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