Borrowing it
Nothing to install: this file belongs to Othmane-Khadri/YALC-the-GTM-operating-system. 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/Othmane-Khadri/YALC-the-GTM-operating-system/main/.claude/skills/answer-linkedin-comments/SKILL.mdgit clone --depth 1 https://github.com/Othmane-Khadri/YALC-the-GTM-operating-systemWrote 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/othmane-khadri/yalc-the-gtm-operating-system/answer-linkedin-comments)<a href="https://agentmods.dev/skills/othmane-khadri/yalc-the-gtm-operating-system/answer-linkedin-comments"><img src="https://agentmods.dev/badge/skills/othmane-khadri/yalc-the-gtm-operating-system/answer-linkedin-comments/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/othmane-khadri/yalc-the-gtm-operating-system/answer-linkedin-comments"><img src="https://agentmods.dev/badge/skills/othmane-khadri/yalc-the-gtm-operating-system/answer-linkedin-comments.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 3 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Privilege Escalation · line 36 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
- medium MCP Rug Pull · line 37 npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.Fix: Pin the version: npx @scope/[email protected]
- medium MCP Rug Pull · line 53 npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.Fix: Pin the version: npx @scope/[email protected]
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 | $0.00092 | $0.00551 |
| Opus 5 | $0.00046 | $0.00275 |
| Sonnet 5 | $0.00018 | $0.00110 |
| Haiku 4.5 | $0.00009 | $0.00055 |
Grade A, and why
answer-linkedin-comments 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 12d 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
Answer LinkedIn Comments
I'll wrap linkedin:answer-comments. Take a post URL, fetch comments via Unipile, draft replies in the user's voice, ask for approval, and send on yes.
When This Skill Applies
- "answer comments on my post"
- "reply to LinkedIn comments"
- "respond to engagement on this post"
- "draft replies to commenters"
- "answer the LinkedIn thread"
NOT this skill (use scrape-post-engagers instead):
- "scrape the engagers off this post" — that produces a result set; this skill produces replies.
NOT this skill (use personalize-message instead):
- "draft a DM to this commenter" — DM is one-to-one; this skill is for the public thread.
Workflow
Step 0 — Ask for the LinkedIn post URL
"What's the LinkedIn post URL with comments to answer?"
Step 1 — Validate URL
Step 2 — Shell out to draft
cd ~/Desktop/gtm-os && set -a && source .env.local && set +a && \
npx tsx src/cli/index.ts linkedin:answer-comments --url <url> --draft-only
(Verify exact flag via --help. The skill always drafts first, then asks before sending.)
Step 3 — Render the drafts
Show each comment + its drafted reply.
Step 4 — Ask for approval per comment OR bulk
"Send all? (yes / approve some / cancel)"
Step 5 — Shell out to send (if approved)
npx tsx src/cli/index.ts linkedin:answer-comments --url <url> --approved <ids>
Step 6 — Render send result
Notes
- Replies use the brand voice from
~/.gtm-os/brand-voice.yamlif present; otherwise defaults to a neutral conversational tone. - The CLI never auto-sends without
--approved. Drafts always render to chat first. - Skips comments from the post author themselves (no self-replies).
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 12d ago First seen · 63 lines · 92 tokens per session scan A fb7427ebe0ce
answer-linkedin-comments is a skill published in the GitHub repository Othmane-Khadri/YALC-the-GTM-operating-system (301 stars, last pushed 22d ago), licensed MIT. It adds 92 tokens to every session and 551 once invoked, about $0.0005 per session on Opus 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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Use when orchestrating a full Knowns spec or task wave through planning, implementation, review, integration, and verification, optionally using sub-agents when scopes are parallel-safe.
kn-debug
Use when debugging errors, test failures, build issues, or blocked tasks — structured triage to fix to learn.
kn-research
Use when you need to understand existing code, find patterns, search project knowledge, investigate current external facts, or explore a large codebase before implementation.