screening-alert-adjudication-amir-fadavi
Adjudicates whether a hit generated by sanctions, PEP, or adverse-media screening is a true positive, false positive, or requires human escalation. Use whenever a user presents a screening alert, a name match against a watchlist (OFAC SDN, EU consolidated list, UK OFSI, UN list, PEP list, adverse media hit, etc.), or asks to clear a screening hit / reduce false positives / determine whether a flagged name is actually the listed party. Use even when the user describes the task casually — \"is this person actually on the sanctions list\", \"did we get a real match\", \"clear this alert\", \"I ha
npx skills add lawve-ai/awesome-legal-skills --skill screening-alert-adjudication-amir-fadavi --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Screening Alert Adjudication This skill adjudicates a single screening hit — a name that an upstream screening system flagged as a possible match against a sanctions list, PEP list, adverse-media source, or similar watchlist — and reaches one of three conclusions: - **True positive (TP)** — the screened party is the listed party - **False positive (FP)** — the screened party is not the listed party - **Escalate** — evidence is insufficient to deterministically conclude either way; hand off to a human analyst with the full evidence record ## Why this skill exists Screening systems generate enormous volumes of low-quality alerts. Analysts spend their time clearing alerts that should never have fired (wrong entity type, common name with no overlap on identifiers, partial-name matches that ignore naming convention). A deterministic, criteria-driven adjudication layer can clear the obvious false positives and confirm the obvious true positives, leaving humans to focus on the genuinely ambiguous cases. The skill is designed around two non-negotiable properties: 1. **Determinism.** Given identical evidence, the skill reaches an identical conclusion. The skill never weighs probabilities,
- Why this skill exists
- How the work is organized
- What the skill needs to start
- Inferring entity type when not provided
- The core question the skill answers
- Working with names across scripts and naming conventions
- Output requirements
- The adjudication procedure
- Step 1: Read the alert
- Step 2: Run Tier 0
- Step 3: Run Tier 1
- Step 4: Run Tier 2
- Step 5: Decide whether to enter Tier 3
- Step 6: Run Tier 3 (if entered)
What does the screening-alert-adjudication-amir-fadavi skill do?
Adjudicates whether a hit generated by sanctions, PEP, or adverse-media screening is a true positive, false positive, or requires human escalation. Use whenever a user presents a screening alert, a name match against a watchlist (OFAC SDN, EU consolidated list, UK OFSI, UN list, PEP list, adverse media hit, etc.), or asks to clear a screening hit / reduce false positives / determine whether a flagged name is actually the listed party. Use even when the user describes the task casually — \"is this person actually on the sanctions list\", \"did we get a real match\", \"clear this alert\", \"I ha
How do I install it?
Run `npx skills add lawve-ai/awesome-legal-skills --skill screening-alert-adjudication-amir-fadavi --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From lawve-ai/awesome-legal-skills, a repository with 618 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.