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 agents/raja21068/autoresearch/content-engine-skillgit clone --depth 1 https://github.com/raja21068/AutoResearchWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/raja21068/autoresearch/content-engine-skill)<a href="https://agentmods.dev/agents/raja21068/autoresearch/content-engine-skill"><img src="https://agentmods.dev/badge/agents/raja21068/autoresearch/content-engine-skill.svg" alt="Measured on agentmods" height="20"></a>What 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 4d 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.
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 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.
- 4d ago First seen · 89 lines · 55 tokens per session scan A 057a610eab12
content-engine is an agent published in the GitHub repository raja21068/AutoResearch (2 stars, last pushed 3mo 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. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other agents, from other repositories
build-agent-empty-input-diagnosis
Status: Resolved for interview injections. Date: 2026-08-10 Related PR: #818 (fix/preset-tui-slash-command) — same root class as the original /preset fix. Suspected sibling bug reported by user: During superpowers / brainstorm skill conversations, when the orchestrator asks for confirmation or work is interrupted…
codemap
Defines agent personalities (Orchestrator, Explorer, Librarian, etc.) and manages their configuration lifecycle. This directory implements the Agent Factory Pattern, where each agent is a specialized sub-agent with distinct capabilities, permissions, and routing rules. The Orchestrator agent (src/agents/index.ts)…
domain
How the engineering skills should consume this repo's domain documentation when exploring the codebase.
multimodel-orchestrator
When multiple AI tools work on the same codebase, they need.
planner
Agent "planner" from rizvee/multimodel-dev-os, covering planner agent spec, focus areas and primary tooling.
devops
Agent "devops" from rizvee/multimodel-dev-os, covering devops agent spec and focus areas.