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.
git clone --depth 1 https://github.com/Amey-Thakur/AI-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/commands/amey-thakur/ai-skills/linkedin-about)<a href="https://agentmods.dev/commands/amey-thakur/ai-skills/linkedin-about"><img src="https://agentmods.dev/badge/commands/amey-thakur/ai-skills/linkedin-about/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/commands/amey-thakur/ai-skills/linkedin-about"><img src="https://agentmods.dev/badge/commands/amey-thakur/ai-skills/linkedin-about.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 | $0.00025 | $0.00384 |
| Opus 5 | $0.00013 | $0.00192 |
| Sonnet 5 | $0.00005 | $0.00077 |
| Haiku 4.5 | $0.00003 | $0.00038 |
Grade A, and why
linkedin-about 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 8d 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
You were invoked as a slash command. The user's input:
$ARGUMENTS
Use that input to fill this prompt's variables (take the main content, topic, or task from it; ask only if a required value is missing and not supplied), then follow the prompt exactly.
Write my LinkedIn headline and About section.
Background: {background} Goal: {goal}
Headline (the 220-character line under your name, shown everywhere):
- Not just your job title. Convey who you are, what you do, and the value or the specialty, in a way that makes the right person want to click. Give 3 options.
About section:
- Open with a strong first line or two (only these show before "see more", so they must hook the reader you want).
- Written in first person, as a real human: your story, what you do, what you are good at, and what you care about or are looking for. Not a resume in paragraph form, and not stiff third-person corporate-speak.
- Lead with value to the reader (what you can do for them / why they should care), supported by concrete evidence (specifics, results, scope).
- End with what you want (connections, opportunities, a call to action) if it fits the goal.
Rules: authentic and specific over generic buzzword soup ("results-driven professional passionate about synergy" is invisible). Confident without bragging. Skimmable (short paragraphs). Real numbers and specifics where you have them. Mark anything I have not given as a placeholder. Tailor the emphasis to the goal.
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.
- 8d ago First seen · 42 lines · 25 tokens per session scan A f80333d2efd0
linkedin-about is a command published in the GitHub repository Amey-Thakur/AI-SKILLS (7 stars, last pushed 5d ago), licensed MIT. It adds 25 tokens to every session and 384 once invoked, about $0.0001 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 2026-09-03.
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.