# Linkedin Content System > Use this skill when the user asks to create LinkedIn posts, promotional posts, thought-leadership posts, newsletter introductions, article intros, or long-form LinkedIn content from source notes, facts, or drafts. - Skill: `kody-w/linkedin-content-system` (Agent Skill, multi-file: 3 files) - Install (CLI): `npx skillmds@latest add kody-w/linkedin-content-system` - Raw SKILL.md: https://api.skillmd.com/api/skills/kody-w/linkedin-content-system/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: kody-w (https://skillmd.com/u/kody-w) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/kody-w/linkedin-content-system --- # LinkedIn Content System Use this skill to create credible LinkedIn content from supplied facts, notes, drafts, articles, or source material. ## Core rules 1. **No invented evidence.** Do not invent metrics, client names, event details, quotes, claims, or results. 2. **No fake authority.** Do not imply the author has done, seen, led, or researched something unless supplied evidence supports it. 3. **Keep the voice human.** Avoid generic inspirational language, over-polished corporate phrasing, and forced hooks. 4. **Fit the format.** Match the output to the requested LinkedIn surface: short post, promotional post, event post, newsletter intro, article intro, carousel copy, or long-form article. 5. **Avoid unnecessary links or citations in the post body unless the user asks for them.** Keep LinkedIn copy clean and paste-ready. ## Inputs to look for - Source article, draft, notes, or bullet points. - Audience and desired action. - Author voice guidance. - Desired length. - Specific announcement, event, launch, model, or point of view. - Whether links, hashtags, mentions, or calls to action should be included. ## Workflow 1. Read the supplied source material. 2. Extract the central point, proof points, and intended reader response. 3. Choose the appropriate LinkedIn format. 4. Draft in a natural style with clear paragraphs. 5. Keep the post grounded in supplied content. 6. Provide variants if requested. ## Output formats ### Short post ```markdown [Post copy] ``` ### Promo post ```markdown [Post copy] [Optional CTA] ``` ### Newsletter or article intro ```markdown # [Title] [Intro copy] ``` ### Multi-variant output ```markdown ## Recommended version [Post] ## Alternative angle [Post] ## Shorter version [Post] ``` ## Style guidance - Prefer concrete observations over broad claims. - Use short paragraphs but avoid a stack of one-line fragments unless the user requests that style. - Avoid manipulative hooks such as "Stop doing X" unless it genuinely fits the user's tone. - Make the first line specific enough to be worth reading. - End with a useful reflection, question, or action rather than a generic CTA. ## References This skill includes supporting reference material. Read the relevant reference file when the task needs additional structure, rubric detail, examples, or checklist support. - `references/linkedin-format-guide.md` - use this when additional structure, examples, or checks are useful for the task. ## Quality checklist Before responding, check: - The content is grounded in supplied facts. - It reads like a credible human post. - It fits the requested LinkedIn surface. - It avoids unnecessary links, references, and invented proof. - The user can paste it directly into LinkedIn. ## Run this — do not improvise This capability's deterministic implementation is a RAPP single-file agent, linked beside this file as `linkedin_content_system_agent.py` and embedded as the fenced Python below (sha256 ae1ea403f54f2ecd…; a byte-exact copy is also vaulted in the capsule comment at the end of this file). On a host with sandbox execution, run the linked file directly — if it is missing, write the fence contents verbatim to `linkedin_content_system_agent.py` first: ```bash python3 linkedin_content_system_agent.py '{"key": "value"}' # arguments as one JSON object echo '{"key": "value"}' | python3 linkedin_content_system_agent.py # or on stdin python3 linkedin_content_system_agent.py --tool # emit the JSON tool contract ``` Treat stdout as a tool result. If it reports missing or unresolved inputs, stop and collect them. If it returns `steps`, execute those steps in order exactly as returned; if it returns `instructions`, follow them with the supplied inputs. Otherwise use the result verbatim. Do not invent behavior beyond that output. On a host without code execution, treat the Parameters schema and the code below as the exact specification and never paraphrase a step. Never edit inside the generated markers; a converter-equipped host can instead restore the original file checksum-verified with the installed `rapp-agent-converter/scripts/toast.py convert SKILL.md --to agent`. ````python # rapp:deterministic """LinkedinContentSystem -- Use this skill when the user asks to create LinkedIn posts, promotional posts, thought-leadership posts, newsletter introductions, article intros, or long-form LinkedIn content from source notes, facts, or drafts. Generated by the rapp skill from linkedin-content-system. The RCI capsule at the bottom of this file carries the full original; `toast.py convert` restores it byte-exact.""" import json import re import sys try: from agents.basic_agent import BasicAgent except ImportError: # running OUTSIDE a brainstem -- stay executable anyway. class BasicAgent: # noqa: D101 - minimal stand-in, same contract def __init__(self, name=None, metadata=None): if name: self.name = name if metadata: self.metadata = metadata def perform(self, **kwargs): return "Not implemented." def system_context(self): return None def to_tool(self): return {"type": "function", "function": { "name": self.name, "description": self.metadata.get("description", ""), "parameters": self.metadata.get("parameters", {})}} # The procedural layer, verbatim from the source capability. INSTRUCTIONS = '# LinkedIn Content System\n\nUse this skill to create credible LinkedIn content from supplied facts, notes, drafts, articles, or source material.\n\n## Core rules\n\n1. **No invented evidence.** Do not invent metrics, client names, event details, quotes, claims, or results.\n2. **No fake authority.** Do not imply the author has done, seen, led, or researched something unless supplied evidence supports it.\n3. **Keep the voice human.** Avoid generic inspirational language, over-polished corporate phrasing, and forced hooks.\n4. **Fit the format.** Match the output to the requested LinkedIn surface: short post, promotional post, event post, newsletter intro, article intro, carousel copy, or long-form article.\n5. **Avoid unnecessary links or citations in the post body unless the user asks for them.** Keep LinkedIn copy clean and paste-ready.\n\n## Inputs to look for\n\n- Source article, draft, notes, or bullet points.\n- Audience and desired action.\n- Author voice guidance.\n- Desired length.\n- Specific announcement, event, launch, model, or point of view.\n- Whether links, hashtags, mentions, or calls to action should be included.\n\n## Workflow\n\n1. Read the supplied source material.\n2. Extract the central point, proof points, and intended reader response.\n3. Choose the appropriate LinkedIn format.\n4. Draft in a natural style with clear paragraphs.\n5. Keep the post grounded in supplied content.\n6. Provide variants if requested.\n\n## Output formats\n\n### Short post\n\n```markdown\n[Post copy]\n```\n\n### Promo post\n\n```markdown\n[Post copy]\n\n[Optional CTA]\n```\n\n### Newsletter or article intro\n\n```markdown\n# [Title]\n\n[Intro copy]\n```\n\n### Multi-variant output\n\n```markdown\n## Recommended version\n\n[Post]\n\n## Alternative angle\n\n[Post]\n\n## Shorter version\n\n[Post]\n```\n\n## Style guidance\n\n- Prefer concrete observations over broad claims.\n- Use short paragraphs but avoid a stack of one-line fragments unless the user requests that style.\n- Avoid manipulative hooks such as "Stop doing X" unless it genuinely fits the user's tone.\n- Make the first line specific enough to be worth reading.\n- End with a useful reflection, question, or action rather than a generic CTA.\n\n\n## References\n\nThis skill includes supporting reference material. Read the relevant reference file when the task needs additional structure, rubric detail, examples, or checklist support.\n\n- `references/linkedin-format-guide.md` - use this when additional structure, examples, or checks are useful for the task.\n\n## Quality checklist\n\nBefore responding, check:\n\n- The content is grounded in supplied facts.\n- It reads like a credible human post.\n- It fits the requested LinkedIn surface.\n- It avoids unnecessary links, references, and invented proof.\n- The user can paste it directly into LinkedIn.' # Ordered commands lifted verbatim from the capability's own documentation. STEPS = [] class LinkedinContentSystemAgent(BasicAgent): def __init__(self): self.name = 'LinkedinContentSystem' self.metadata = { "name": "LinkedinContentSystem", "description": "Use this skill when the user asks to create LinkedIn posts, promotional posts, thought-leadership posts, newsletter introductions, article intros, or long-form LinkedIn content from source notes, facts, or drafts.", "parameters": { "type": "object", "properties": {}, "required": [] } } super().__init__(name=self.name, metadata=self.metadata) def perform(self, **kwargs): # toaster:generated-perform return json.dumps({"status": "ok", "instructions": INSTRUCTIONS, "inputs": kwargs, "note": "Prose-only capability: follow INSTRUCTIONS " "with the given inputs."}, indent=2) if __name__ == "__main__": # echo '{"arg": "value"}' | python3 linkedin_content_system_agent.py # python3 linkedin_content_system_agent.py '{"arg": "value"}' # python3 linkedin_content_system_agent.py --tool # emit the JSON tool contract _a = sys.argv[1:] if _a and _a[0] == "--tool": print(json.dumps(LinkedinContentSystemAgent().to_tool(), indent=2)) else: _raw = _a[0] if _a else (sys.stdin.read().strip() or "{}") print(LinkedinContentSystemAgent().perform(**json.loads(_raw))) # rci-capsule:v1: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 ````