1474948ef8
Replace manual YAML editing instructions with deterministic state-updater.mjs calls in 8 content commands (post, quick, react, pipeline, first-post, video, publish, carousel) and 2 hook prompts (state-update-reminder, post-creation-automation). Batch.md gets note that state updates happen at publish time. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
85 lines
4 KiB
Markdown
85 lines
4 KiB
Markdown
Before ending this LinkedIn content session, do two things:
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**1. Update State File**
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If a post was created or finalized in this session, use the state-updater script:
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```bash
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node --input-type=module -e "
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import { writeState, updatePostTracking } from '${CLAUDE_PLUGIN_ROOT}/hooks/scripts/state-updater.mjs';
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writeState(content => updatePostTracking(content, {
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postDate: 'YYYY-MM-DD',
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postTopic: 'topic_area',
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hookText: 'First 60 chars of hook...',
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charCount: NNNN,
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format: 'post'
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}));
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"
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```
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Replace the placeholder values with actual post data from this session.
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If the user mentioned or updated their follower count during this session:
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```bash
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node --input-type=module -e "
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import { writeState, updateFollowerCount } from '${CLAUDE_PLUGIN_ROOT}/hooks/scripts/state-updater.mjs';
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writeState(content => updateFollowerCount(content, {
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count: NNNN,
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month: 'YYYY-MM'
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}));
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"
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```
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- Clear `next_planned_topic` if it was used, or set it to the next suggested topic
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- If analytics data was imported in this session, set `last_import_date` to today (YYYY-MM-DD) and `last_import_week` to current ISO week (YYYY-WXX)
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**2. Pre-Publish Reminders** (only if a post was created)
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- **Quality Check**: Has content been reviewed against quality scorecard? Hook 110-140 chars, 1,200-1,800 chars total, authentic tone, no external links.
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- **5x5x5 Engagement**: Before posting, complete 15-20 min pre-posting engagement — 5 people with overlapping audiences, find their recent posts, write 5 thoughtful comments (15+ words each).
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- **First-Hour Plan**: Respond within 5 minutes to first comments. Add value in responses. Target 15+ engagements in first hour.
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- **Posting Time**: Post when target audience is most active.
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**3. Queue Status Check**
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If posts were added to the queue during this session (`assets/drafts/queue.json` was modified):
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- Confirm how many posts were queued and their scheduled dates
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- Remind: "View your full schedule with /linkedin:calendar"
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If a scheduled post was published during this session:
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- Verify it was marked as published in queue.json (status = "published")
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- If not, remind: "Run /linkedin:publish to update the queue status"
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Provide reminders naturally based on what was done in the session. If no LinkedIn content was created, skip the reminders and just ensure state is consistent.
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**4. Voice Sample Collection** (if a post was created)
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If a LinkedIn post was created or finalized in this session, save the full post text as a voice sample:
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- Read the full post text from the draft that was just created
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- Check if `assets/voice-samples/authentic-voice-samples.md` exists
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- Append the full post to the `## Collected Post Samples` section:
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```
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### [YYYY-MM-DD] — [post type] ([char count] chars)
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[Full post text exactly as written]
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```
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- **Ask the user for confirmation** before writing: "I'll save this post as a voice sample for drift detection. OK?"
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- This builds the voice sample library that enables automatic drift scoring (needs 5+ samples for reliable scoring)
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- The more samples collected, the more accurate the voice-trainer's drift detection becomes
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**5. Content History Log** (if a post was created)
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If a LinkedIn post was created or finalized, append an entry to the content history log:
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- If `assets/analytics/content-history.md` does not exist, initialize it from `config/content-history.template.md`
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- Append a new row to the "## Content Log" table:
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```
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| YYYY-MM-DD | "Hook text..." | topic_area | format | word_count | char_count | source |
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```
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Where:
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- `date`: Today's date
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- `hook`: First 60 characters of the hook line
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- `topic`: Matching expertise_area value (for pillar tracking)
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- `format`: post/quick/react/video/pipeline
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- `word_count`: Word count of the full post
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- `char_count`: Character count of the full post
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- `source`: original/url/curated (where the idea came from)
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- This is append-only — never edit or delete existing entries
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- This log enables `/linkedin:report` and `analytics-interpreter` to track content production over time without requiring LinkedIn CSV imports
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