Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/tpavanipradeep/everything-claude-code/crosspostnpx skills add tpavanipradeep/everything-claude-code --skill crosspostgit clone --depth 1 https://github.com/tpavanipradeep/everything-claude-codeWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00049 | $0.01225 |
| Opus 5 | $0.00024 | $0.00613 |
| Sonnet 5 | $0.00010 | $0.00245 |
| Haiku 4.5 | $0.00005 | $0.00122 |
Grade B, and why
crosspost scanned grade B with 2 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Sends data to an external URLmediumData exfiltration
A POST to an outside endpoint may be telemetry or may be exfiltration; either way the mod talks to somewhere, and you should know where.
resp = requests.post( "https://api.postbridge.io/v1/posts", Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
resp = requests.post( How it starts
The opening of the file, as written. The whole thing — 189 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Crosspost
Distribute content across multiple social platforms with platform-native adaptation.
When to Activate
- User wants to post content to multiple platforms
- Publishing announcements, launches, or updates across social media
- Repurposing a post from one platform to others
- User says "crosspost", "post everywhere", "share on all platforms", or "distribute this"
Core Rules
- Never post identical content cross-platform. Each platform gets a native adaptation.
- Primary platform first. Post to the main platform, then adapt for others.
- Respect platform conventions. Length limits, formatting, link handling all differ.
- One idea per post. If the source content has multiple ideas, split across posts.
- Attribution matters. If crossposting someone else's content, credit the source.
Platform Specifications
| Platform | Max Length | Link Handling | Hashtags | Media |
|---|---|---|---|---|
| X | 280 chars (4000 for Premium) | Counted in length | Minimal (1-2 max) | Images, video, GIFs |
| 3000 chars | Not counted in length | 3-5 relevant | Images, video, docs, carousels | |
| Threads | 500 chars | Separate link attachment | None typical | Images, video |
| Bluesky | 300 chars | Via facets (rich text) | None (use feeds) | Images |
Workflow
Step 1: Create Source Content
Start with the core idea. Use content-engine skill for high-quality drafts:
- Identify the single core message
- Determine the primary platform (where the audience is biggest)
- Draft the primary platform version first
Step 2: Identify Target Platforms
Ask the user or determine from context:
- Which platforms to target
- Priority order (primary gets the best version)
- Any platform-specific requirements (e.g., LinkedIn needs professional tone)
Step 3: Adapt Per Platform
For each target platform, transform the content:
X adaptation:
- Open with a hook, not a summary
- Cut to the core insight fast
- Keep links out of main body when possible
- Use thread format for longer content
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 3d ago First seen · 189 lines · 49 tokens per session scan B faaf23586bdc
crosspost is a skill published in the GitHub repository tpavanipradeep/everything-claude-code (104 stars, last pushed 5mo ago), licensed MIT. It adds 49 tokens to every session and 1,225 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it B with 2 findings (sends data to an external url, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…