How the AI Agent Compound Health Score (AI-CHS) synthesises public sentiment, evidence signals, and source-weighted discourse data across peptide research compounds and the hard limits of what an AI-derived score can and cannot tell you.
The AI Agent Compound Health Score (AI-CHS) (CHS-AI) is a multi-step inference pipeline that runs CHS-AI Agent against a structured query template for each compound, producing two independent scores: an Evidence Score derived from published research quality signals, and a Sentiment Score derived from public discourse analysis across four weighted source channels.
Each pipeline run is scoped to a single compound and runs in parallel across all 44 profiles. Output is validated against a confidence threshold; runs that fail to meet the minimum token window are flagged and re-queued.
The agent draws on four source channel types from the CHS-AI Agent model's training corpus. Weights are calibrated to reflect genuine user experience signal rather than promotional volume — community forums carry the highest weight because they represent the largest pool of authentic first-person discussion.
| Channel | Sources Included | Weight | Signal Type |
|---|---|---|---|
| Community Forums | r/Peptides, r/Nootropics, r/Biohacking, r/PeptideScience, Longecity, PeptideForum.com | 40% | First-person user experience |
| Podcasts & Video | Huberman Lab, The Joe Rogan Experience, The Peter Attia Drive, FoundMyFitness, Ben Greenfield Life, SuperHuman Radio | 25% | Expert and influencer commentary |
| Biohacker Blogs | Independent self-experimenters, longevity newsletters, protocol write-ups, personal blogs | 20% | Documented protocol outcomes |
| Medical & Mainstream Press | STAT News, Undark, BBC Health, New York Times Health, peer-reviewed lay summaries, WWD, Forbes Health | 15% | Institutional and journalistic coverage |
Why medical press receives only 15%: Institutional skepticism of gray-market peptides is well-founded but already reflected in the ICPS Evidence Score. Including it at full weight in the sentiment score would double-count the same signal. Medical press sentiment is a different question from clinical evidence quality.
Each discourse unit sampled by the agent is assigned to one of four classifications. Neutral units are excluded from the final ratio; the score reflects: of all mentions with a clear sentiment signal, what proportion is positive?
Evidence %, Sentiment %, and ICPS Effective Score for all compounds in the CompoundProfile library. Click any column header to sort. Filter by category using the buttons below.
| Compound | Category | Evidence % | Sentiment % | ICPS Score | Status |
|---|
The Sentiment Score has meaningful constraints that must be understood before it informs any decision. These are not caveats added for legal protection — they are genuine epistemic limits of the method.
Training data cutoff May 2025. Sentiment shifts after that date — regulatory events, new adverse reports, updated clinical findings, viral coverage — are not reflected. The Run Update button simulates a re-query but does not change the underlying model data.
Promotional content may have entered training data and can inflate positive sentiment for commercially marketed compounds. The source weighting system partially mitigates this, but it cannot eliminate it entirely. Compounds with very active commercial marketing should be read with this in mind.
Sentiment is not efficacy. A compound can score 85% sentiment with 22% evidence — that gap is meaningful data, not noise. The score specifically measures what people say and believe, not what controlled research has established. Always read the ICPS Evidence Score alongside the Sentiment Score.
The two-score model exists precisely because these signals diverge. Where they align (high evidence, high sentiment) you get compounds like GLP-1/Semaglutide. Where they diverge most (low evidence, high sentiment) you get the clearest cases for user caution — such as IGF-LR3, MOTS-C, or 5-Amino-1MQ.
Each score run is tagged with the CHS-AI Agent model version, training data cutoff, and run timestamp. As new model versions with updated training data become available, scores will be recalculated and the version log updated on each profile page.
| Field | Current Value |
|---|---|
| Model | chs-ai-agent-v4.6 |
| Training Data Cutoff | May 2025 |
| Last Full Run | August 4, 2026 · 09:14 UTC |
| Compounds Scored | 44 of 44 |
| Failed Runs | 0 |
| Low-Volume Flags | 8 compounds (under 100 tracked mentions) |
| Run ID | chs_20260804_full_v4 |