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 ulises-jeremias/agent-toolkit --skill technical-unit-assessmentgit clone --depth 1 https://github.com/ulises-jeremias/agent-toolkitWrote 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/ulises-jeremias/agent-toolkit/technical-unit-assessment)<a href="https://agentmods.dev/skills/ulises-jeremias/agent-toolkit/technical-unit-assessment"><img src="https://agentmods.dev/badge/skills/ulises-jeremias/agent-toolkit/technical-unit-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/ulises-jeremias/agent-toolkit/technical-unit-assessment"><img src="https://agentmods.dev/badge/skills/ulises-jeremias/agent-toolkit/technical-unit-assessment.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 | $0.00035 | $0.00500 |
| Opus 5.5 | $0.00014 | $0.00200 |
| Sonnet 5.5 | $0.00007 | $0.00100 |
| Haiku 4.5 | $0.00003 | $0.00050 |
Grade A, and why
technical-unit-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.
What it actually says
Technical Unit Assessment (WHAT)
Assess a technical unit: frontend app, backend API, data platform, infrastructure/IaC scope, mobile app, AI/ML pipeline, or another technical workload.
Run project-assessment-evidence first.
Use project-assessment as the router for multi-unit assessments.
Out of scope
- Does NOT score indicators without evidence — mark as Not assessed
- Does NOT make technical decisions — scores inform, humans decide
- Does NOT produce final report without
output-handshake
Unit intake
Ask before scoring:
- Unit name and type (frontend / backend / infra / data / mobile / AI)
- Repositories, services, infrastructure scopes, or pipelines included
- Technical decision ownership (team, client, shared, unknown)
- Assessment period
- Authoritative systems for code, docs, CI/CD, incidents, observability, security
Workflow
- Run
project-assessment-evidenceto build evidence map - Select indicator groups matching the unit type (see
references/indicator-groups.md) - Score only indicators with evidence; mark rest as Not assessed
- Note confidence and missing evidence per score
- Apply
output-handshakebefore final scorecard
Scoring rules
Use the 1–5 scale from references/indicator-groups.md. Do not average unrelated indicators
without explaining weighting. Request validator for subjective scores.
References
references/indicator-groups.md— all indicator groups + scoring scale + AI-native readinessreferences/default-template.md— technical unit scorecard templatereferences/example-frontend-assessment.md— example assessment for a frontend team
Delegates to
| Need | Skill |
|---|---|
| Evidence intake | project-assessment-evidence |
| Multi-unit routing | project-assessment |
| Deep UI/UX review | figma / figma-implement-design |
| Repo discovery | assistant |
| Final output gate | output-handshake |
What ships with it
3 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 · 59 lines · 35 tokens per session scan A 676a0fb1c91a
technical-unit-assessment is a skill published in the GitHub repository ulises-jeremias/agent-toolkit (19 stars, last pushed yesterday), licensed MIT. It adds 35 tokens to every session and 500 once invoked, about $0.0001 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 2026-09-04.
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