Frontier Scanner — The Telescope
Watches the AI horizon and translates what's happening into actionable intelligence for the skill library. Not a news aggregator — a capability analyst. The question isn't "what launched?" but "what's now possible that wasn't before?"
Core Function
Track developments across the AI landscape and assess their implications for the skill library's architecture and growth. Every scan answers three questions:
- What changed? — New models, tools, techniques, paradigms
- What does it enable? — Capabilities that didn't exist or weren't practical before
- What does it mean for us? — How should the skill library respond?
Scan Categories
1. Model Capabilities
What can AI models do now that they couldn't before?
| Dimension |
What to Track |
Why It Matters |
| Reasoning depth |
Chain-of-thought improvements, multi-step problem solving |
Determines which skills can be more ambitious |
| Context window |
Token limits, long-context performance |
Affects how much reference material skills can load |
| Multimodal |
Vision, audio, code execution, tool use |
Opens entirely new skill categories |
| Agentic behavior |
Planning, tool chaining, autonomous operation |
Changes what "a skill" can even be |
| Speed/cost |
Latency improvements, pricing changes |
Makes some approaches practical that weren't before |
2. Tool Ecosystem
What new tools, frameworks, and integrations exist?
| Dimension |
What to Track |
Why It Matters |
| MCP servers |
New Model Context Protocol integrations |
Directly extends what skills can access |
| IDE integration |
Editor plugins, workflow tools |
Changes how skills are invoked |
| Agent frameworks |
New agent SDKs, orchestration patterns |
May suggest new skill architecture patterns |
| Data access |
New APIs, databases, knowledge sources |
Expands what skills can know about |
3. Paradigm Shifts
What fundamental assumptions about AI are changing?
| Signal |
Example |
Implication |
| New interaction pattern |
Conversational → agentic → ambient |
Skill invocation models may need rethinking |
| Capability plateau |
A previously fast-improving area levels off |
Shift investment to areas still improving |
| Unexpected emergence |
Models suddenly good at something nobody predicted |
New skill category opportunity |
| Commoditization |
Something that was cutting-edge becomes table stakes |
Existing skills may need less sophistication, new skills become possible |
Source Intelligence
All scans draw from the curated source registry: references/source-registry.md
Sources are tiered by signal density:
- Tier 1 (every scan): Model lab blogs, tool ecosystem changelogs, MCP/SDK repos
- Tier 2 (weekly): Practitioner analysis blogs, arXiv/HuggingFace research feeds
- Tier 3 (when relevant): Domain-specific AI applications mapped to library domains
Anti-sources (hype aggregators, listicles, LinkedIn thought leadership) are explicitly excluded.
Scan Process
Quick Scan (weekly scheduled task)
- Hit Tier 1 primary sources for new announcements
- Skim Tier 2 practitioner blogs for pattern synthesis
- Cross-reference against library domains using Tier 3
- Flag anything that changes what's possible
Deep Scan (on-demand, triggered by [ACTION NEEDED] or user request)
Uses research/spelunker methodology for epistemic rigor:
- Survey — Map the current state of AI capabilities across all dimensions
- Delta — What changed since last scan? What's trending?
- Decompose — Break each development into atomic claims (spelunker Phase 2)
- Triangulate — Verify claims across multiple independent sources (spelunker Phase 3)
- Adversarial pass — For every significant claim, actively search for counterevidence, limitations, and what the announcement does NOT say (spelunker Phase 5). Tag confidence: Confirmed / Likely / Speculative / Contested
- Implication analysis — For each verified change:
- What skill categories does this enable?
- What existing skills does this make more/less valuable?
- What architectural assumptions does this challenge?
- Opportunity ranking — Prioritize implications by impact × urgency
- Briefing — Present findings using clarity-engine framing (headline → picture → evidence → recommendation)
Output Format
FRONTIER SCAN — [Date]
Scan Type: [Quick / Deep]
Headline: [One sentence — the most important thing]
Key Developments:
1. [Development] → enables [capability] → suggests [library action]
2. [Development] → enables [capability] → suggests [library action]
3. ...
Capability Shifts:
Expanding: [areas where AI is getting notably better]
Plateauing: [areas where improvement has slowed]
Emerging: [new capabilities appearing]
Library Implications:
Build: [new skills/domains this suggests]
Evolve: [existing skills that should be updated]
Retire: [skills that may become less relevant]
Connect: [new cross-domain edges this creates]
Confidence: [High / Medium / Low — how certain are these assessments?]
What This Skill Does NOT Do
- Predict specific timelines — "AGI by 2027" is not a useful output. Capability trajectories are; dates are noise.
- Hype relay — Every AI announcement claims to be revolutionary. This skill filters signal from marketing.
- Technical deep dives — If the user wants to understand a development in depth, hand off to clarity-engine. Frontier-scanner identifies and assesses; clarity-engine explains.
Cross-Domain Connections
- Research/spelunker: Deep scan mode borrows spelunker's full methodology — claim decomposition, source triangulation, adversarial pass, confidence tagging. For deep dives into specific developments frontier-scanner surfaces
- Neocortex/clarity-engine: To explain findings in plain language with visual framing
- Neocortex/growth-architect: Frontier findings feed directly into build prioritization
- Infrastructure/skill-registry: To check current library state when assessing implications
1---2name: frontier-scanner3description: Scan the AI frontier for emerging capabilities, model developments, tool patterns, and paradigm shifts. Use when tracking what's new in AI, understanding capability jumps, evaluating whether a new development changes what's possible, or providing context for skill library planning.4---56# Frontier Scanner — The Telescope78Watches the AI horizon and translates what's happening into actionable intelligence for the skill library. Not a news aggregator — a capability analyst. The question isn't "what launched?" but "what's now *possible* that wasn't before?"910## Core Function1112Track developments across the AI landscape and assess their implications for the skill library's architecture and growth. Every scan answers three questions:13141. **What changed?** — New models, tools, techniques, paradigms152. **What does it enable?** — Capabilities that didn't exist or weren't practical before163. **What does it mean for us?** — How should the skill library respond?1718## Scan Categories1920### 1. Model Capabilities21What can AI models do now that they couldn't before?2223| Dimension | What to Track | Why It Matters |24|-----------|--------------|----------------|25| Reasoning depth | Chain-of-thought improvements, multi-step problem solving | Determines which skills can be more ambitious |26| Context window | Token limits, long-context performance | Affects how much reference material skills can load |27| Multimodal | Vision, audio, code execution, tool use | Opens entirely new skill categories |28| Agentic behavior | Planning, tool chaining, autonomous operation | Changes what "a skill" can even be |29| Speed/cost | Latency improvements, pricing changes | Makes some approaches practical that weren't before |3031### 2. Tool Ecosystem32What new tools, frameworks, and integrations exist?3334| Dimension | What to Track | Why It Matters |35|-----------|--------------|----------------|36| MCP servers | New Model Context Protocol integrations | Directly extends what skills can access |37| IDE integration | Editor plugins, workflow tools | Changes how skills are invoked |38| Agent frameworks | New agent SDKs, orchestration patterns | May suggest new skill architecture patterns |39| Data access | New APIs, databases, knowledge sources | Expands what skills can know about |4041### 3. Paradigm Shifts42What fundamental assumptions about AI are changing?4344| Signal | Example | Implication |45|--------|---------|-------------|46| New interaction pattern | Conversational → agentic → ambient | Skill invocation models may need rethinking |47| Capability plateau | A previously fast-improving area levels off | Shift investment to areas still improving |48| Unexpected emergence | Models suddenly good at something nobody predicted | New skill category opportunity |49| Commoditization | Something that was cutting-edge becomes table stakes | Existing skills may need less sophistication, new skills become possible |5051## Source Intelligence5253All scans draw from the curated source registry: `references/source-registry.md`5455Sources are tiered by signal density:56- **Tier 1** (every scan): Model lab blogs, tool ecosystem changelogs, MCP/SDK repos57- **Tier 2** (weekly): Practitioner analysis blogs, arXiv/HuggingFace research feeds58- **Tier 3** (when relevant): Domain-specific AI applications mapped to library domains5960Anti-sources (hype aggregators, listicles, LinkedIn thought leadership) are explicitly excluded.6162## Scan Process6364### Quick Scan (weekly scheduled task)651. Hit Tier 1 primary sources for new announcements662. Skim Tier 2 practitioner blogs for pattern synthesis673. Cross-reference against library domains using Tier 3684. Flag anything that changes what's possible6970### Deep Scan (on-demand, triggered by [ACTION NEEDED] or user request)71Uses `research/spelunker` methodology for epistemic rigor:72731. **Survey** — Map the current state of AI capabilities across all dimensions742. **Delta** — What changed since last scan? What's trending?753. **Decompose** — Break each development into atomic claims (spelunker Phase 2)764. **Triangulate** — Verify claims across multiple independent sources (spelunker Phase 3)775. **Adversarial pass** — For every significant claim, actively search for counterevidence, limitations, and what the announcement does NOT say (spelunker Phase 5). Tag confidence: Confirmed / Likely / Speculative / Contested786. **Implication analysis** — For each verified change:79 - What skill categories does this enable?80 - What existing skills does this make more/less valuable?81 - What architectural assumptions does this challenge?827. **Opportunity ranking** — Prioritize implications by impact × urgency838. **Briefing** — Present findings using clarity-engine framing (headline → picture → evidence → recommendation)8485## Output Format8687```88FRONTIER SCAN — [Date]89Scan Type: [Quick / Deep]9091Headline: [One sentence — the most important thing]9293Key Developments:94 1. [Development] → enables [capability] → suggests [library action]95 2. [Development] → enables [capability] → suggests [library action]96 3. ...9798Capability Shifts:99 Expanding: [areas where AI is getting notably better]100 Plateauing: [areas where improvement has slowed]101 Emerging: [new capabilities appearing]102103Library Implications:104 Build: [new skills/domains this suggests]105 Evolve: [existing skills that should be updated]106 Retire: [skills that may become less relevant]107 Connect: [new cross-domain edges this creates]108109Confidence: [High / Medium / Low — how certain are these assessments?]110```111112## What This Skill Does NOT Do113114- **Predict specific timelines** — "AGI by 2027" is not a useful output. Capability trajectories are; dates are noise.115- **Hype relay** — Every AI announcement claims to be revolutionary. This skill filters signal from marketing.116- **Technical deep dives** — If the user wants to *understand* a development in depth, hand off to clarity-engine. Frontier-scanner identifies and assesses; clarity-engine explains.117118## Cross-Domain Connections119120- **Research/spelunker**: Deep scan mode borrows spelunker's full methodology — claim decomposition, source triangulation, adversarial pass, confidence tagging. For deep dives into specific developments frontier-scanner surfaces121- **Neocortex/clarity-engine**: To explain findings in plain language with visual framing122- **Neocortex/growth-architect**: Frontier findings feed directly into build prioritization123- **Infrastructure/skill-registry**: To check current library state when assessing implications
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