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-post-performance-criticgit 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-post-performance-critic)<a href="https://agentmods.dev/skills/taplioofficial/taplio-linkedin-claude-skills/linkedin-post-performance-critic"><img src="https://agentmods.dev/badge/skills/taplioofficial/taplio-linkedin-claude-skills/linkedin-post-performance-critic/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-post-performance-critic"><img src="https://agentmods.dev/badge/skills/taplioofficial/taplio-linkedin-claude-skills/linkedin-post-performance-critic.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 | $0.00104 | $0.01585 |
| Opus 5 | $0.00052 | $0.00792 |
| Sonnet 5 | $0.00021 | $0.00317 |
| Haiku 4.5 | $0.00010 | $0.00159 |
Grade A, and why
linkedin-post-performance-critic 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-post-performance-critic — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 109 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LinkedIn Post Performance Critic
Most posts fail in pre-flight, not on the runway. This skill catches the failures before publish.
When to trigger
The user says "review this post before I publish", "is this good ?", "critique my draft", "spot the weaknesses in this", "what would you change ?".
Inputs to ask for
- The full post draft.
- The CTA goal (comments, DMs, follows, clicks).
- The audience.
- The user's positioning (so the critique stays on-brand, not generic).
The 6 audit dimensions
- Hook : do lines 1-2 stop the scroll alone, without context ?
- Structure : is the body scannable ? White space, one idea per line, no walls of text ?
- Specificity : real names, real numbers, real moments ? Or vague "businesses", "lots of growth", "many lessons" ?
- Voice : does it sound like the user, or like an LLM ? Cliché-flag : "delve, leverage, in today's fast-paced world".
- Payoff : does the body deliver on the hook's promise ?
- CTA : does the closing earn the desired action, or default to "Thoughts ?".
Process
- Score each dimension on a 1-5 scale.
- Identify the 2 most impactful fixes. Do not overwhelm with 6 fixes.
- Rewrite the weakest section so the user sees a concrete before / after.
- Give a final publish / rewrite / kill verdict.
Output format
POST AUDIT
SCORES
- Hook : X/5 - [one-liner]
- Structure : X/5 - [one-liner]
- Specificity : X/5 - [one-liner]
- Voice : X/5 - [one-liner]
- Payoff : X/5 - [one-liner]
- CTA : X/5 - [one-liner]
OVERALL : X/5
TOP 2 FIXES
FIX 1 - [dimension]
What is wrong : [one-liner]
Concrete change : [what to do]
FIX 2 - [dimension]
What is wrong : [one-liner]
Concrete change : [what to do]
REWRITE OF THE WEAKEST SECTION
Original : "[paste the weak chunk]"
Rewrite : "[the improved version]"
VERDICT
- PUBLISH AS IS : [if 4+ on every dimension]
- PUBLISH AFTER 5-MIN FIXES : [if 1-2 weak spots fixable fast]
- REWRITE : [if 3+ dimensions are below 3, or the angle is fundamentally off]
- KILL : [if the post has no clear takeaway, no audience match, or is plain self-promo]
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 · 109 lines · 104 tokens per session scan A 2dbe314f1505
linkedin-post-performance-critic is a skill published in the GitHub repository TaplioOfficial/taplio-linkedin-claude-skills (5 stars, last pushed yesterday), licensed MIT. It adds 104 tokens to every session and 1,585 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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