Best for
- Use when the user wants to distribute content across social platforms.
affaan-m/ECC/skills/crosspost/SKILL.md
Multi-platform content distribution across X, LinkedIn, Threads, and Bluesky. Adapts content per platform using content-engine patterns. Never posts identical content cross-platform. Use when the user wants to distribute content across social platforms.
Decision brief
Distribute content across platforms without turning it into the same fake post in four costumes.
Compatibility matrix
| Platform | Status | Evidence | What to check |
|---|---|---|---|
| Codex | Not declared | No explicit evidence | Portability before use |
| Claude Code | Not declared | No explicit evidence | Portability before use |
| Cursor | Not declared | No explicit evidence | Portability before use |
| Gemini CLI | Not declared | No explicit evidence | Portability before use |
Installation
The source command is displayed only when detected. A safe inspection prompt is always available so your agent can explain every action before execution.
npx skills add https://github.com/affaan-m/ECC --skill "skills/crosspost"Inspect the Agent Skill "crosspost" from https://github.com/affaan-m/ECC/blob/4e973d3eaf92d97f8d2e2d8abb39d8bdc8711b38/skills/crosspost/SKILL.md at commit 4e973d3eaf92d97f8d2e2d8abb39d8bdc8711b38. List every install step, command, network request, credential, file read/write, external action, and rollback step. Explain whether it fits my task. Do not install or execute anything until I approve.
Workflow
Pick the strongest source version first: - the original X post - the original article - the launch note - the thread - the memo or changelog
Pick the strongest source version first: - the original X post - the original article - the launch note - the thread - the memo or changelog
Run brand-voice first if the source voice is not already captured in the current session.
Review the “Step 3: Adapt by Platform Constraint” section in the pinned source before continuing.
Permission review
No configured static risk pattern was detected
This is not proof of safety. Runtime behavior, indirect dependencies, and hidden external systems are outside the static scan.
Evidence record
| Signal | Value | Evidence type | Meaning |
|---|---|---|---|
| Quality score | 74/100 | Computed | Documentation, specificity, maintenance, and trust rules |
| Repository stars | 234,327 | Source | Repository attention, not individual Skill quality |
| Compatibility | 0 platforms | Source | Declared in the catalog source record |
| Usage guide | automated source guide | Editorial | Generated or reviewed according to the visible evidence level |
Pinned source
Distribute content across platforms without turning it into the same fake post in four costumes.
Pick the strongest source version first:
Use content-engine first if the source still needs voice shaping.
Run brand-voice first if the source voice is not already captured in the current session.
Reuse the resulting VOICE PROFILE directly.
Do not build a second ad hoc voice checklist here unless the user explicitly wants a fresh override for this campaign.
Default:
Do not add cross-platform references unless useful. Most of the time, the post should stand on its own.
Delete and rewrite any of these:
Return:
Before delivering:
brand-voice for reusable source-derived voice capturecontent-engine for voice capture and source shapingx-api for X publishing workflowsAlternatives
affaan-m/ECC
Use it for engineering tasks; the detail page covers purpose, installation, and practical steps.
affaan-m/ECC
Review crosspost's use cases, installation, workflow, and original source instructions.
event4u-app/agent-config
Use when the user says "review the design", "check the UI", or wants a comprehensive UI/UX review. Uses a 7-phase methodology covering interaction, responsiveness, accessibility, and more.
K-Dense-AI/scientific-agent-skills
Distributed computing for larger-than-RAM pandas/NumPy workflows. Use when you need to scale existing pandas/NumPy code beyond memory or across clusters. Best for parallel file processing, distributed ML, integration with existing pandas code. For out-of-core analytics on single machine use vaex; for in-memory speed use polars.