content-monitor
Content Monitor
Domain Context
What is this skill? This skill provides expertise for a multi-source intelligence pipeline covering YouTube channels, GitHub trending/topic/search feeds, Reddit subreddits, awesome-lists, Google Search, and RSS/Atom feeds (including Hacker News, changelogs, blogs, arXiv, and podcasts). Covers source adapter patterns, unified evaluation pipeline, deduplication, scheduling cadence, and actionable intelligence brief generation. Use when adding new content sources to the pipeline, configuring monitoring schedules, understanding the discover/transcribe/summarize/evaluate phases, extending or debugging the discovery pipeline, or deciding which model to use at each pipeline stage. Do NOT use for SEO keyword research — use the keywords skill. Do NOT use for competitive product analysis — use user-research-synthesis.
Key Files
| File | Purpose |
|---|---|
scripts/content-monitor/sources.json |
Multi-source configuration for the live monitor pipeline. |
scripts/content-monitor/channels.json |
Legacy YouTube-channel configuration still referenced by the pipeline. |
.content-monitor/seen-items.json |
Persistent deduplication state. |
.content-monitor/backlog-evaluate-status.json |
Tracks historical scoring progress for batch backlog evaluation sweeps. |
.content-monitor/resume-status.json |
Catch-up sweep progress used by resume flows. |
Workflow
Use the ordered phases, checklists, and guardrails in the sections below as the canonical workflow for this skill. When multiple subsections describe steps, follow them in the order presented.
Coverage
More from jacob-balslev/skills
layout-composition
Use when deciding responsive page or screen structure: section order, scan pattern, grid/flex composition, breakpoints, viewport hierarchy, responsive media, and density. Do NOT use for user-goal decomposition (use `task-analysis`), navigation taxonomy (use `information-architecture`), visual polish (use `visual-design-foundations`), or component/token contracts (use `design-system-architecture`).
8context-graph
Use when designing or auditing the multi-graph context architecture of an AI-coding workspace: skill graph, document routing graph, memory index, script registry, and the cross-graph edges between them. Covers edge typing, orphan detection, connectivity health, deterministic graph synthesis signals, change-propagation checks, and drift or hub-and-spoke anti-patterns. Do NOT use for authoring one SKILL.md (use `skill-scaffold`), validating one skill (use `graph-audit`), live routing decisions (use `skill-router`), context-window budgeting (use `context-window`), or session load/drop choices (use `context-management`).
8visual-design-foundations
Use when designing or auditing visual craft: color palette, typography, spacing, elevation, rhythm, density, visual hierarchy, brand fit, contrast intent, and motion feel. Do NOT use for sign-system meaning (use `semiotics`), token/component architecture (use `design-system-architecture`), responsive structure (use `layout-composition`), or accessibility compliance (use `a11y`).
7project-knowledge-extraction
Use when extracting durable project knowledge from code, docs, issues, incidents, reports, screenshots, or conversations into reusable context such as skills, ADRs, glossaries, context docs, or memory. Do NOT use for writing a new skill contract (use `skill-scaffold`), maintaining library tooling (use `skill-infrastructure`), or generic documentation polish (use `documentation`).
6problem-framing
Use when a team is converging on solutions before agreeing on the problem, when a brief reads as a feature request, when symptoms and root needs are tangled, or when assumptions need surfacing before design work proceeds. Do NOT use for code-level bug triage, runtime failure diagnosis, or root-cause analysis of system errors — those are engineering investigation tasks, not design problem framing.
6ai-native-development
Use when reasoning about agent autonomy levels, designing auto-improve loops, evaluating AI-generated code quality, or measuring agent productivity in an LLM-assisted codebase. Covers Karpathy's three eras of software (1.0 explicit / 2.0 learned / 3.0 natural-language), the vibe-coding-vs-agentic-engineering distinction, the 0–5 autonomy slider with task-type recommendations, the one-asset / one-metric / one-time-box AutoResearch loop, Software 3.0 productivity metrics, and the documented quality regressions of ungated AI-generated code (the 'vibe hangover'). Do NOT use for choosing a specific autonomy-loop topology (use `agent-engineering`), for the per-prompt authoring discipline (use `prompt-craft`), or for reviewing the AI-generated code that comes out of a Software 3.0 workflow (use `code-review`).
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