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/infopibe/everything-claude-code/content-enginenpx skills add Infopibe/everything-claude-code --skill content-enginegit clone --depth 1 https://github.com/Infopibe/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 | $0.00055 | $0.00577 |
| Opus 5 | $0.00028 | $0.00289 |
| Sonnet 5 | $0.00011 | $0.00115 |
| Haiku 4.5 | $0.00006 | $0.00058 |
Grade A, and why
content-engine scanned grade A with 0 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 2d 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to content-engine — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 89 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Content Engine
Turn one idea into strong, platform-native content instead of posting the same thing everywhere.
When to Activate
- writing X posts or threads
- drafting LinkedIn posts or launch updates
- scripting short-form video or YouTube explainers
- repurposing articles, podcasts, demos, or docs into social content
- building a lightweight content plan around a launch, milestone, or theme
First Questions
Clarify:
- source asset: what are we adapting from
- audience: builders, investors, customers, operators, or general audience
- platform: X, LinkedIn, TikTok, YouTube, newsletter, or multi-platform
- goal: awareness, conversion, recruiting, authority, launch support, or engagement
Core Rules
- Adapt for the platform. Do not cross-post the same copy.
- Hooks matter more than summaries.
- Every post should carry one clear idea.
- Use specifics over slogans.
- Keep the ask small and clear.
Platform Guidance
X
- open fast
- one idea per post or per tweet in a thread
- keep links out of the main body unless necessary
- avoid hashtag spam
- strong first line
- short paragraphs
- more explicit framing around lessons, results, and takeaways
TikTok / Short Video
- first 3 seconds must interrupt attention
- script around visuals, not just narration
- one demo, one claim, one CTA
YouTube
- show the result early
- structure by chapter
- refresh the visual every 20-30 seconds
Newsletter
- deliver one clear lens, not a bundle of unrelated items
- make section titles skimmable
- keep the opening paragraph doing real work
Repurposing Flow
Default cascade:
- anchor asset: article, video, demo, memo, or launch doc
- extract 3-7 atomic ideas
- write platform-native variants
- trim repetition across outputs
- align CTAs with platform intent
Deliverables
When asked for a campaign, return:
- the core angle
- platform-specific drafts
- optional posting order
- optional CTA variants
- any missing inputs needed before publishing
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.
- 2d ago First seen · 89 lines · 55 tokens per session scan A 057a610eab12
content-engine is a skill published in the GitHub repository Infopibe/everything-claude-code (8 stars, last pushed 5mo ago), licensed MIT. It adds 55 tokens to every session and 577 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to content-engine, differing in 0 lines, and is treated as a copy.
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.
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…
agent-host-chat-contributions
Build and review cross-cutting agent-host chat behavior through lifecycle contributions. Use when adding turn lifecycle side effects, prompt or context injection, restored-history transformation, protocol-action observation, or when reviewing changes that add code to AgentSideEffects or AgentService.
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.