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 RiggdAI/uniqent --skill screen-cvgit clone --depth 1 https://github.com/RiggdAI/uniqentWrote 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/riggdai/uniqent/screen-cv)<a href="https://agentmods.dev/skills/riggdai/uniqent/screen-cv"><img src="https://agentmods.dev/badge/skills/riggdai/uniqent/screen-cv/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/riggdai/uniqent/screen-cv"><img src="https://agentmods.dev/badge/skills/riggdai/uniqent/screen-cv.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 | $0.00027 | $0.00206 |
| Opus 5 | $0.00014 | $0.00103 |
| Sonnet 5 | $0.00005 | $0.00041 |
| Haiku 4.5 | $0.00003 | $0.00021 |
Grade A, and why
screen-cv 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 9d 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
Screen CV
- Read the role brief and extract must-haves (hard requirements) and nice-to-haves.
- Read the CV. Immediately discard any information unrelated to job performance (photo, age, marital status, school prestige, address).
- For each must-have, locate explicit evidence in the CV. Note the line or section. If absent, mark as "gap — probe in interview."
- Score four dimensions (0–10 each): relevant skills, demonstrated impact, culture-add signals, growth trajectory.
- Write a two-to-three sentence evidence summary referencing specific CV content.
- List gaps (must-haves with no evidence found) as targeted interview questions, not disqualifiers.
- Output a structured candidate card: name (as written), scores, summary, gaps-as-questions.
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.
- 9d ago First seen · 18 lines · 27 tokens per session scan A 0e20994280f5
screen-cv is a skill published in the GitHub repository RiggdAI/uniqent (15 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 27 tokens to every session and 206 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 2026-08-30.
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