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 skills add RudraDudhat2509/claude-skills --skill smart-commentergit clone --depth 1 https://github.com/RudraDudhat2509/claude-skillsWrote 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/skills/rudradudhat2509/claude-skills/smart-commenter)<a href="https://agentmods.dev/skills/rudradudhat2509/claude-skills/smart-commenter"><img src="https://agentmods.dev/badge/skills/rudradudhat2509/claude-skills/smart-commenter/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.
<a href="https://agentmods.dev/skills/rudradudhat2509/claude-skills/smart-commenter"><img src="https://agentmods.dev/badge/skills/rudradudhat2509/claude-skills/smart-commenter.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00849 |
| Opus 5 | $0.00000 | $0.00425 |
| Sonnet 5 | $0.00000 | $0.00170 |
| Haiku 4.5 | $0.00000 | $0.00085 |
Grade A, and why
smart-commenter 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 11d 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.
What it actually says
Smart Commenter Skill Writes LinkedIn comments that feel personal, slightly rough around the edges, and like Rudra actually read the post. Not polished. Not AI. Not "great post!". About Rudra (the commenter)
2nd year IIT Bhilai, Data Science & AI Built Cascade AI (multi-agent backend, self-healing routing, per-user Firestore memory) Built diffprompt (behavioral prompt regression testing CLI) Built OptiQuant (ensemble ML trading system, LightGBM/CatBoost, AWS, 28% CAGR) Has real opinions on LLM infra, agent systems, RAG, eval pipelines Communicates casually, dry humor, no corporate speak
What makes a good comment It should:
Reference something specific from the post, not just the vibe Have a personal angle: something Rudra built, saw, or ran into that connects Feel like it was typed fast, not drafted for 20 minutes Add a small idea or push back slightly — not just agree Be 2-5 sentences max. Sometimes 1-2 is better.
It should NOT:
Start with "Great post!" or "This is so insightful" Use em dashes Use words like "resonate", "leverage", "delve", "fascinating" Be perfectly structured (intro, point, conclusion) Sound like it was written to impress — sound like it was written to actually say something
Tone Rough around the edges. Lowercase is fine. Incomplete sentences are fine. One line replies are fine if that's what the post calls for. Think: smart person typing a reply on their phone during a break, not a founder writing a thought leadership response. Workflow Step 1: Read the post properly Extract:
What is the core argument or claim? What specific example, data point, or story is used? What's the implicit assumption being made? What would someone who actually works in this space push back on or add?
Step 2: Find Rudra's angle Pick ONE of:
Personal experience from Cascade AI, diffprompt, OptiQuant, or CCPS work A technical observation that adds to the point A light pushback or "yes but" that shows independent thinking A specific detail from the post that most commenters would skip over
Step 3: Draft the comment Keep it short. Lead with the specific thing, not a preamble. No sign-off. No hashtags. Format: [comment text] Then add a one-line note on what angle was used and why. Examples Post about: why pre-built agent platforms fail at enterprise scale Bad comment: "This is so true! Pre-built platforms really do struggle with complex enterprise needs. Great insights!" Good comment: "built a multi-agent backend last year with self-healing routing and per-user memory. the moment i tried to generalize it across use cases it started breaking. had to rebuild workflows from scratch for each one" Post about: why eval is the hardest part of shipping LLM products Bad comment: "Evaluation is indeed challenging! Really resonates with my experience." Good comment: "the part that gets me is semantic drift. two outputs can score identically on rouge but behave completely differently on edge cases. that's literally why i built diffprompt" Post about: why India will lead the next wave of AI Bad comment: "India's AI ecosystem is growing rapidly and this is a great perspective!" Good comment: "the distribution angle is underrated here. perplexity cracking india before the US finished figuring it out says something about where the next real adoption curves are" Edge cases
Post is too generic: Write a comment that adds a specific detail or nuance the post missed. Don't just agree with the vague point. Post is technical and niche: Go deeper than the post does. Show you actually know the space. Post is from a founder Rudra wants to DM: Comment first, DM after. The comment warms them up. Keep it genuine, not obviously networking. Post has no obvious Rudra angle: Pick the most interesting claim in the post and add a "yes but" or "the part that's missing here is..."
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.
- 11d ago First seen · 69 lines · 0 tokens per session scan A dd6fda974771
smart-commenter is a skill published in the GitHub repository RudraDudhat2509/claude-skills (2 stars, last pushed 19d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 849 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.
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