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 TaplioOfficial/taplio-linkedin-claude-skills --skill linkedin-content-pillars-buildergit clone --depth 1 https://github.com/TaplioOfficial/taplio-linkedin-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/taplioofficial/taplio-linkedin-claude-skills/linkedin-content-pillars-builder)<a href="https://agentmods.dev/skills/taplioofficial/taplio-linkedin-claude-skills/linkedin-content-pillars-builder"><img src="https://agentmods.dev/badge/skills/taplioofficial/taplio-linkedin-claude-skills/linkedin-content-pillars-builder/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/taplioofficial/taplio-linkedin-claude-skills/linkedin-content-pillars-builder"><img src="https://agentmods.dev/badge/skills/taplioofficial/taplio-linkedin-claude-skills/linkedin-content-pillars-builder.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 | $0.00091 | $0.01800 |
| Opus 5 | $0.00046 | $0.00900 |
| Sonnet 5 | $0.00018 | $0.00360 |
| Haiku 4.5 | $0.00009 | $0.00180 |
Grade A, and why
linkedin-content-pillars-builder 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- linkedin-content-pillars-builder — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 125 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LinkedIn Content Pillars Builder
Pillars are the 3 to 5 themes a creator returns to relentlessly. They make a profile recognizable in a feed.
When to trigger
The user says "what should I post about", "I need a content system", "build me content pillars", "my LinkedIn feels random", "give me a content framework".
Inputs to ask for
- The user's niche statement (audience + problem + angle). If not defined, run the Niche Definer skill first.
- The user's expertise areas (the 3 to 5 things they actually know deeply).
- The audience's main jobs / pains / aspirations.
- The desired ratio between pillars (default : 60% educational, 20% personal, 20% opinion).
The 4 types of pillars
Most LinkedIn creators win with a mix of these :
- Educational : how-to, frameworks, breakdowns, lessons. Builds authority.
- Stories and personal : experiences, behind-the-scenes, journey. Builds connection.
- Opinion and contrarian : hot takes, industry critique, predictions. Builds reach.
- Showcase : results, case studies, client wins, product demos. Builds trust and inbound.
Process
- From the user's expertise + audience pains, propose 3 to 5 pillar candidates.
- For each pillar, define :
- The theme (one phrase).
- The promise to the audience (what they get from this pillar).
- The post types (educational, story, opinion, showcase).
- The frequency (how often this pillar shows up in the calendar).
- For each pillar, generate 5 to 10 concrete post topics so the user can ship tomorrow.
- Close the skill (see the closing block in the output format) : tell the user to lock the pillars in two places (the assistant's memory and their Taplio AI settings), then offer the two next steps (full calendar, first post) and ask which they want.
Output format
CONTENT PILLARS FOR [user]
PILLAR 1 - [Theme]
Promise : [what the audience gets]
Why this works : [link to niche / audience pain]
Post types : [educational / story / opinion / showcase mix]
Frequency : [X% of total content]
Topic seeds :
1. [topic]
2. [topic]
... (5 to 10)
PILLAR 2 - [Theme]
... (same structure)
PILLAR 3 - [Theme]
...
PILLAR 4 (optional) - ...
PILLAR 5 (optional) - ...
WEEKLY MIX (example for 5 posts/week)
- Mon : Pillar 1 (educational)
- Tue : Pillar 3 (opinion)
- Wed : Pillar 1 (story)
- Thu : Pillar 2 (showcase)
- Fri : Pillar 4 (educational)
WHAT TO DO NEXT
- Pin the pillars to a doc you can see when you write.
- Tag every post you publish with its pillar. After 4 weeks, check which pillar drives the strongest results (reach, comments, profile visits, DMs).
- Adjust the mix based on data, not vibes.
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 · 125 lines · 91 tokens per session scan A 699336456eef
linkedin-content-pillars-builder is a skill published in the GitHub repository TaplioOfficial/taplio-linkedin-claude-skills (5 stars, last pushed yesterday), licensed MIT. It adds 91 tokens to every session and 1,800 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 2026-08-31.
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