Cc content linkedin post

@./format-guidelines.md Read when: starting this skill @../_shared/storytelling-frameworks.md Read when: selecting a narrative framework in Step 3b @../_shared/persuasion-principles.md Read when: selecting persuasion principles in Step 3c

LinkedIn Post Skill

You are helping the owner produce a complete, publishable LinkedIn post. The post must comply with the format guidelines in this skill folder, reflect the company's brand voice, and — if a campaign briefing is present — serve the briefing's goals.

This skill is language-, industry-, and audience-neutral. It works for any output language and any B2B or B2C context. Calibration happens through the loaded context files and the owner's topic input.

Step 0: Recall learnings

If .claude/learnings.md exists, read it silently. Apply all entries relevant to this run — both [cc-content:*]-tagged entries and entries from other plugins that inform content quality or project constraints. Do not announce this step. If the file is absent, continue normally.

Step 1: Load context

Read the context table from all loaded CLAUDE.md files:

grep -A 200 '## Context files' CLAUDE.md 2>/dev/null || echo "(no context table)"

CLAUDE.md files may exist at multiple hierarchy levels (workspace root, project root, sub-directory). The harness already loads all applicable ones into your context window. If multiple ## Context files tables exist, rows from more specific CLAUDE.md files take precedence over less specific ones.

If no context table is found in any loaded CLAUDE.md, ask once:

"I don't see any context files registered. Would you like to: (a) Pause and run /cc-content-onboarding to set up context (b) Continue without project context (output will be generic)"

Stop if (a); note "generating without project context" and continue if (b).

If a context table exists, read every file listed in the File column.

After loading, assess what each file covers by reading its Summary entry. Map the loaded files to these content needs:

Need	What to look for in the Summary
Brand voice	Writing style, tone, vocabulary, phrasing rules, things to avoid
Organization background	Who the company/author is, products, positioning, mission
Target audience	Reader personas, goals, challenges, job titles
Output language	Default language, locale, or region
LinkedIn-specific rules	Hashtag policies, disclaimers, link policies, CTA constraints

When multiple files plausibly cover the same need, pick the one whose Summary best fits this specific post. For example: if one file's Summary says "casual, inclusive — job ads and employer branding" and another says "formal — corporate communications", and this post is a recruiting post, load the casual one and note the choice.

Coverage gaps — flag these two:

If no loaded file plausibly covers brand voice, ask once:

"I don't see any writing style or brand voice context. Is this intentional, or should I pause while you run /cc-content-onboarding?"

• Intentional: note the gap; label the final output ⚠ DEGRADED OUTPUT — no brand voice context
• Pause: direct the owner to onboarding and stop.

Apply the same ask for organization background if no loaded file covers it.

For absent audience, language, or LinkedIn-specific rules: note silently and continue.

After loading and assessing, note which files were loaded and which need each covers. Proceed to Step 2.

Step 2: Check for campaign briefing

Determine the briefing path:

1. If the owner passed a file path as an argument ($ARGUMENTS), use that path.
2. Otherwise, check for brief.md in the current working directory:

ls brief.md 2>/dev/null && echo "found" || echo "missing"

• Found (either via argument or brief.md): read the file and note its key messages, goals, and constraints. Confirm: "✓ Campaign briefing loaded from <path>."
• Missing: note "No campaign briefing found — generating from company context only." and continue.

Step 3: Ask for the post topic (if not already provided)

If the owner has not specified a topic or goal for the post, ask:

"What should this post be about? You can describe the topic, share a key message you want to convey, or paste raw notes and I'll turn them into a post."

Wait for the answer, then proceed.

Step 3b: Select a storytelling framework

Read ../_shared/storytelling-frameworks.md and follow the selection process described there. Apply the chosen framework as the structural spine of the post.

Step 3c: Select persuasion principles

Read ../_shared/persuasion-principles.md and follow its selection process. Pick 1–3 principles that fit the post's goal and the reader's state, plus a pre-suasive opener strategy. Note the choice in working notes (e.g., "Using Authority + Social Proof, opener primes credibility").

Step 4: Generate the post

Produce a complete LinkedIn post that:

• Opens with a strong hook (max 190 chars, starts with an emoji)
• Is structured according to the framework chosen in Step 3b (if applicable)
• Includes a body delivering the substance the hook promises
• Ends with a specific, singular CTA that is easy to answer
• Uses ~6 emojis placed at natural pauses (omit if brand voice prohibits)
• Omits hashtags by default (add 1–2 only if the campaign requires them)
• Stays within the recommended character target (1,242–2,500 characters)
• Reflects tone and vocabulary from loaded brand voice context
• Addresses the audience from loaded audience context
• Does NOT embed a link in the post body (reference "first comment" if a link is needed)

Internally verify against the quality checklist in format-guidelines.md before presenting the output.

If the briefing is present, the post must serve its stated goals and key messages.

Present the post in a clear block:

─────────────────────────────────────────────
LinkedIn post draft
─────────────────────────────────────────────
<post content>
─────────────────────────────────────────────
Character count: <N> / 3,000

If the output is degraded (brand voice or organization context missing), prepend:

⚠ DEGRADED OUTPUT — generated without: <list of missing context>

Step 5: Feedback

Auto-store phase. Before asking for feedback, review this run. For each qualifying observation, append one tagged line to .claude/learnings.md (create with standard header if missing):

[cc-content:cc-content-linkedin-post] <concise observation> — <YYYY-MM-DD>

Qualifies: content preferences or constraints not already in any loaded context/ file or CLAUDE.md; corrections the owner made to the output; project-specific facts that would change future output; accepted/rejected suggestions deviating from best practices.

Does not qualify: standard behavior applied without deviation; facts already in context files or CLAUDE.md; anything derivable by re-reading context files; facts semantically equivalent to an existing .claude/learnings.md entry under any plugin tag — when in doubt, skip; redundancy is worse than a missed entry.

Check for the file before appending:

ls .claude/learnings.md 2>/dev/null && echo "exists" || echo "missing"

Standard header when creating the file:

# Learnings

Corrections and feedback collected during content sessions.
Entries are tagged by skill and dated.

---

Explicit feedback. After the auto-store phase, ask:

"Did this post meet expectations? If you have any corrections or notes for future posts, share them here — or press Enter to finish."

• If the owner provides a correction: append it as a tagged entry using the same format and qualification criteria above. Confirm total entries written across both phases: "✓ N learning(s) saved to .claude/learnings.md."
• If the owner confirms quality or skips: if any entries were auto-stored, confirm "✓ N learning(s) auto-saved to .claude/learnings.md." Then say "Great — the post is ready to publish. Copy it above and paste directly into LinkedIn." and exit. If nothing was stored, skip the confirmation and exit directly.