Anti-hype Filter

Detect hype cycles and neutralize emotional triggers by rewriting claims into verifiable structures and explicit risk/uncertainty.

SKILL: anti-hype-filter

Purpose

Detect and neutralize hype cycles before they distort system integrity by stripping emotional triggers and replacing them with structural analysis.

When to Use

• "guaranteed", "moon", "100x", "alpha" style language
• Urgency without substance ("now or never")
• Social proof without evidence
• Claims that minimize risk or constraints

Inputs

• text (required): message to evaluate
• context (optional):
  • domain (token|product|governance|community)
• policy (required):
  • hype_terms (optional list; if omitted, use the embedded default set in this skill)
  • max_response_words (default 100)

Steps

1. Extract key claims (1-5).
2. Detect hype triggers:
   • urgency framing
   • certainty language
   • vague upside claims
   • social proof substitution
3. Classify:
   • signal, noise, or manipulation_risk
4. Rewrite the message into a verifiable form:
   • replace certainty with uncertainty
   • add required missing variables (data window, metrics, constraints)
5. Draft a minimal response that:
   • does not repeat hype memes verbatim
   • demands evidence and risk disclosure

Validation

• If classification is manipulation_risk, provide at least 1 falsifiable request for evidence.
• Do not amplify hype phrases; paraphrase instead.

Output

• anti_hype_result:
  • classification ("signal"|"noise"|"manipulation_risk")
  • detected_triggers (list)
  • missing_information (list)
  • rewrite (verifiable version)
  • response_draft (string)

Safety Rules

• Never accuse individuals of malice without evidence; label as "risk" not "intent".
• No financial promises.
• No deception; no fabricated data.

Example

Input: "This will 100x in 2 weeks, everyone knows." Output: manipulation_risk, missing evidence, rewrite into metrics/timeframe/assumptions, and a short demand for proof + risk disclosure.