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-story-extractorgit 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-story-extractor)<a href="https://agentmods.dev/skills/taplioofficial/taplio-linkedin-claude-skills/linkedin-story-extractor"><img src="https://agentmods.dev/badge/skills/taplioofficial/taplio-linkedin-claude-skills/linkedin-story-extractor/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-story-extractor"><img src="https://agentmods.dev/badge/skills/taplioofficial/taplio-linkedin-claude-skills/linkedin-story-extractor.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 | $0.00102 | $0.01320 |
| Opus 5 | $0.00051 | $0.00660 |
| Sonnet 5 | $0.00020 | $0.00264 |
| Haiku 4.5 | $0.00010 | $0.00132 |
Grade B, and why
linkedin-story-extractor scanned grade B with 1 finding 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.
Strips warnings and disclaimersmediumAnti-refusal
Omitting safety caveats hides risk from the user and is a common jailbreak preamble.
- Never moralize. The reader extracts the lesson, you only set it up. Copies of this mod
1 near-identical copy found in the catalogue:
- linkedin-story-extractor — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 97 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LinkedIn Story Extractor
Most professionals live great stories every week. They just do not see them. This skill mines the story out of a raw dump.
When to trigger
The user says "I had this experience but I do not know how to post it", "something happened today, can I post about it ?", "I have a story but it sounds boring", "help me find the angle in this".
Inputs to ask for
- The raw experience. Encourage the user to dump everything : what happened, who was involved, what they felt, what they did, what changed.
- Why this matters to them today (so you can find the lesson).
- The audience (so you can frame the lesson).
Story arc to enforce
Every good LinkedIn story follows this arc :
- Situation : the context, in 2 lines max.
- Tension : what made it hard, risky, or weird.
- Turning point : the decision, the moment, the realization.
- Outcome : what happened.
- Lesson : what the reader can take away.
Process
- Read the dump. Identify the strongest tension. If there is no tension, ask the user to add one (a fear, a doubt, an obstacle).
- Compress the situation into 2 lines.
- Write the tension in present tense, even if it happened years ago.
- Mark the turning point with a one-line shift ("Then I decided to...", "So I called...", "That is when I realized...").
- Tell the outcome simply, without bragging.
- Land the lesson in 1 to 2 lines. The lesson must be portable, the reader must be able to apply it.
Output format
STORY ANGLE
[one-line summary of why this story matters]
LINKEDIN POST
[hook : the most surprising fact of the story]
[line 2]
[Situation, 2 lines]
[Tension, 3-4 lines, white space]
[Turning point, 1 line]
[Outcome, 2 lines]
[Lesson, 2 lines, portable to the reader]
[CTA : "Has this happened to you ?" or similar]
WHAT I MINED
- Tension : [what makes this story interesting]
- Turning point : [the pivotal moment]
- Lesson : [the takeaway for the audience]
Rules
- No story without tension. If the user dumped a flat day, push back and ask for the friction.
- Real names, real numbers, real dates. Specifics earn trust.
- Never moralize. The reader extracts the lesson, you only set it up.
- Cut the chronology. Stories on LinkedIn jump : start at the punch, then back-fill.
- If the lesson is "be yourself" or "never give up", reject it. Find a sharper one.
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 · 97 lines · 102 tokens per session scan B b3c0465263fe
linkedin-story-extractor is a skill published in the GitHub repository TaplioOfficial/taplio-linkedin-claude-skills (5 stars, last pushed yesterday), licensed MIT. It adds 102 tokens to every session and 1,320 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it B with 1 finding (strips warnings and disclaimers). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
linkedin-humanizer
Remove the AI tells human readers and LinkedIn's AI-slop filter react to in a post or comment: 2026 vocabulary by paragraph density, reveal bridges, staccato fragments, stacked triads, performed sincerity. Tiered rewriter (forensic / strict / aesthetic / all) plus --mode audit pass-fail review and --mode profile voice…
linkedin-marketing
Plan, draft, audit, and publish LinkedIn posts and comments. Use when the user wants to write a viral LinkedIn post, draft a comment or reply on any LinkedIn post URL, audit a draft against 2026 algorithm heuristics, remove AI tells, extract hook formulas from viral posts, or plan a week of content. Powered by the…
linkedin-reply-handler
Draft a reply to a specific existing LinkedIn comment from its URL. Use when the user wants to reply to a comment on any post, or follow up after an author replied to them. Parses the commentUrn, resolves the correct parentComment target (LinkedIn flattens threads to 2 levels), and posts via Publora on approval. Not…
linkedin-post-writer
Draft a new LinkedIn post from scratch using one of 20 2026 hook formulas (anaphora, R.I.P., time-anchor, curiosity-gap, contrarian, controlled A/B, false-binary, and more) plus a founders-edition angle library, picked by engagement goal (comments, reposts, likes, saves). Runs the humanizer pass and schedules via…
linkedin-comment-drafter
Draft a LinkedIn comment on someone else's post from its URL, or reshare (repost) it to your feed with optional commentary. Use when the user pastes a post URL and asks to comment, engage, be first commenter, or repost with their thoughts. Produces 1-3 variants in the user's voice, picks a reaction, and publishes via…
linkedin-content-planner
Generate a 7-day LinkedIn content plan from a theme, audience, and pillars. Produces per-day post pillar, format, hook type, CTA, posting time, daily comment targets, and a weekly inbound-readiness check. Use when the user wants to plan a week or month of content, not draft a single post.