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 zircote-plugins/sigint --skill tech-assessmentgit clone --depth 1 https://github.com/zircote-plugins/sigintWrote 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/zircote-plugins/sigint/tech-assessment)<a href="https://agentmods.dev/skills/zircote-plugins/sigint/tech-assessment"><img src="https://agentmods.dev/badge/skills/zircote-plugins/sigint/tech-assessment/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/zircote-plugins/sigint/tech-assessment"><img src="https://agentmods.dev/badge/skills/zircote-plugins/sigint/tech-assessment.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 | $0.00065 | $0.03278 |
| Opus 5.5 | $0.00026 | $0.01311 |
| Sonnet 5.5 | $0.00013 | $0.00656 |
| Haiku 4.5 | $0.00006 | $0.00328 |
Grade A, and why
tech-assessment 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 1mo 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 — 365 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Technology Assessment
Overview
Technology assessment evaluates technologies for strategic fit, technical feasibility, and competitive advantage. This skill covers frameworks for making informed technology decisions.
Required Frameworks
| Framework | Output Section | Required | Condition |
|---|---|---|---|
| TRL Levels | Technology Readiness | yes | — |
| Hype Cycle Mapping | Hype Cycle Position | yes | — |
| Build vs Buy Matrix | Build vs Buy Analysis | yes | — |
| Domain-Specific Due Diligence | Due Diligence | conditional | Applicable domain detected (AI/ML, Fintech, Healthcare, Infrastructure) |
Trend Indicators: Load and apply the trend indicator definitions from protocols/TREND-INDICATORS.md.
Critical Assessment Rules
Follow these rules to produce assessments that are honest, grounded, and actionable:
- Contextual Grounding: Always tie scores, recommendations, and analysis back to the user's specific context (team size, scale, budget, timeline). A technology that scores 5/5 for Google may score 2/5 for a 10-person startup.
- Honest Scoring: Do not inflate scores. A score of 2 or 3 is normal and valuable — it tells the user where the risks are. If a technology has known weaknesses, say so. Scores above 4 require strong justification.
- Decisive Recommendations: End every assessment with a clear recommendation. "It depends" is not a recommendation. If the answer genuinely depends, specify what it depends ON and give a recommendation for each scenario.
- Trend Indicators Everywhere: Apply INC/DEC/CONST trend indicators to every technology mentioned, not just the primary subject. Trend context helps the user understand trajectory.
- Domain Tailoring: When the user's domain has specific considerations (AI/ML, fintech, healthcare, infrastructure), extend the standard framework with domain-specific evaluation criteria rather than using a generic checklist.
- Always Consider Alternatives: Every assessment should mention at least 2-3 alternatives to the technology being assessed, with brief comparative notes. The user needs to know what else they could choose.
- Quantify When Possible: Prefer concrete numbers over vague qualifiers. "50k msgs/sec" is better than "high throughput." "$23k/year" is better than "affordable." "3-6 months" is better than "some time."
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.
- 1mo ago First seen · 365 lines · 65 tokens per session scan A fb99004ff3c1
tech-assessment is a skill published in the GitHub repository zircote-plugins/sigint (24 stars, last pushed 2mo ago), licensed MIT. It adds 65 tokens to every session and 3,278 once invoked, about $0.0003 per session on Opus 5.5. 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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