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 Kevin-Liu-01/Agent-Machines --skill social-draftgit clone --depth 1 https://github.com/Kevin-Liu-01/Agent-MachinesWrote 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/kevin-liu-01/agent-machines/social-draft)<a href="https://agentmods.dev/skills/kevin-liu-01/agent-machines/social-draft"><img src="https://agentmods.dev/badge/skills/kevin-liu-01/agent-machines/social-draft/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/kevin-liu-01/agent-machines/social-draft"><img src="https://agentmods.dev/badge/skills/kevin-liu-01/agent-machines/social-draft.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00069 | $0.00943 |
| Opus 5 | $0.00034 | $0.00472 |
| Sonnet 5 | $0.00014 | $0.00189 |
| Haiku 4.5 | $0.00007 | $0.00094 |
Grade A, and why
social-draft 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 9d 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 — 69 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Social Draft
Input Contract
Minimum viable draft input is not "write about X." It is: topic, claim, proof, why it matters, goal, tone, constraints. If proof is missing, do not invent it. Use qualitative specificity or clearly marked placeholders.
Drafting Workflow
- Distill the idea into one claim
- Find the strongest proof block
- Decide: teach, reveal, provoke, or reflect
- Pick format (X single post, X thread, LinkedIn story, LinkedIn list)
- Write the opening first
- Remove every sentence that sounds generic when detached from the proof
X Rules
Good X posts: stop the scroll in line one, compress one idea hard, sound current and alive, create curiosity without bait.
Preferred openings: specific result (I shipped X in Y), contrarian take (Most people get X wrong), build tension (The weirdest part of building X was not Y), field note (Watching X happen live changed my view on Y).
Format: target under 140 characters for single posts, under 110 for sharp takes. Thread only when genuinely needed. Links in reply, not main post. One emoji max. Close with a clean question, challenge, or takeaway.
Avoid: "Thoughts?", "Agree?", "Like and repost."
LinkedIn Rules
Good LinkedIn posts: win the first two lines, feel conversational but professional, tell one coherent story, translate detail into a lesson.
Structures: Story (hook -> event -> change -> lesson -> close), Contrarian (claim -> why common wisdom fails -> evidence -> better framing), List (hook -> numbered points -> synthesis), Lesson learned (I used to think X. Now I think Y.).
Format: paragraphs 1-2 sentences, under 1300 characters unless depth helps, line breaks aggressively, 3-5 hashtags max at bottom only.
Tone Modes
Casual: tighter sentences, lighter language, more immediacy. Avoid slang that hurts credibility.
Professional: clean sentences, explicit lessons. Avoid consulting voice and over-hedging.
Thought-leader: sharper framing, stronger claims, broader implication. Avoid prophecy without proof.
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.
- 9d ago First seen · 69 lines · 69 tokens per session scan A 86a8f156f438
social-draft is a skill published in the GitHub repository Kevin-Liu-01/Agent-Machines (29 stars, last pushed yesterday), licensed MIT. It adds 69 tokens to every session and 943 once invoked, about $0.0003 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.
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