Borrowing it
Nothing to install: this file belongs to tarunkorat/claude-linkedin-post-creator. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/tarunkorat/claude-linkedin-post-creator/main/.claude/commands/post/log.mdgit clone --depth 1 https://github.com/tarunkorat/claude-linkedin-post-creatorWrote 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/tarunkorat/claude-linkedin-post-creator/log)<a href="https://agentmods.dev/commands/tarunkorat/claude-linkedin-post-creator/log"><img src="https://agentmods.dev/badge/commands/tarunkorat/claude-linkedin-post-creator/log.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 | $0.00000 | $0.00614 |
| Opus 5 | $0.00000 | $0.00307 |
| Sonnet 5 | $0.00000 | $0.00123 |
| Haiku 4.5 | $0.00000 | $0.00061 |
Grade A, and why
log 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 7d 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.
How it starts
The opening of the file, as written. The whole thing — 80 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LOG — Record engagement for a published post
Use this after you post to LinkedIn. Run /post log to pick a draft, mark it posted, and record likes/comments/shares.
No Chrome needed.
Step 1 — Show unlogged drafts
Read posts/tracker.csv. Read all draft_*.md files in posts/.
Find drafts that are NOT yet in tracker.csv (match by slug from filename).
If all drafts are already logged:
All drafts are logged. Nothing new to record. Show the tracker table and exit.
Show unlogged drafts as a numbered list:
Unlogged drafts:
1. draft_2026-05-13_react-server-components.md — React Server Components
2. draft_2026-05-14_career-growth-tips.md — Career Growth Tips
...
Which one did you post? (enter number, or "all" to log multiple)
Wait for response.
Step 2 — Collect engagement
For each selected draft, ask:
<Topic> — enter engagement (or press Enter to skip a field):
Likes:
Comments:
Shares:
Notes (optional — e.g. "got lots of DMs", "flopped", "wrong audience"):
Wait for input. Accept blank for any field (store as 0).
Step 3 — Append to tracker.csv
For each logged post, append a row to posts/tracker.csv:
Date,Slug,Topic,Format,Likes,Comments,Shares,Posted,Notes
- Date — from the draft filename (YYYY-MM-DD)
- Slug — from the draft filename (e.g.
react-server-components) - Topic — from the draft file's Topic field
- Format — from the draft file's format (e.g. "numbered list", "personal story", "contrarian take") — read from the draft file Style notes line
- Likes/Comments/Shares — from user input (0 if blank)
- Posted — yes
- Notes — from user input (empty if blank)
Preserve all existing rows exactly. Only append new rows.
Step 4 — Show insight (if 5+ posted entries exist)
Read all rows in tracker.csv where Posted=yes. If 5 or more rows exist, compute and show a brief performance insight:
Performance snapshot (last <N> posts):
Best format: <format with highest avg comments>
Avg likes: <number>
Avg comments: <number>
Top post: "<topic>" — <likes> likes, <comments> comments
<one sentence insight, e.g. "Story-format posts get 3x more comments than list posts for you.">
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.
- 7d ago First seen · 80 lines · 0 tokens per session scan A 5b8a7bc9971e
log is a command published in the GitHub repository tarunkorat/claude-linkedin-post-creator (6 stars, last pushed 3mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 614 tokens. 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 2026-08-31.
Other commands, from other repositories
content-pipeline
Run the full multi-agent content production pipeline.
pull-repos
Pull all repos (parent + marketplace clones + configured project repos).
security-scan
Run security audit on codebase.
test-suite
Run comprehensive test suite with coverage analysis.
standardize-claude-md
Add missing toolkit sections (Related Global Rules, Quick Start) to existing CLAUDE.md.
help
Explain Ralph Wiggum technique and available commands.