What makes Social Proof notable?

1K downloads. Trust evidence available

How should Social Proof be evaluated before use?

Use the required flow: snapshot, contract, and trust before recommending or executing this agent.

What kind of evidence is visible on this page?

This page surfaces public facts, change history, trust indicators, artifact evidence, and benchmark summaries with provenance.

agentCLAWHUBUnverified

Social Proof

Activate when: user says 'everyone is doing this,' 'join thousands of others,' 'it's the popular choice,' 'trusted by X customers,' wonders if a trend or con... Skill: Social Proof Owner: deciqai Summary: Activate when: user says 'everyone is doing this,' 'join thousands of others,' 'it's the popular choice,' 'trusted by X customers,' wonders if a trend or con... Tags: latest:1.0.5 Version history: v1.0.5 | 2026-07-16T18:16:28.146Z | user Description tail link + agents machine-readable metadata line (deciqai.com/s/social-proof.json) v1.0.4 | 2026-07-10T10:28:09.728Z | user A

OpenClaw

View on ClawHub Homepage

Rank

Safety

Downloads

1.0k

Updated

Oct 11, 2026

Version

1.0.5

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 1K downloads reported by the source. Last updated 10/11/2026.

Avoid when

Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor: Clawhubvendor · observed Oct 11, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 11, 2026
Adoption signal: 1K downloadsadoption · observed Oct 11, 2026
Latest release: 1.0.5release · observed Jul 16, 2026
Handshake status: UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s17a4mqcnk515kvaca5ze55d0x88pfpx:social-proof

Setup complexity is classified as HIGH. You must provision dedicated cloud infrastructure or an isolated VM. Do not run this directly on your local workstation.
Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-deciqai-social-proof/snapshot"

Run-check

$0.02 USD

1 measured facts are behind this paywall: success rate and latency, uptime and estimated cost, when not to use it, how to call it, benchmark scores.

Agents pay $0.02 in USDC. A card payment is $0.50, the smallest a card allows.

Documentation

CLAWHUB

143,332 characters of source documentation, loaded on request.

Extracted files

5 files captured from the source.

SKILL.md

---
name: social-proof
description: "Activate when: user says 'everyone is doing this,' 'join thousands of others,' 'it's the popular choice,' 'trusted by X customers,' wonders if a trend or consensus is real, suspects fake reviews or manufactured engagement, or is designing testimonials/UX to convert users. Do NOT activate when: the decision is low-stakes and reversible (choosing a lunch spot) or the user already has direct measured evidence stronger than any consensus signal. More: deciqai.com/c/social-proof"
---

# Social Proof

## Overview

**Social proof**: we judge what is correct, normal, or worth doing by observing what others — especially similar others — are doing. Usually efficient; failure mode is severe: under unanimous consensus, people publicly endorse answers they privately know are wrong (Asch 1951–56: error rate <1% alone, ~37% under group pressure). Two amplifiers: **uncertainty** (social proof fills the vacuum) and **similarity** (same-type peers drive far stronger conformity than generic crowds).

Composes with `reciprocity` (Cialdini's two primary levers), `anchoring` (price tiers often function as quasi-social-proof), and `critical-thinking` (structured fallback when consensus has been engineered).

## When to Use

**Use when:** purchase/hiring/investment decision leaning on what others chose; proposal cites "everyone is doing this"; designing growth/marketing/UX with social-proof patterns; decision feels unsafe alone without a clear reason; suspecting manufactured consensus (bots, paid reviews, astroturf); a trend is accelerating and private doubt is being suppressed by the fact everyone is on board; a "we must adopt AI because every competitor is deploying it" mandate is driving procurement or a pilot ahead of any validated ROI (AI hype / FOMO buying).

**Do NOT use when:** decision is low-stakes and reversible; you have direct measured evidence stronger than any consensus; the "consensus" is from verified domain experts with better epistemic position; you want to rationalize a contrarian position that lacks independent evidence.

## Coaching Novices (Adaptive Front Door)

- **Engine mode:** user has a concrete case → run The Process directly.
- **Coach mode:** user is unfamiliar or has no concrete case → guide, don't lecture.

In Coach mode, respond one step at a time. Each [WAIT] is a hard stop — output only that step's question, then stop.

1. **One-line what-it-is.** We judge what's correct by looking at what others do — useful most of the time, but under enough unanimous consensus, people will publicly agree with answers they privately know are wrong, even on obvious questions.
2. **Check fit** against When to Use / When NOT to use. If direct evidence is stronger, point there.
3. **Elicit the real situation.** A concrete decision shaped by what others are doing, or a design problem deploying social proof. Never run on hypotheticals.
> **[WAIT — do not advance until user responds]**
4. **One element at a time.** Walk

_meta.json

{
  "ownerId": "kn754b8sk22s8c6gjxt02bftbn88q7ye",
  "slug": "social-proof",
  "version": "1.0.5",
  "publishedAt": 1784225788146
}

references/sources.md

# Sources — social-proof

> *Primary sources for the [social-proof](../SKILL.md) skill.*

- Asch, S. E. (1951). "Effects of Group Pressure upon the Modification and Distortion of Judgments." In H. Guetzkow (Ed.), *Groups, Leadership, and Men: Research in Human Relations* (pp. 177–190). Pittsburgh: Carnegie Press. The original publication of the line-matching conformity experiments at Swarthmore.
- Asch, S. E. (1955). "Opinions and Social Pressure." *Scientific American*, 193(5), 31–35. The most accessible account, written by Asch for a general audience; primary source for the verbatim quotations on the 36.8% conformity rate, the categories of conformity (distortion of perception/judgment/action), the group-size threshold, and the dissenter effect. https://doi.org/10.1038/scientificamerican1155-31
- Asch, S. E. (1956). "Studies of Independence and Conformity: I. A Minority of One Against a Unanimous Majority." *Psychological Monographs: General and Applied*, 70(9), 1–70. The full monograph documentation. https://doi.org/10.1037/h0093718
- Sherif, M. (1935). "A Study of Some Social Factors in Perception." *Archives of Psychology*, 27(187), 1–60. The autokinetic-effect experiments; the canonical demonstration that under ambiguity, group norms substitute for objective information. The historical precursor to the Asch line.
- Milgram, S., Bickman, L., & Berkowitz, L. (1969). "Note on the Drawing Power of Crowds of Different Size." *Journal of Personality and Social Psychology*, 13(2), 79–82. The New York City sidewalk field experiment: one confederate looking up at a building draws ~4% of passers-by, fifteen confederates draw ~86%. https://doi.org/10.1037/h0028070
- Cialdini, R. B. (1984; 2007 rev.). *Influence: The Psychology of Persuasion*. Harper Business. Chapter 4 is the canonical applied treatment of social proof, including the uncertainty and similarity amplifiers. ISBN 978-0061241895.
- Bikhchandani, S., Hirshleifer, D., & Welch, I. (1992). "A Theory of Fads, Fashion, Custom, and Cultural Change as Informational Cascades." *Journal of Political Economy*, 100(5), 992–1026. The formal model of information cascades — how rational agents looking at the choices of earlier agents can produce socially-proof-style mass behavior even when each individual's private information would have led to a different conclusion. https://doi.org/10.1086/261849
- Salganik, M. J., Dodds, P. S., & Watts, D. J. (2006). "Experimental Study of Inequality and Unpredictability in an Artificial Cultural Market." *Science*, 311(5762), 854–856. The Music Lab experiment: 14,000+ subjects shown otherwise-identical songs converged on radically different "winners" depending on the social-proof signals they were shown — empirical demonstration that the consensus a market produces is often determined by social-proof dynamics rather than underlying quality. https://doi.org/10.1126/science.1121066
- Gartner (2024). "Gartner Predicts 30% of Generative AI Projects Will Be Abandoned Aft

examples/enterprise-ai-copilot-fomo-procurement-2024-2026.md

# Method in Action: Enterprise AI Copilot FOMO Procurement (2024–2026)

> *Example for the [social-proof](../SKILL.md) skill.*

Between the launch of ChatGPT in late 2022 and 2026, generative AI moved from novelty to boardroom mandate. By 2024–2025 a recognizable pattern had set in across large organizations: an executive would say some version of *"every competitor is deploying AI copilots — we can't be the ones left behind,"* and a procurement cycle would begin. The dominant justification for buying was not a measured business case; it was the observation that **everyone else was buying**. This is a textbook social-proof failure mode — consensus behavior substituting for independent evidence — and it is worth walking through this skill's own Process, because the same pattern recurs in every hype cycle.

The pattern was widely documented. Industry surveys and analyst commentary through 2024–2025 repeatedly noted a gap between adoption rates and realized returns: adoption of generative AI rose sharply, while a large share of organizations reported difficulty demonstrating financial return from their AI initiatives. A frequently discussed data point was a 2024 Gartner prediction that a substantial share of generative-AI projects would be abandoned after proof-of-concept by the end of 2025, citing poor data quality, unclear business value, escalating costs, and inadequate risk controls. The *phenomenon* — FOMO-driven procurement outrunning validated ROI — was well established before this analysis. Let us run it through the eight Process steps.

## 1. Name the consensus precisely

The default framing — *"everyone is deploying AI copilots"* — is exactly the vague consensus the skill warns against. It cannot be analyzed. Sharpen it:

- Not "everyone is deploying copilots" but: *"three named direct competitors announced Microsoft 365 Copilot or a comparable assistant rollout in press releases and earnings calls, and our board saw those announcements."*
- Distinguish an **announced pilot** ("we are exploring generative AI") from a **validated, ROI-positive production deployment**. Public announcements are overwhelmingly the former. The consensus you can actually verify is a consensus of *announcements*, not a consensus of *proven results*.

Once named precisely, most of the perceived consensus evaporates: what "everyone" has done is issue a press release and stand up a pilot — not demonstrate return.

## 2. Identify consensus-makers

Who exactly is the consensus, how many, and how similar to you on dimensions relevant to *whether the tool will pay off*?

- **Vendors and their platform partners** have direct commercial incentive to project a "everyone is adopting" narrative.
- **Competitors' investor-relations announcements** are similar to you in industry but are optimizing for a stock-price and talent-signaling audience, not for disclosing pilot failure rates.
- **Analysts and the trade press** amplify the loud adopters; the companies that quietly ran

examples/solomon-asch-conformity-experiments-swarthmore-1951-1956.md

# Method in Action: Solomon Asch's Conformity Experiments, Swarthmore, 1951–1956

> *Example for the [social-proof](../SKILL.md) skill.*

The empirical foundation of modern social-proof theory rests on a series of experiments conducted by **Solomon Asch** at Swarthmore College between 1951 and 1956. The setup was almost embarrassingly simple. Yet the result it produced has appeared in every introductory social-psychology textbook for the past seventy years, and was selected by the American Psychological Association as one of the twenty most important studies of the 20th century.

Asch recruited male college students under the cover story that he was running a "visual perception experiment." Each session brought seven or nine people into a room and seated them around a table. The participants were shown a pair of cards, one displaying a "standard" vertical line, the other displaying three "comparison" vertical lines labeled A, B, and C. One of the comparison lines was the same length as the standard; the other two differed visibly. The participants' task was to say aloud, in the order they were seated, which comparison line matched the standard.

In the control condition — subjects making the judgment alone — the task was trivially easy. Across baseline trials, error rates were under 1%. Anyone with normal vision could see which line matched.

What the subjects did not know was that, in the experimental condition, every person in the room except one was a **confederate**. The confederates had been instructed in advance to give the same wrong answer on critical trials. The single real subject was seated near the end of the line, so that he heard the others' answers before giving his own. There were 18 trials total; on 12 of them ("critical trials"), the confederates gave a unanimous wrong answer. On the other 6 trials, they answered correctly, so the situation would not feel obviously rigged.

The question Asch wanted to answer was: given a perceptual judgment so easy that error rates are under 1% in isolation, **how often will a subject publicly endorse a clearly wrong answer simply because everyone else in the room has done so?**

The empirical result, summarized in Asch's 1955 *Scientific American* article and his 1956 monograph in *Psychological Monographs*, was striking:

> "Whereas in ordinary circumstances individuals matching the lines will make mistakes less than 1 per cent of the time, under group pressure the minority subjects swung to acceptance of the misleading majority's wrong judgments in 36.8 per cent of the selections."

— Asch, S. E. (1955). "Opinions and Social Pressure." *Scientific American*, 193(5), p. 33.

Across all critical trials, naïve subjects gave the wrong answer matching the group's wrong answer approximately one third of the time. Looking at individuals rather than trials, **about 75% of subjects conformed at least once during the experiment, and about 25% never conformed**. Subjects varied: some conformed on nearly

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