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-post-performance-criticgit 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-post-performance-critic)<a href="https://agentmods.dev/skills/taplioofficial/taplio-linkedin-plugin/linkedin-post-performance-critic"><img src="https://agentmods.dev/badge/skills/taplioofficial/taplio-linkedin-plugin/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-plugin/linkedin-post-performance-critic"><img src="https://agentmods.dev/badge/skills/taplioofficial/taplio-linkedin-plugin/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 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-post-performance-critic — 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 — 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.
- 9d 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-plugin (2 stars, last pushed 2mo ago), 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. It is 100% identical to linkedin-post-performance-critic, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
financial-statement-analysis
Reads a set of financial statements and establishes what changed and why — fluctuation analysis against prior period and against budget, profitability, liquidity, solvency and efficiency ratios, benchmarking, and the non-GAAP measures presented alongside them. Use this to interpret results, review a counterparty's or…
youtube-producer
Plans, packages, and scripts long-form video for retention and channel growth — idea selection, titles and thumbnails, script structure, and diagnosing why a video or channel underperforms. Use this for video ideas, packaging, scripting, a retention teardown, or channel strategy — including when someone describes a…
scenario-planning
Plans under genuine uncertainty — building scenarios, identifying which assumptions are load-bearing, setting early-warning indicators, and stress-testing a plan against futures rather than forecasting one. Use this when a decision depends on something unknowable, when a plan assumes conditions that may not hold…
ai-ml-governance
Governs models and AI systems in production — intended use, evaluation, monitoring, human oversight, documentation, and the decision to deploy or retire. Use this before deploying a model or AI feature, when defining evaluation criteria, when a model's behavior has drifted, when assessing AI risk or regulatory…
paid-advertising
Plans, runs, and optimizes paid acquisition across search, social, and display — account structure, targeting, creative, bidding, budget, and the analysis that says whether to scale or stop. Use this to set up or restructure campaigns, write and iterate ad creative, diagnose rising costs or falling performance, decide…
ai-research-analyst
Produces executive-level research — market sizing, competitor mapping, trend analysis, and strategic intelligence — grounded in cited sources with the confidence in each claim made explicit. Use this to analyze a market or industry, map competitors, evaluate a market-entry or build-versus-buy decision, produce a…