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 agentmods add commands/iushv/linkedin-agent-mcp/reviewgit clone --depth 1 https://github.com/iushv/linkedin-agent-mcpWrote 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/commands/iushv/linkedin-agent-mcp/review)<a href="https://agentmods.dev/commands/iushv/linkedin-agent-mcp/review"><img src="https://agentmods.dev/badge/commands/iushv/linkedin-agent-mcp/review.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00007 | $0.01358 |
| Opus 5 | $0.00003 | $0.00679 |
| Sonnet 5 | $0.00001 | $0.00272 |
| Haiku 4.5 | $0.00001 | $0.00136 |
Grade A, and why
review scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
89% identical to review-pr — 66 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 208 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Comprehensive PR Review
Run a comprehensive pull request review using multiple specialized agents, each focusing on a different aspect of code quality. You can review in plan mode, the review doesnt require modifications until the user approves the final plan with the suggested fixes.
Review Aspects (optional): "$ARGUMENTS"
Review Workflow:
-
Determine Review Scope
- Check git status to identify changed files
- Parse arguments to see if user requested specific review aspects
- Default: Run all applicable reviews
-
Available Review Aspects:
- comments - Analyze code comment accuracy and maintainability
- tests - Review test coverage quality and completeness
- errors - Check error handling for silent failures
- types - Analyze type design and invariants (if new types added)
- code - General code review for project guidelines
- simplify - Simplify code for clarity and maintainability
- all - Run all applicable reviews (default)
-
Identify Changed Files
- Run
git diff --name-onlyto see modified files - Check if PR already exists:
gh pr view - Identify file types and what reviews apply
- Run
-
Determine Applicable Reviews
Based on changes:
- Always applicable: code-reviewer (general quality)
- If test files changed: pr-test-analyzer
- If comments/docs added: comment-analyzer
- If error handling changed: silent-failure-hunter
- If types added/modified: type-design-analyzer
- After passing review: code-simplifier (polish and refine)
-
Launch Review Agents
Sequential approach (user can request one at a time):
- Easier to understand and act on
- Each report is complete before next
- Good for interactive review
Parallel approach (default):
- Launch all agents simultaneously
- Faster for comprehensive review
- Results come back together
-
Aggregate Results
After agents complete, summarize:
- Critical Issues (must fix before merge)
- Important Issues (should fix)
- Suggestions (nice to have)
- Positive Observations (what's good)
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.
- 5d ago First seen · 208 lines · 7 tokens per session scan A 6ee5946533f2
review is a command published in the GitHub repository iushv/linkedin-agent-mcp (1 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 7 tokens to every session and 1,358 once invoked, about $0.0000 per session on Opus 5. A static security scan graded it A with 0 findings. It is 89% identical to review-pr, differing in 66 lines, and is treated as a copy.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.