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Elaboration Likelihood Model (ELM) · asgard-ai-platform | DecimalAI

marketingby asgard-ai-platform

Elaboration Likelihood Model (ELM)

asgard-ai-platform/grad-elm

Source asgard-ai-platform/skills ↗License MIT

Apply the Elaboration Likelihood Model to design persuasion strategies by matching message type to audience elaboration level.

SkillScore91Maintained

Automatic

Marketing & Content pack

Last verified 8/27/2026

What it changes

Passed 20 of 22 test cases with the skill, and 9 of 22 without it.

Without skill41%

With skill91%

Checks it passed that the model alone missed

The response includes the header '### Route Selection:'.
The response includes the context header '## ELM Persuasion Strategy:'.
The response includes the section header '### Audience Elaboration Assessment'.

and 9 more in the full report

11 fixed9 passed either way2 still failing0 broke

Pass-rate lift: +50 pp
Tokens: +19%
Agent turns: 0%
Response time: -12%

Model gemini-3.6-flashMeasured 8/27/2026v120 of 22 cases passed with skill

Tokens, turns and time describe resource use, not a dollar price.

Use this skill

Save it into your project's skills folder. Claude Code picks it up in its next session.

curl -fsSL --create-dirs -o .claude/skills/asgard-ai-platform-grad-elm/SKILL.md https://app.decimal.ai/s/asgard-ai-platform-grad-elm/SKILL.md

Want it pinned and updatable? pip install "decimalai>=0.15", then decimalai skills add asgard-ai-platform-grad-elm, and start Claude Code with claude --plugin-dir "$(git rev-parse --show-toplevel)/.agents"

1 · One-time — install the CLI (docs ↗)

pip install "decimalai>=0.15"

2 · Get the skill — writes .agents/skills/asgard-ai-platform-grad-elm/ and pins it in .decimal/skills.lock

decimalai skills add asgard-ai-platform-grad-elm

No CLI? Get the same file — curl · raw file

curl -fsSL --create-dirs -o .claude/skills/asgard-ai-platform-grad-elm/SKILL.md https://app.decimal.ai/s/asgard-ai-platform-grad-elm/SKILL.md

Raw file: /s/asgard-ai-platform-grad-elm/SKILL.md ↗ · machine-readable summary at /s/asgard-ai-platform-grad-elm.json ↗

Safety checks: 3 of 3 passed

Code scan CleanIntent PassedContent Clean

See it in action

One of 11 test cases the skill fixed. Same model, same prompt, with and without the skill.

1 of 11

Prompt

We are launching a new enterprise security compliance protocol for our software organization. Senior engineering managers care deeply about security risks and have extensive technical background, while junior developers feel overwhelmed by deadlines and lack time to analyze policy details. Please analyze this scenario using the Elaboration Likelihood Model. Formulate an ELM Persuasion Strategy document containing: an Audience Elaboration Assessment table broken down by audience segment (evaluating motivation, ability, and overall elaboration level), the chosen route selection, a Message Design section detailing primary evidence and peripheral cues, a Predicted Outcomes section tracking attitude durability, behavioral impact, and counter-persuasion vulnerability, and a final Risk Mitigation section.

What was checked, without and with the skill
Checked	Without skill	With skill
The response includes the context header '## ELM Persuasion Strategy:'.	Missed	Passed
The response classifies junior developers under low elaboration or peripheral route processing due to high distraction and time constraints.	Passed	Passed
The response prescribes evidence-based technical arguments specifically for senior engineering managers.	Passed	Passed

case-01 · gemini-3.6-flash · 8/27/2026 · answers are capped and redacted

What's inside

The instructions your model reads when the skill loads.

SKILL.md111 linessingle file

Elaboration Likelihood Model (ELM)

Overview

The Elaboration Likelihood Model (Petty & Cacioppo, 1986) proposes that persuasion occurs through two distinct routes depending on the audience's motivation and ability to process a message. The central route relies on careful evaluation of argument quality, producing durable attitude change. The peripheral route relies on heuristic cues (source attractiveness, number of arguments), producing temporary and fragile attitude shifts.

When to Use

Designing marketing, policy, or internal communication strategies for different audience segments
Diagnosing why a well-reasoned message failed (audience lacked motivation/ability to process)
Predicting whether attitude change will persist and resist counter-persuasion
Choosing between investing in argument quality vs. source credibility or presentation style

When NOT to Use

When the goal is behavioral compliance rather than genuine attitude change (use compliance techniques)
For purely emotional appeals where cognitive processing models are insufficient (use affect-as-information)
When the audience has no prior schema on the topic and needs education before persuasion

Assumptions

IRON LAW: Attitude change via the central route is MORE durable
and resistant to counter-persuasion — but requires motivation
AND ability to process. When either is absent, only the
peripheral route is available, and its effects decay.

Key assumptions:

Elaboration exists on a continuum from low to high, not a strict binary
The same variable (e.g., source expertise) can serve as argument, cue, or affect elaboration direction depending on context
Central-route attitudes predict behavior better than peripheral-route attitudes

Methodology

Step 1 — Assess Audience Elaboration Likelihood

Factor	High Elaboration	Low Elaboration
Personal relevance	High (topic matters to them)	Low (distant from self)

Track record

Benchmark

20 / 22

test cases passed, vs 9 / 22 without

Adoption

Users: 0
Times used: 0

Safety and source details

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v4 — 8/3/2026

View 15 scanner checks

live_secretremote_code_executionreverse_shelldata_exfiltrationssrf_metadataagent_snoopinganti_refusaldestructive_commandsobfuscated_unicodeunicode_homoglyphopaque_blobsuspicious_urltool_overreachtrigger_abuseprompt_injection_phrasing

IntentSafe

LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

asgard-ai-platform / skills

grad-elm/SKILL.md

View on GitHub ↗

License MITImported 8/3/2026Version v1

More skills from the same source and nearby in the registry, with their headline benchmark lift.

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See it in action

What's inside

Elaboration Likelihood Model (ELM)

Overview

When to Use

When NOT to Use

Assumptions

Methodology

Step 1 — Assess Audience Elaboration Likelihood

Track record

Benchmark

Adoption

SkillSafety

Source

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