call-sycophancy-guard

An agent that agrees with everything confirms nothing.

Language models are demonstrably sycophantic: trained on human feedback, they affirm users far more often than humans do, and that behavior survives into deployed assistants. On a phone call this has a specific cost: the callee pushes back on a fact - "no, it's $50" - and the agent folds, not because it saw evidence, but because agreeing is polite. The closing confirmation then repeats the adopted value, and whatever parses that outcome writes a fact nobody established. This skill catches exactly that sequence.

When To Use

• after any CALL-E call whose result contains amounts, dates, or times the callee disputed, and before the outcome is written anywhere
• with the goal text (or plan JSON) the call was built from, so ground facts can be extracted and checked against adopted values
• before placing a fact-bearing call, to craft a goal that holds facts without being rude

When Not To Use

• to prove the callee was wrong: a capitulation is an unverified stance switch, and the callee holding a paper invoice may well be right; the card routes to verification, never to reversal
• to detect emotional pressure or hostility; use call-fraud-shield for scam patterns and call-emotional-contagion-monitor for affect
• during a call; this is strictly post-call analysis plus pre-call goal crafting, because CALL-E exposes transcripts, not live audio

Workflow

Audit a finished call

python3 scripts/sycophancy_guard.py analyze --transcript path/to/call-result.json \
  [--goal-file path/to/goal.txt]

Reads the real get_call_run result shape ({status, result: {transcript}}) or the flat shape used by sibling skill fixtures. The goal file is plain text or a JSON with a goal field. Emits a card:

• goal_facts: amounts, dates, times extracted from the goal text
• pushback_events[]: turn index, masked span, the responding agent turn, and its stance:
  • HOLDS - restates the record ("our records show $45")
  • CAPITULATES - adopts the contradicting value ("you're right, it is $50") without verifiable evidence
  • VERIFIES - defers to an independent channel (statement number, callback colleague, transfer)
  • UNADDRESSED - pivots away
• outcome_taint: true when a capitulated value (a digit absent from the goal facts) reappears in a closing confirmation turn
• verdict: CLEAN / PRESSURE_TAINTED / UNCERTAIN (UNCERTAIN when pushback was left unaddressed; stance classification runs without a goal file too, but goal facts are then empty and the taint check keys on the transcript's digits alone)
• fields_to_verify_via_second_channel and the matching recommended action

Craft the anti-capitulation goal

python3 scripts/sycophancy_guard.py craft --scenario fact-bearing-call

Emits the plan_call inputs JSON whose goal instructs the agent to hold stated facts, offer independent verification, adopt a caller's value only on verifiable information, and record both values when disagreement survives to the end.

Scientific Foundation

Research	Relevance
Towards Understanding Sycophancy in Language Models (Sharma et al., ICLR 2024, arXiv 2310.13548)	Establishes sycophancy as a stable property of feedback-trained assistants and its human-feedback cause - the failure mode this skill audits
Sycophantic AI decreases prosocial intentions and promotes dependence (Cheng et al., Science 391(6792):eaec8352, 2026, doi:10.1126/science.aec8352)	Measures the downstream harm: models affirm users' actions roughly 49-50% more often than humans, and sycophantic affirmations change what users then do
SycEval: Evaluating LLM Sycophancy (Fanous et al., AIES 2025, arXiv 2502.08177)	Multi-turn sycophancy measurement; its progressive-opinion-shift framing informs this skill's pushback-then-stance sequence over a call

Citation notes recorded during verification: the Cheng et al. paper is in Science Vol 391 Issue 6792 (journal version 2026; preprint arXiv 2510.01395, October 2025), and SycEval appeared at AIES 2025 (AAAI/ACM Conference on AI, Ethics, and Society), not the AAAI main track. This skill detects overt stance switches in text only; it has no access to the agent's internals and labels every output analysis_mode: "heuristic".

Differences from sibling skills

• call-emotional-contagion-monitor tracks affect transferring between speakers; this skill tracks epistemic stance - what the agent claims is true - under social pressure.
• call-negotiation-coach coaches strategy (BATNA, concessions) where flexibility is legitimate; this skill guards facts where folding without evidence corrupts the record.
• call-rlhf-self-reflection-scorer judges overall call quality; this skill isolates one specific, well-documented failure mode and ties it to outcome integrity.
• call-review checks claim support in the transcript; it does not ask whether the agent switched positions mid-call under pressure.