skills/rapport-intelligence-yield/SKILL.md
Research linking rapport quality in interviews to information yield and intelligence value
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license: Apache-2.0
metadata:
name: rapport-intelligence-yield
version: 1.0
source: "The Impact of Rapport on Intelligence Yield: Police Source Handler
Telephone Interactions with Covert Human Intelligence Sources"
authors: "Nunan, Stanier, Milne, Shawyer, Walsh, May"
description: >
Empirically-grounded framework for maximizing information elicitation
through measurable rapport behaviors. Overturns the warmth-first consensus;
operationalizes relationship quality as auditable behavioral frequencies.
activation_triggers:
- Designing agents that elicit information from users
- Auditing why conversational agents fail to get useful outputs
- Evaluating response quality across granular output sub-types
- Building training or evaluation frameworks for communicative agents
- Any task involving trust-based disclosure or cooperative information transfer
- Diagnosing gaps between an agent's self-assessed behavior and actual behavior
Load this skill when the core problem is getting a human to disclose useful, accurate, detailed information in a conversational context — or when building/evaluating systems that must do this reliably.
Specific triggers:
What this skill is not for: General conversational design, persuasion, or influence tasks that don't involve genuine information elicitation from a motivated human source.
Rapport is not an ambient interpersonal quality — it is a count of specific verbal behaviors: back-channels ("mm-hmm," "I see"), paraphrases, probes, agreement signals, procedural framing. This means rapport is auditable, trainable, and comparable across sessions. An agent either produced N paraphrases or it didn't. You cannot assess rapport by asking the agent how it felt the conversation went.
Operational implication: Replace "was this a good conversation?" with "how many attention-class behaviors appeared per exchange unit?"
The empirical finding that overturns practitioner consensus: attention behaviors explain ~69% of variance in intelligence yield; positivity (warmth, empathy, humor) explains ~4%. What makes sources disclose is not being liked — it is being demonstrably processed. The source must see that their output is being received, remembered, explored, and acted upon.
Attention behaviors include: active listening signals, probing questions, summarizing back, exploring motivation, tracking prior disclosures.
Operational implication: If forced to choose where to invest, optimize for attention over positivity. Positivity has interpersonal value but weak causal link to information output.
Coordination — shared goal-framing, agreeing on process, encouraging the other party to speak, appropriate pausing — creates the conditions under which transfer can occur. It answers: "Do both parties know why we're talking and what success looks like?" Though used least frequently by handlers in the study, it significantly correlated with yield, particularly for action and temporal detail types.
Operational implication: Early coordination investment (framing the purpose, confirming mutual goals) is disproportionately leveraged. Don't treat it as optional scene-setting.
Agents and humans alike cannot accurately describe their own communicative behavior. Police officers in this study reported using rapport behaviors they demonstrably were not using when recordings were analyzed. This is not deception — it is a structural gap between perceived practice and actual practice. Any quality framework that relies on self-assessment of behavioral frequency will be biased toward overconfidence.
Operational implication: Behavioral auditing of actual outputs is mandatory. Self-report is useful only as a hypothesis about what an agent believes it does — never as ground truth.
"Output quality" is not one thing. The paper codes yield across five detail types: surrounding (location/context), object, person, action, and temporal. Different rapport components differentially predict different yield types — attention predicts all five, coordination specifically predicts action and temporal detail. Treating yield as monolithic hides this structure.
Operational implication: Decompose output evaluation into typed sub-categories. A system that produces rich person-descriptions but thin temporal context has a different failure mode than one producing the inverse.
→ First: Audit attention-class behaviors. Are probes being issued? Are prior disclosures being referenced and built upon? Is the agent demonstrably tracking what the human said?
→ Second: Check coordination. Did the interaction establish shared purpose? Does the human know what a useful response looks like?
→ Third (only after the above): Check positivity. Warmth issues are rarely the bottleneck.
→ Decompose into typed sub-categories before scoring. Use a yield taxonomy (surrounding, object, person, action, temporal or domain-equivalent). Identify which types are present, absent, or thin.
→ Do not collapse to a single score until you've mapped the sub-type profile.
→ Treat as hypothesis, not evidence. Collect behavioral counts from actual transcripts. Expect systematic overestimation of attention and coordination behaviors.
→ Use behavioral auditing as the primary quality signal; self-report as a secondary diagnostic only.
→ Prioritize attention architecture first: build in probing logic, back-channel signaling, paraphrase-and-confirm loops, prior-disclosure tracking.
→ Design coordination hooks early: opening frames that establish shared purpose, checkpoints that confirm mutual goal alignment.
→ Add positivity/warmth last: it is necessary for relationship maintenance but not the primary yield driver.
→ Attention > Coordination > Positivity for yield.
→ Coordination > Attention > Positivity for establishing the interaction's productive structure before content exchange begins.
→ Check for coordination failure first: has shared purpose been established? Does the source understand what they're contributing to?
→ Check for attention failure second: have their previous disclosures been visibly processed and built upon?
→ Check for working alliance erosion: is the source's motivation being modeled and reciprocated? Has the relationship value been maintained across sessions?
| File | When to Load |
|---|---|
| references/rapport-as-behavioral-frequency-not-feeling.md | When you need the full operationalization of rapport into countable behaviors; when designing behavioral audit frameworks; when explaining why rapport can be measured and trained |
| references/attention-dominates-yield-active-processing-over-warmth.md | When diagnosing low-yield interactions; when deciding where to invest optimization effort; when the assumption is that warmth is the primary driver |
| references/coordination-shared-goal-structure-enables-transfer.md | When designing interaction opening structures; when an exchange lacks shared purpose framing; when action/temporal detail yield is specifically low |
| references/self-report-gap-behavioral-auditing-vs-perceived-practice.md | When evaluating agent quality via self-assessment; when building quality auditing pipelines; when there's a gap between perceived and actual agent behavior |
| references/intelligence-yield-taxonomy-decomposing-output-quality.md | When designing or applying output quality metrics; when "quality" is being treated as monolithic; when different detail types need to be evaluated separately |
| references/working-alliance-motivation-modeling-source-management.md | When managing multi-session relationships; when source motivation is variable or declining; when the relationship itself is a resource to be maintained |
| references/informal-vs-formal-interaction-elicitation-context-matters.md | When the interaction context (formal/structured vs. informal/conversational) shapes what rapport behaviors are available; when adapting elicitation strategy to setting |
The Warmth Fallacy
Assuming that if an agent is friendly, empathetic, and personable, disclosure will follow. Positivity is necessary but not sufficient — and it explains almost none of the variance in yield. Optimizing for warmth while neglecting attention and coordination is the most common practitioner error documented in this research.
Monolithic Quality Scoring
Collapsing output evaluation to a single score ("this response was a 7/10"). This destroys the sub-type structure of yield and makes it impossible to diagnose which kinds of information are missing and why.
Self-Report as Ground Truth
Accepting an agent's (or practitioner's) description of their own behavior as accurate evidence. The self-report gap is large, consistent, and skews toward overconfidence. Behavioral auditing of actual outputs is not optional.
Front-Loading Warmth, Deferring Coordination
Spending the early interaction on rapport-building pleasantries without establishing shared purpose and process. Coordination in the opening phase has disproportionate leverage on the entire exchange.
Treating Rapport as a One-Time Achievement
Rapport is not a state you achieve and then maintain passively. It is a frequency of behavior that must be sustained across every exchange unit. A single session of high attention does not carry over unless the behaviors are repeated.
Ignoring the Working Alliance Across Sessions
Treating each interaction as independent. In repeated-interaction contexts, the relationship itself is an asset with ongoing maintenance requirements. Motivation modeling — understanding why the source is participating and reciprocating that investment — determines whether the alliance survives across sessions.
Auditing Behavior You Were Looking For
Designing behavioral audits that only detect the categories you expect. The five yield-type taxonomy and three rapport components exist because the researchers coded for all behaviors, not just expected ones. Narrow audit frameworks will miss the structure.
How to tell if someone has genuinely internalized this book vs. skimmed it:
They have internalized it if they:
They have only read the summary if they:
Load reference files on demand as specific sub-problems arise. This index is sufficient for most diagnostic and design tasks; the reference files provide the mechanistic depth needed for implementation and evaluation work.
tools
Building resilient distributed systems with circuit breakers, retries with full-jitter exponential backoff, retry budgets (per-request 3-attempt + per-client 10% ratio per Google SRE), deadline propagation, and the cascading-failure math (4 layers × 3 retries = 64x amplification). Grounded in Resilience4j, Microsoft Cloud Patterns, AWS Architecture Blog (Marc Brooker), and Google SRE Book.
testing
Designing HTTP cache headers that work correctly across browsers, CDNs, and shared proxies — `Cache-Control` directives per RFC 9111, `stale-while-revalidate` and `stale-if-error` per RFC 5861, the Vary header for varying responses, and surrogate keys for tag-based purging. Grounded in IETF RFCs and Cloudflare/Fastly docs.
development
Use when designing or fixing a Content Security Policy on a real site, choosing between nonce-based and hash-based CSP, adding strict-dynamic, debugging "Refused to execute inline script" errors, deploying CSP in report-only mode first, configuring report-to / report-uri, or auditing an existing policy for unsafe-inline / unsafe-eval / wildcards. Triggers: "CSP blocks legitimate inline script", strict-dynamic, nonce-{RANDOM}, sha256-{HASH}, object-src none, base-uri none, frame-ancestors, Trusted Types, X-Content-Security-Policy obsolete, report-only vs enforced. NOT for general HTTP security headers (HSTS, COOP/COEP), Trusted Types deep dive, CORS configuration, or building a WAF.
tools
Choosing and operating an HTTP API versioning strategy that doesn't break clients — Stripe's date-based pinned versions, the Deprecation/Sunset header pair (RFC 9745 + RFC 8594), URI vs header vs media-type approaches, and the version-transformer pattern. Grounded in Stripe's published architecture and IETF RFCs.