linkedin-mcp-server: Skill for Claude Code

.agents/skills/triage-reviews/SKILL.md

triage-reviews is a skill for Claude Code from stickerdaniel/linkedin-mcp-server. It costs 24 tokens per session (1,433 once invoked), scanned A, original, Apache-2.0.

A workflow for collecting pull-request review comments, checking them against the actual code and documentation, and handling the valid issues. A pull request is a proposed set of code changes submitted for review.

In plain words
What is it for?
Use it to triage pull-request feedback, fix confirmed issues, commit the changes, and push them to the repository.
Why use it?
It separates real problems from outdated, duplicated, or incorrect review comments. It helps turn scattered feedback from different places into verified code changes.

Skill for Claude Code

Written for Claude Code: argument-hint in frontmatter. Also seen: installed under .agents/ (shared by several agents).

This is stickerdaniel/linkedin-mcp-server's own configuration. It tells Claude Code how to work on linkedin-mcp-server itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything linkedin-mcp-server configures →

About the project

linkedin-mcp-server is a local MCP server that gives compatible AI assistants access to LinkedIn through a logged-in browser session. Agents can use it to inspect profiles and companies, search jobs, retrieve job details, and manage certain LinkedIn interactions. The catalogue add-ons operate this server and expose its LinkedIn workflows to agents.

stickerdaniel/linkedin-mcp-server · 4,014 stars · on GitHub

Reuse

Borrowing it

Nothing to install: this file belongs to stickerdaniel/linkedin-mcp-server. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/stickerdaniel/linkedin-mcp-server/main/.agents/skills/triage-reviews/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/stickerdaniel/linkedin-mcp-server

Made for: Claude Code.

Wrote 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.

agentmods badge for triage-reviews

README.md
[![agentmods](https://agentmods.dev/badge/skills/stickerdaniel/linkedin-mcp-server/triage-reviews/github.svg)](https://agentmods.dev/skills/stickerdaniel/linkedin-mcp-server/triage-reviews)
Your own site
<a href="https://agentmods.dev/skills/stickerdaniel/linkedin-mcp-server/triage-reviews"><img src="https://agentmods.dev/badge/skills/stickerdaniel/linkedin-mcp-server/triage-reviews/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.

agentmods 80×15 button for triage-reviews

Your own site · 80×15
<a href="https://agentmods.dev/skills/stickerdaniel/linkedin-mcp-server/triage-reviews"><img src="https://agentmods.dev/badge/skills/stickerdaniel/linkedin-mcp-server/triage-reviews.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 24 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,433 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00024 $0.01433
Opus 5.5 $0.00010 $0.00573
Sonnet 5.5 $0.00005 $0.00287
Haiku 4.5 $0.00002 $0.00143

Measured 25d ago against content hash 473d97473b1f, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

triage-reviews 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 25d 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.

Origin

Copies of this mod

5 near-identical copies found in the catalogue:

.agents/skills/triage-reviews/SKILL.md · 128 lines

How it starts

The opening of the file, as written. The whole thing — 128 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Triage PR Review Comments

Fetch all review comments on the current PR, verify each finding against real code, fix valid issues, and push.

Phase 1: Gather Comments

  1. Determine the PR number:

    • Use $ARGUMENTS if provided
    • Otherwise: gh pr view --json number --jq .number
  2. Fetch ALL comments (reviewers post in multiple places):

    gh api --paginate repos/{owner}/{repo}/pulls/{pr}/reviews
    gh api --paginate repos/{owner}/{repo}/pulls/{pr}/comments
    gh api --paginate repos/{owner}/{repo}/issues/{pr}/comments
    
  3. Extract unique findings — deduplicate across Copilot, Greptile, and human reviewers. Group by file and line.

Phase 2: Verify Each Finding

For EVERY finding, verify against real code before accepting or rejecting:

  1. Read the actual code at the referenced file:line
  2. Check if the issue still exists — it may already be fixed in a later commit
  3. Verify correctness using:
    • Code analysis (read surrounding context, trace call paths)
    • Run btca resources to see what's available, then btca ask -r <resource> -q "..." for library/framework questions
    • Web search for API behavior, language semantics, or CVEs
  4. Classify each finding:
    • Valid — real bug, real gap, or real improvement needed
    • False positive — reviewer misread the code, outdated reference, or style preference

Phase 3: Fix & Ship

  1. Fix all Valid findings
  2. Run the project's lint/test commands
    • If lint/tests fail, fix the failures before committing
    • If a failure cannot be fixed automatically, skip that fix and report it as Valid (unfixed) in the Phase 4 table
  3. git add only changed files, git commit with message:
    fix: Address PR review feedback
    
    - <one-line summary per fix>
    
  4. Push: gt submit (or git push if not using Graphite)

Phase 3.5: Reply on each thread, then resolve it

Pushing the fix isn't enough. Each inline review comment lives in its own thread that GitHub keeps showing as "Unresolved" until someone explicitly resolves it. The skill must close that loop for every Valid and False-positive finding so the PR view actually reflects what was triaged.

Read the full file on GitHub · 128 lines

Changes

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.

  1. 25d ago Changed 473d97473b1f
  2. 1mo ago First seen · 128 lines · 24 tokens per session scan A 82583f75645b

Subscribe to this mod's changes

triage-reviews is a skill published in the GitHub repository stickerdaniel/linkedin-mcp-server (4,014 stars, last pushed yesterday), licensed Apache-2.0. It adds 24 tokens to every session and 1,433 once invoked, about $0.0001 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 2026-08-30.

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