---
name: daily-post
description: Create today's best evidence-backed professional post after live research, topic scoring, originality checks, and format selection. Use on /CSpost, "what should I post today?", "good morning where is my post?", or equivalent requests.
---

# Daily Post Factory

The user should not choose the topic, category, audience, or format unless they want to.

## 0. Load context

Read durable CareerSignal context and recent history.

If profile is uninitialized, hand off to onboarding and then resume.

## 1. Research broadly inside the user's professional universe

Use live web/search capability when available.

Prefer:
1. first-party official documentation, release notes, repos, standards, papers;
2. primary research;
3. reputable technical sources;
4. community sources for pain points, not as sole authority for factual claims.

Search for:
- recent changes/releases;
- recurring implementation failures;
- API/tool changes;
- practitioner pain points;
- research worth translating;
- misunderstood concepts;
- debugging patterns;
- security/governance problems;
- useful evergreen problems when news is weak.

Do not collect generic news.

## 2. Generate candidates

Create 5–10 internal candidates across different domains.

Domain examples:
- agentic systems
- LLMs
- RAG
- evals
- MCP/tool use
- Python
- ML/deep learning
- computer vision
- data engineering
- SQL/data quality
- MLOps/deployment
- APIs/automation
- AI safety
- open-source AI
- research papers
- cloud/GPU
- debugging
- BI/analytics
- research data
- the user's own evidence/project domains

Use `references/domain-library.md`.

## 3. Turn each candidate into a problem

For each candidate identify:
- what changed or what commonly fails;
- who experiences it;
- why it matters;
- the practical fix/decision;
- what the reader can reuse.

Reject candidates that are only announcements.

## 4. Score

Use `references/scoring.md`.

Important dimensions:
- timeliness;
- problem importance;
- educational value;
- actionability;
- evidence strength;
- match with user's knowledge boundary;
- career/professional signal;
- novelty;
- memorability;
- connection quality potential.

## 5. Originality and feed diversity

Read recent topic/category/domain/format memory.

Check:
- same topic;
- same core lesson;
- same hook pattern;
- same format;
- same audience;
- same visual;
- same analogy/joke;
- same emotional tone.

Use script when available:
`python3 engine/originality.py ...`

Do not repeat polls or humor back-to-back.
Do not let one domain dominate the feed.

## 6. Choose category independently from domain

Use `references/category-library.md`.

The best topic determines the category, not a rigid calendar.

## 7. Choose teaching structure

For advanced learning content prefer:

**problem → core idea → how it works → practical example → common mistake → when to use → one rule to remember**

Not every post needs every section. Keep one main idea.

## 8. Build-before-post option

If the best content would be stronger with original evidence, return BUILD instead of weak commentary.

A BUILD action can be:
- 5–30 minute code experiment;
- small benchmark;
- tiny data analysis;
- quick architecture prototype;
- reproducible comparison.

When code execution is available, run it.
Then use the result as original evidence and continue to the post.

## 9. Defensibility + counterargument check

Before writing:
- What would a knowledgeable peer challenge?
- What limitation matters?
- When is the recommendation wrong?
- Can the user defend the post?

Soften or remove claims that fail this check.

## 10. Choose format

Possible outputs:
- text;
- poll;
- carousel/document;
- generated teaching image;
- annotated real screenshot;
- code + explanation;
- architecture diagram;
- decision tree;
- chart;
- short situational humor;
- mini-guide.

Prefer a real evidence visual over decorative generation.
If image generation exists and an original image teaches better, generate it.
If not, produce a precise visual brief.

## 11. Draft + quality pipeline

Run:
1. evidence/claim check;
2. source freshness check;
3. privacy scan;
4. humanizer;
5. technical/factual audit;
6. hype/AI-tell audit;
7. originality check;
8. format-specific validation;
9. final voice pass.

When executable code is used in a post, run/validate it when possible.

## 12. Save history

If local state is available, save the draft metadata before returning it:
- create a post JSON compatible with `schemas/post.schema.json`;
- run `python3 skills/user-context/scripts/context_cli.py record-post --file <post.json>`.

Do not mark as published until the user says it was published. When they do, use `mark-published` and include the LinkedIn URL if they provide it.

## 13. User-visible response

By default return only:
- final post;
- and the selected asset/poll/carousel/code if needed.

Do not expose candidate rankings, chain-of-thought, audit tables, or research notes unless asked.

If decision is BUILD / ENGAGE / SKIP, say so briefly with one useful action.


## CareerSignal shortcut modes

All commands remain intent aliases and may also be expressed naturally.

- `/CSpost` — run the complete daily-post workflow.
- `/CSideas` — return 5–10 strong ranked ideas only; do not draft the post.
- `/CSseries` — create a progressive multi-post learning series from one topic without repeating the same lesson.
- `/CSvisual` — create or brief the strongest visual/carousel/diagram for an existing or selected post idea.
- `/CSpoll` — create a useful practitioner poll with 2–4 distinct options and a follow-up learning angle.
- `/CSreply` — draft a substantive reply/comment grounded in the supplied post/comment and the user's real evidence.
- `/CSrepost` — take an older user post and find a materially new angle; do not paraphrase the same lesson.

These modes still use evidence, privacy, voice, originality, and source checks.