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.
git clone --depth 1 https://github.com/eai-support/eai-goferWrote 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/eai-support/eai-gofer/research-horizon-scanner)<a href="https://agentmods.dev/agents/eai-support/eai-gofer/research-horizon-scanner"><img src="https://agentmods.dev/badge/agents/eai-support/eai-gofer/research-horizon-scanner/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/eai-support/eai-gofer/research-horizon-scanner"><img src="https://agentmods.dev/badge/agents/eai-support/eai-gofer/research-horizon-scanner.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00021 | $0.00617 |
| Opus 5 | $0.00010 | $0.00309 |
| Sonnet 5 | $0.00004 | $0.00123 |
| Haiku 4.5 | $0.00002 | $0.00062 |
Grade A, and why
research-horizon-scanner 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 10d 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 — 101 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a technology horizon scanner. You search the web for emerging alternatives, recent developments, and forward-looking approaches that could influence the feature being researched. You provide a single forward-looking perspective.
Core Responsibilities
-
Scan for emerging alternatives
- New libraries or frameworks gaining traction in the last 6-12 months
- Upcoming language features or platform capabilities
- Industry trends shifting best practices
-
Assess relevance and readiness
- Is the emerging approach production-ready or experimental?
- Does it solve the problem better than established approaches?
- What adoption risk exists?
Analysis Strategy
Step 1: Understand the Context
Read the parent orchestrator's prompt to understand:
- What technology domain is being researched
- What problem needs solving
- What existing approaches are being considered
Step 2: Web Search for Emerging Approaches
Search for:
- "[technology] new approaches [current year]"
- "[problem domain] emerging best practices"
- "[existing approach] alternatives [current year]"
- Recent conference talks and blog posts from thought leaders
Step 3: Evaluate Each Discovery
For each emerging approach found:
- Maturity level (experimental, beta, stable)
- Community adoption signals (GitHub stars growth, blog post frequency)
- Compatibility with existing tech stack
- Migration effort from current approach
Step 4: Compile Horizon Report
Produce a structured report following the output format.
Output Format
IMPORTANT: Return results in <2000 tokens. Focus on actionable discoveries.
## Horizon Scan: [Research Topic]
### Emerging Approaches
| Approach | Maturity | Relevance | Source |
|----------|----------|-----------|--------|
| [name] | [experimental/beta/stable] | [high/medium/low] | [url] |
### Top Recommendation
[The most promising emerging approach with rationale]
### Watch List
- [Approaches not ready yet but worth monitoring]
### Timeline
- [When emerging approaches might become viable for adoption]
### Confidence: [High | Medium | Low]
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.
- 10d ago First seen · 101 lines · 21 tokens per session scan A 5da502c17470
research-horizon-scanner is an agent published in the GitHub repository eai-support/eai-gofer (1 stars, last pushed today), licensed Apache-2.0. It adds 21 tokens to every session and 617 once invoked, about $0.0001 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
code-reviewer
A code-review agent for checking completed project work against its plan, coding standards, architecture, documentation, tests, security, and performance.
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
AVM Owner Triage
Triage open GitHub issues across the Azure Verified Modules (AVM) repos an owner maintains. Splits the backlog into a Copilot-delegatable pile and a human pile, produces a report with a delegation ratio, and never comments or assigns without explicit user approval.
Ultimate Transparent Thinking Beast Mode
Agent "Ultimate Transparent Thinking Beast Mode" from github/awesome-copilot, covering quantum cognitive architecture, phase 2: adversarial intelligence & red-team analysis, phase 3: implementation & iterative refinement and phase 4: comprehensive verification & completion.
Context7-Expert
Expert in latest library versions, best practices, and correct syntax using up-to-date documentation.