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/nklofy/code-agent-skills/content-enginenpx skills add nklofy/code-agent-skills --skill content-enginegit clone --depth 1 https://github.com/nklofy/code-agent-skillsWhat 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 | $0.00940 |
| Opus 5 | $0.00028 | $0.00470 |
| Sonnet 5 | $0.00011 | $0.00188 |
| Haiku 4.5 | $0.00006 | $0.00094 |
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 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.
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
98% identical to content-engine — 2 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 — 133 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Content Engine
Build platform-native content without flattening the author's real voice into platform slop.
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, docs, or internal notes into public content
- building a launch sequence or ongoing content system around a product, insight, or narrative
Non-Negotiables
- Start from source material, not generic post formulas.
- Adapt the format for the platform, not the persona.
- One post should carry one actual claim.
- Specificity beats adjectives.
- No engagement bait unless the user explicitly asks for it.
Source-First Workflow
Before drafting, identify the source set:
- published articles
- notes or internal memos
- product demos
- docs or changelogs
- transcripts
- screenshots
- prior posts from the same author
If the user wants a specific voice, build a voice profile from real examples before writing.
Use brand-voice as the canonical workflow when voice consistency matters across more than one output.
Voice Handling
brand-voice is the canonical voice layer.
Run it first when:
- there are multiple downstream outputs
- the user explicitly cares about writing style
- the content is launch, outreach, or reputation-sensitive
Reuse the resulting VOICE PROFILE here instead of rebuilding a second voice model.
If the user wants Affaan / ECC voice specifically, still treat brand-voice as the source of truth and feed it the best live or source-derived material available.
Hard Bans
Delete and rewrite any of these:
- "In today's rapidly evolving landscape"
- "game-changer", "revolutionary", "cutting-edge"
- "here's why this matters" unless it is followed immediately by something concrete
- ending with a LinkedIn-style question just to farm replies
- forced casualness on LinkedIn
- fake engagement padding that was not present in the source material
Platform Adaptation Rules
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 · 133 lines · 55 tokens per session scan A a1ccc0ecfe30
content-engine is a skill published in the GitHub repository nklofy/code-agent-skills (18 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 55 tokens to every session and 940 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 98% identical to content-engine, differing in 2 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.
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…