Borrowing it
Nothing to install: this file belongs to victoriacity/openakari. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/victoriacity/openakari/main/.claude/skills/horizon-scan/SKILL.mdgit clone --depth 1 https://github.com/victoriacity/openakariWrote 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/skills/victoriacity/openakari/horizon-scan)<a href="https://agentmods.dev/skills/victoriacity/openakari/horizon-scan"><img src="https://agentmods.dev/badge/skills/victoriacity/openakari/horizon-scan/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/skills/victoriacity/openakari/horizon-scan"><img src="https://agentmods.dev/badge/skills/victoriacity/openakari/horizon-scan.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 | $0.00034 | $0.01766 |
| Opus 5 | $0.00017 | $0.00883 |
| Sonnet 5 | $0.00007 | $0.00353 |
| Haiku 4.5 | $0.00003 | $0.00177 |
Grade A, and why
horizon-scan 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 — 162 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/horizon-scan [focus area]
Proactively scan external sources for GenAI developments relevant to akari's active projects. This skill replaces human knowledge injection for routine developments (new model releases, benchmark results, capability announcements, relevant papers).
If a focus area is provided (e.g., "3D generation models", "LLM judge evaluation"), narrow the scan to that domain. Otherwise, scan broadly across all active project concerns.
Step 1: Determine scan scope
Read the following to understand what akari currently cares about:
- Active projects:
ls projects/→ read each active project's README for Mission, Open questions, and recent log entries. - Model changes (if relevant): new model releases or API changes that might affect current work.
- Open questions: Collect open questions from all active project READMEs. These are the knowledge gaps horizon-scan aims to fill.
- Prior scan reports: Check
.scheduler/skill-reports/horizon-scan-*.mdfor previous scans to avoid redundant coverage.
From this, produce a scan agenda: 3-5 specific topics to search for, each tied to a project need or open question. Example:
Scan agenda:
1. New model releases since last scan (if you track models)
2. 3D generation quality benchmarks or evaluations (sample-project open questions)
3. LLM agent architecture papers (akari design-patterns paper related work)
4. Multi-modal evaluation methods (sample-project judge methodology)
Step 2: Search
For each topic in the scan agenda, run 2-3 WebSearch queries. Use time-bounded queries where possible (e.g., include the current month/year to find recent results).
Search strategy:
- Model releases: Search for "[model family] new release 2026", "[model family] announcement"
- Capabilities: Search for "[capability] benchmark results 2026", "[capability] evaluation"
- Research: Search for "[topic] arxiv 2026", "[topic] research paper"
- Tools/APIs: Search for "[tool] release", "[API] update changelog"
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 · 162 lines · 34 tokens per session scan A b34d20535b84
horizon-scan is a skill published in the GitHub repository victoriacity/openakari (47 stars, last pushed 6mo ago), licensed MIT. It adds 34 tokens to every session and 1,766 once invoked, about $0.0002 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 2026-08-30.
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