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 shortlistgit 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/shortlist)<a href="https://agentmods.dev/skills/riggdai/uniqent/shortlist"><img src="https://agentmods.dev/badge/skills/riggdai/uniqent/shortlist/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/shortlist"><img src="https://agentmods.dev/badge/skills/riggdai/uniqent/shortlist.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 | $0.00024 | $0.00196 |
| Opus 5 | $0.00012 | $0.00098 |
| Sonnet 5 | $0.00005 | $0.00039 |
| Haiku 4.5 | $0.00002 | $0.00020 |
Grade A, and why
shortlist 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
Shortlist
- Collect all candidate cards produced by the screen-cv skill.
- Compute a weighted total score: skills 40 %, impact 35 %, culture-add 15 %, growth 10 %.
- Rank candidates by total score descending. Select the top 5 (or fewer if fewer were screened).
- For each shortlisted candidate, write a one-paragraph rationale citing the strongest two or three evidence points from their CV.
- For each shortlisted candidate, list their must-have gaps as explicit interview questions (not rejection reasons).
- For candidates not shortlisted, include a one-sentence note on the primary differentiator that placed them below the cut-off — keep this neutral and evidence-based.
- Output the ranked shortlist table followed by the per-candidate rationale sections.
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 · 24 tokens per session scan A 0c10e258074f
shortlist is a skill published in the GitHub repository RiggdAI/uniqent (15 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 24 tokens to every session and 196 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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