call-ai-disclosure-comprehension-auditor

Saying "this is an AI" is step one. Knowing it landed is the actual job.

call-review checks that a disclosure exists somewhere in the agent's turns. This skill audits the full sequence the law and the research both point at: disclosure in the first breath, an explicit comprehension question before any pitch, and a captured acknowledgment - and it writes the opening that produces that sequence next time.

When To Use

• after any CALL-E outbound call, to grade its disclosure posture
• before acting on a result where the callee's agreement matters - an agreement from someone who never knew an AI was speaking is fragile
• before placing calls, to craft an Article-50-style disclosure opening

When Not To Use

• to certify legal compliance for any jurisdiction; the verdicts are transcript observations, and regulators - not scripts - decide compliance
• to prove the person did not understand; silence after a disclosure is not evidence of confusion, and the card says so
• during a call; this is strictly post-call analysis plus pre-call goal crafting

Workflow

Audit a finished call

python3 scripts/disclosure_comprehension_auditor.py analyze --transcript path/to/call-result.json

Reads the real get_call_run result shape ({status, result: {transcript}}) or the flat shape used by sibling skill fixtures. Emits a card:

• disclosure_present / disclosure_early (first agent turn vs later)
• comprehension_check_present - an explicit question ("do you understand", "is that okay", "does that make sense") in the disclosure turn or the next agent turn, BEFORE business content: if digits (dates, amounts, times) already appear in the disclosure turn, the pitch came first and the check does not count
• acknowledgment_captured - an affirmative callee reply in the first callee turn after the check
• evidence: the disclosure / check / acknowledgment turns, masked
• verdict: FULL / PARTIAL_NO_ACK / DISCLOSED_NO_CHECK / LATE_DISCLOSURE / UNDISCLOSED, plus unclear paths for empty or agent-less transcripts

Only agent turns can disclose; a callee asking "are you a robot?" is not disclosure and does not count.

Craft the disclosure-first goal

python3 scripts/disclosure_comprehension_auditor.py craft --scenario disclosure-first-call

Emits the plan_call inputs JSON whose goal puts the disclosure in the first sentence, pairs it with one comprehension question, and refuses to proceed to business until the person acknowledges.

Scientific and Regulatory Foundation

Source	Relevance
Regulation (EU) 2024/1689 (AI Act), Article 50	The transparency obligation this skill operationalizes on the transcript axis: AI systems interacting with natural persons must inform them; applicable from 2026-08-02
European Commission Guidelines on Transparency Obligations (digital-strategy.ec.europa.eu)	Official interpretation accompanying Article 50
Chen et al., The impact of artificial intelligence disclosure on user engagement, Computers in Human Behavior 2024, doi:10.1016/j.chb.2024.108448	Empirical evidence that disclosure measurably changes user engagement - so WHERE and HOW it lands matters, not just whether it exists
Chen, Sun et al., The Effects of AI Identity Disclosure on User Responses: A Meta-Analysis, Information Processing & Management 2026 (PII S0378720626001114)	Meta-analytic effect sizes of disclosure on evaluations, attitudes, intentions, behaviors

Citation notes recorded during verification: the two Chen-lineage papers are distinct (a 2024 individual study in CHB with the exact DOI above, and a 2026 IP&M meta-analysis); Article 50 obligations apply from 2 August 2026. California's B.O.T. Act (SB 1001, 2018) covers bot disclosure in the US and is consistent with this design but is not separately cited in the table. This skill implements a lexical approximation and labels every output analysis_mode: "heuristic".

Differences from sibling skills

• call-review checks disclosure presence as one compliance line; this skill grades the full disclosure-comprehension-acknowledgment sequence and writes the compliant opening for the next call.
• call-sycophancy-guard protects factual integrity under pressure; this skill protects the conversational precondition - the person knowing who is talking - under which any agreement is meaningful.