Skill: ksrinivas2304/linkedin-post-generator-nivas (version 1.0.0)

--- name: linkedin-post-generator description: Generate high-quality LinkedIn posts locally from a short prompt, topic, or outline. Use when the user asks to draft, rewrite, or improve a LinkedIn post, headline, or caption, including adding hooks, CTAs, or tailoring tone and length. --- # LinkedIn Post Generator This skill helps generate and refine LinkedIn posts **locally on this machine**, without calling the LinkedIn API. The user will copy-paste the final post into LinkedIn manually. ## When to use this skill Use this skill whenever the user asks for help with: - Writing a new LinkedIn post from a topic, idea, or outline - Rewriting or improving an existing LinkedIn post - Adjusting tone (professional, casual, storytelling, technical, etc.) - Changing length (short, medium, long / thread-style) - Adding hooks, CTAs, or hashtags suitable for LinkedIn Examples of triggering requests: - "Draft a LinkedIn post about my new project in AI" - "Rewrite this LinkedIn post to be more engaging" - "Make this post more professional and concise for LinkedIn" - "Give me 3 hook options for this LinkedIn update" ## Srinivas-specific optimization When the user is **Kusumanchi Srinivas** (headline mentions SRKR CSE ’25 / Associate ML Engineer @Yanthraa / Research Associate @Li2 Edu): - Optimize posts for: - Growing **reach, followers, likes, comments, and impressions** on a professional audience. - Attracting **engineers, founders, recruiters, tech leaders, and ambitious students** across software and AI. - Emphasize themes that fit his profile and past posts, without locking into a tiny niche: - Software engineering and backend/system design. - AI/ML and data-driven products. - Cloud and modern infrastructure when relevant. - Career growth, mindset, and lessons from internships, research, and real projects. - Tech leadership, teamwork, and how to think about building products. - Avoid: - Over-focusing on old “B.Tech student” identity (keep it light if used at all). - Internal project names, sprint labels, file names, or code snippets unless explicitly requested. ## Inputs to collect When the user asks for a LinkedIn post, try to clarify these (only ask follow-ups if not obvious): 1. **Goal** of the post (choose or infer): - announce (launch, promotion, new role, milestone) - share learning (lesson, story, failure, insight) - ask (help, feedback, hiring, referrals) - promote (product, service, content) 2. **Audience** (e.g. recruiters, engineers, designers, founders, managers, students). 3. **Tone** (default: "professional but friendly"): - options: professional, friendly, casual, storytelling, technical, inspirational. 4. **Length**: - `short` (1–3 paragraphs) - `medium` (3–6 paragraphs) - `long` (story/essay-style) 5. **Language** (default: English unless user text suggests another). 6. **Input content**: - either a topic/outline, or an existing draft to improve. If the user is being very casual ("just write something"), use sensible defaults and do not over-question. ## Workflow Follow this workflow: 1. **Parse the request** - Identify if the user provided: topic only, topic + key points, or a full draft. - Infer goal, audience, tone, and length when obvious. 2. **Clarify if needed** - Ask at most 1–3 short follow-up questions when critical details are missing. - Skip questions if the request is clear enough to produce something useful. 3. **Generate a first draft** - Use the `scripts/generate_post.py` helper when available. - If script output is missing or script is unavailable, generate directly in the model. 4. **Polish for LinkedIn conventions** Ensure the post generally follows these patterns (adapt when user asks otherwise): - Strong first line (hook) that makes people stop scrolling. - Short paragraphs (1–3 lines) and some line breaks for readability. - Clear structure: hook → context/story → insight/value → CTA (optional). - Avoid heavy emoji spam; 0–3 emojis max unless user wants more. - Optional light hashtags at the end (2–6), relevant and non-spammy. 5. **Offer variants when helpful** - By default, provide **1 main post**. - Optionally add: - 2–3 alternative hooks, or - a shorter / more concise variant, when that seems useful or the user asks for "options". 6. **Respect user constraints** - If the user gives a word/character limit, target within ~10–15%. - Keep or adapt any mandatory phrases, links, or hashtags they specify. ## Helper script: scripts/generate_post.py If this repository includes `scripts/generate_post.py`, prefer calling it for deterministic formatting. Expected behavior (conceptual): - Input (arguments or stdin JSON): - topic / prompt - optional: audience, tone, length, language, and any raw draft text - Output: a single LinkedIn-ready post on stdout (no extra explanations). If the script is missing or fails, fall back to generating the post directly in this agent. ## Style guidelines When generating LinkedIn posts, always optimize for **reach and engagement on a professional audience**, not internal team context. - **Audience-first**: Assume the reader does *not* know the project, sprint names, or internal file names. Explain in plain language. - **Voice**: Clear, confident, and human. Avoid corporate buzzword soup. - **Jargon**: Use domain terms only when audience would understand them. - **Specifics over vagueness**: Prefer concrete, relatable examples and outcomes ("faster app, smoother experience") over internal details ("Sprint 2, PHASE3_RUNTIME_INTERACTION.md"). - **Brevity**: Say what matters in as few words as needed; avoid filler. - **Authenticity**: When user shares personal story, preserve their voice and details. - **No raw code or private data**: Never paste code, logs, or internal data into the post unless the user explicitly says it’s a code-focused audience. - **Hook for attention**: Prioritize strong first lines that make professionals stop scrolling. ## Safety & compliance - Do **not** fabricate employment history, degrees, or certifications unless explicitly requested as a fictional/sample post. - Avoid discriminatory, harassing, or misleading content. - If the user asks for unethical growth-hacking (spammy DMs, fake testimonials, deceptive claims), gently refuse and suggest ethical alternatives. ## Example usages 1. **New post from topic** > User: "Write a LinkedIn post announcing I joined ACME as a Senior Data Engineer in Bangalore, excited about building real-time pipelines. Keep it professional and a bit warm." Action: - Infer goal: announce new role - Audience: professional network, recruiters, colleagues - Tone: professional but warm - Length: short/medium - Generate a post with a clear hook and a brief CTA (e.g. "If you work in data infra, would love to connect"). 2. **Rewrite a draft** > User: "Make this more engaging for LinkedIn while keeping the main points: [user draft]" Action: - Keep all factual claims. - Improve hook, structure, and flow. - Preserve any required links or hashtags. 3. **Multiple hooks** > User: "Give me 5 hook ideas for a LinkedIn post about switching careers from mechanical engineering to data science." Action: - Produce 5 strong first-line hook options tailored to LinkedIn.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)