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 skills add magnus919/hermes-profiles --skill technology-radargit clone --depth 1 https://github.com/magnus919/hermes-profilesWrote 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/magnus919/hermes-profiles/technology-radar)<a href="https://agentmods.dev/skills/magnus919/hermes-profiles/technology-radar"><img src="https://agentmods.dev/badge/skills/magnus919/hermes-profiles/technology-radar/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/magnus919/hermes-profiles/technology-radar"><img src="https://agentmods.dev/badge/skills/magnus919/hermes-profiles/technology-radar.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00079 | $0.01042 |
| Opus 5 | $0.00039 | $0.00521 |
| Sonnet 5 | $0.00016 | $0.00208 |
| Haiku 4.5 | $0.00008 | $0.00104 |
Grade A, and why
technology-radar 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 — 90 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Technology Radar
CTO methodology for making technology decisions, governing architecture, measuring engineering effectiveness, managing technical debt, and operating an innovation pipeline. These frameworks help a CTO balance short-term delivery velocity with long-term platform health.
Domain Model
| Domain | Covers | Artifact |
|---|---|---|
| Technology Radar | Adopt/Trial/Assess/Hold quadrants, tool selection criteria, deprecation policy | Technology radar document |
| Build vs Buy | TCO analysis, decision matrices, vendor evaluation, integration cost | Build-vs-buy recommendation |
| Architecture Governance | Standards, review boards, RFC process, design reviews | ADRs, RFC documents, governance charter |
| Engineering Metrics | DORA (deploy frequency, lead time, MTTR, change failure rate), SPACE, DevEx | Engineering dashboard, health report |
| Technical Debt | Interest calculation, remediation prioritization, principal estimation | Technical debt register |
| Innovation Pipeline | Horizon scanning, POC criteria, production readiness gates | Innovation funnel, POC report |
When to Load
Load this skill when the task involves:
- Evaluating a new technology or tool for adoption
- Making a build-vs-buy decision with TCO analysis
- Designing or auditing architecture governance processes
- Setting up engineering metrics dashboards (DORA, SPACE)
- Quantifying and prioritizing technical debt remediation
- Running an innovation pipeline with POC-to-production gates
- Deprecating or retiring legacy technology
- Conducting an architecture review board session
Loading Order
skill_view('technology-radar') # This — methodology index
skill_view('executive-methodology') # Shared decision frameworks
skill_view('artifact-pyramids') # Output contract
skill_view('technology-radar', file_path='references/technology-radar.md')
skill_view('technology-radar', file_path='references/build-vs-buy.md')
skill_view('technology-radar', file_path='references/architecture-governance.md')
skill_view('technology-radar', file_path='references/engineering-metrics.md')
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 90 lines · 79 tokens per session scan A 67f52832738f
technology-radar is a skill published in the GitHub repository magnus919/hermes-profiles (161 stars, last pushed 2mo ago), licensed MIT. It adds 79 tokens to every session and 1,042 once invoked, about $0.0004 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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