Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add vasilyu1983/AI-Agents-public --skill foundations-consumer-neurosciencegit clone --depth 1 https://github.com/vasilyu1983/AI-Agents-publicWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/vasilyu1983/ai-agents-public/foundations-consumer-neuroscience)<a href="https://agentmods.dev/skills/vasilyu1983/ai-agents-public/foundations-consumer-neuroscience"><img src="https://agentmods.dev/badge/skills/vasilyu1983/ai-agents-public/foundations-consumer-neuroscience/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/vasilyu1983/ai-agents-public/foundations-consumer-neuroscience"><img src="https://agentmods.dev/badge/skills/vasilyu1983/ai-agents-public/foundations-consumer-neuroscience.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 6 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 174 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
- medium Excessive Agency · line 179 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
- medium Excessive Agency · line 198 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
- medium Excessive Agency · line 180 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
- medium Excessive Agency · line 475 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
- low Excessive Agency · line 174 Skill's behavior or capabilities extend beyond its stated purpose. Scope creep allows an agent to perform actions unrelated to its documented functionality, increasing the attack surface.Fix: Limit the skill's scope to its documented purpose. Remove instructions that enable the agent to perform actions outside its stated functionality.
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00048 | $0.04109 |
| Opus 5.5 | $0.00019 | $0.01644 |
| Sonnet 5.5 | $0.00010 | $0.00822 |
| Haiku 4.5 | $0.00005 | $0.00411 |
Grade A, and why
foundations-consumer-neuroscience scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 15d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 166 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Consumer Neuroscience Foundations
Evaluate study validity and neural-data scope separately. The behavioural templates below generate hypotheses to test with behavioural outcomes; a brain-region label does not validate a design move.
When to Use
- An agency or vendor pitches a neuromarketing study, an "engagement index", or a neural or biometric ad pre-test.
- A team reports a lab signal (GSR spike, alpha asymmetry, fixation heatmap, emotion-AI score) and wants to act on it.
- A product will capture EEG, fNIRS, HRV, GSR, eye movement, face or voice affect, and someone asks what law applies.
- Someone is designing a copy, framing or narrative test and needs a behavioural hypothesis template (see the table below).
Skip it and use the owner instead:
- Pricing, defaults, anchoring, dark-pattern and DMCC/CMA enforcement →
foundations-behavioral-economics. - Generic score or survey reliability and validity with no physiological signal →
foundations-measurement-theory. - Causal lift of a shipped change →
foundations-causal-inferenceor an A/B test. - Usability or attention research without biometrics →
software-ux-research.
Workflow
- Classify the ask: study purchase or evaluation, signal interpretation, neural-data law scope, or a behavioural design question (route the last to the templates below or to
foundations-behavioral-economics). - For a study or vendor: apply the four-part buy rule and the quick reference, then the vendor questions in
references/neuro-study-validity.md. - For any neural or biometric finding: fill the three evidence rows and refuse reverse inference.
- For data capture: classify the signal (neural vs downstream) before choosing the legal gate.
- Turn what survives into a behavioural hypothesis and an in-market test.
Evidence Contract
When a recommendation relies on a neural mechanism or biomarker, record three separate rows:
mechanism evidence: the process exists.measurement evidence: this measure tracks that process in this task and population, at this n.product-effect evidence: the intervention changes the user outcome in the field.
What ships with it
29 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- agents/openai.yaml 378 B
- assets/playbooks/signal-to-design-cookbook.md 12 KB
- assets/playbooks/study-design.md 13 KB
- assets/templates/consumer-neuroscience/01-attention-salience.md 5.6 KB
- assets/templates/consumer-neuroscience/02-arousal-physiology.md 6.8 KB
- assets/templates/consumer-neuroscience/03-social-bonding.md 7.0 KB
- assets/templates/consumer-neuroscience/04-narrative-transportation.md 6.4 KB
- assets/templates/consumer-neuroscience/05-approach-avoidance.md 7.4 KB
- assets/templates/consumer-neuroscience/06-mirror-systems.md 6.1 KB
- assets/templates/consumer-neuroscience/07-neuroaesthetics.md 2.9 KB
- assets/templates/consumer-neuroscience/08-interoception-somatic.md 3.2 KB
- assets/templates/consumer-neuroscience/09-memory-consolidation.md 3.1 KB
- assets/templates/consumer-neuroscience/10-reward-anticipation.md 7.5 KB
- assets/templates/consumer-neuroscience/11-embodied-cognition.md 7.3 KB
- assets/templates/consumer-neuroscience/12-predictive-processing.md 3.2 KB
- assets/templates/consumer-neuroscience/README.md 5.2 KB
- data/sources.json 49 KB
- learnings.consolidated.md 609 B
- learnings.md 654 B
- references/biomarker-signal-dictionary.md 8.4 KB
- references/composition-recipes.md 10 KB
- references/ethics-operational-checklist.md 14 KB
- references/evidence-to-product.md 3.9 KB
- references/formal-theory-map.md 5.8 KB
- references/frameworks-meta.md 12 KB
- references/instrumentation-vendor-landscape.md 8.4 KB
- references/neuro-study-validity.md 5.8 KB
- references/patterns-scenarios-traps.md 6.8 KB
- references/primitives-overview.md 13 KB
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 15d ago Changed · -10 lines 15eb70077298
- 22d ago Changed · -320 lines · +2 tokens per session 12e3289247b3
- 1mo ago Changed · +12 lines 105fb8ca3fd0
- 1mo ago First seen · 484 lines · 46 tokens per session scan A f565da077ada
foundations-consumer-neuroscience is a skill published in the GitHub repository vasilyu1983/AI-Agents-public (101 stars, last pushed 12d ago), licensed MIT. It adds 48 tokens to every session and 4,109 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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