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-plugin --skill linkedin-analytics-interpretergit clone --depth 1 https://github.com/TaplioOfficial/taplio-linkedin-pluginWrote 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-plugin/linkedin-analytics-interpreter)<a href="https://agentmods.dev/skills/taplioofficial/taplio-linkedin-plugin/linkedin-analytics-interpreter"><img src="https://agentmods.dev/badge/skills/taplioofficial/taplio-linkedin-plugin/linkedin-analytics-interpreter/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-plugin/linkedin-analytics-interpreter"><img src="https://agentmods.dev/badge/skills/taplioofficial/taplio-linkedin-plugin/linkedin-analytics-interpreter.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 | $0.00095 | $0.01722 |
| Opus 5 | $0.00048 | $0.00861 |
| Sonnet 5 | $0.00019 | $0.00344 |
| Haiku 4.5 | $0.00010 | $0.00172 |
Grade A, and why
linkedin-analytics-interpreter 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.
This is a copy
100% identical to linkedin-analytics-interpreter — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 126 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LinkedIn Analytics Interpreter
Numbers without a story are wallpaper. This skill turns them into decisions.
When to trigger
The user says "look at my LinkedIn analytics", "what do these numbers mean ?", "is this good ?", "interpret my stats", "what should I do based on this ?".
Inputs to ask for
- The reporting period (last 7, 28, 90 days).
- Impressions total + trend vs previous period.
- Engagement rate average (if available).
- Profile visits total + trend.
- Follower growth : net new followers + trend.
- Top 3 to 5 posts in the period (topic, format, performance).
- Bottom 1 to 2 posts (the duds).
- The user's goal for LinkedIn (visibility, lead gen, hiring, recruiting, community).
- Posting cadence in the period (3/week, 5/week, etc.).
Reference benchmarks (rough)
Use as anchors, not absolutes :
- Engagement rate : 3 to 5% on a healthy account, 7%+ is great.
- Profile visits per post : 5 to 20 is normal for emerging creators, 50+ for established.
- Follower growth per post : 0.5 to 2 net new followers per post is healthy.
- Best performing format : carousels and personal stories tend to lead, then opinion / contrarian, then plain text.
Process
- Read the numbers and the trend.
- Spot the 2 to 3 patterns that matter (not all 12 metrics).
- Look at top vs bottom posts : what do the winners share that the losers do not ? Format ? Topic ? Hook style ? Day of week ?
- Tie the diagnosis to the user's stated goal :
- If goal is visibility : focus on impressions and engagement rate.
- If goal is lead gen : focus on profile visits to DM conversion.
- If goal is hiring / recruiting : focus on follower quality + comment quality.
- If goal is community : focus on comment depth and repeat engagers.
- Recommend 3 specific actions for the next month. Not 10. Three.
Output format
ANALYTICS DIAGNOSIS - [period]
THE NUMBERS AT A GLANCE
- Impressions : [X] ([trend vs previous])
- Engagement rate : [X%] ([trend])
- Profile visits : [X] ([trend])
- Follower growth : [X] net new ([trend])
- Posting cadence : [X posts]
WHAT IS WORKING
[2 to 3 specific observations from the top posts and trends]
WHAT IS NOT WORKING
[1 to 2 specific weak spots]
PATTERN MATCH (top vs bottom posts)
- Top posts share : [common factors]
- Bottom posts share : [common factors]
3 ACTIONS FOR NEXT MONTH
ACTION 1
What : [specific thing to do]
Why : [pattern this exploits]
How to measure success : [the metric to watch]
ACTION 2
What : ...
Why : ...
How to measure success : ...
ACTION 3
What : ...
Why : ...
How to measure success : ...
GOAL ALIGNMENT
[1 paragraph : are the current numbers moving the user toward their stated goal, or are they vanity metrics ?]
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 · 126 lines · 95 tokens per session scan A 5b297264d54f
linkedin-analytics-interpreter is a skill published in the GitHub repository TaplioOfficial/taplio-linkedin-plugin (2 stars, last pushed 2mo ago), licensed MIT. It adds 95 tokens to every session and 1,722 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to linkedin-analytics-interpreter, differing in 0 lines, and is treated as a copy.
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