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 deciqAI/knowledge-skills --skill framing-effectgit clone --depth 1 https://github.com/deciqAI/knowledge-skillsWrote 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/deciqai/knowledge-skills/framing-effect)<a href="https://agentmods.dev/skills/deciqai/knowledge-skills/framing-effect"><img src="https://agentmods.dev/badge/skills/deciqai/knowledge-skills/framing-effect/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/deciqai/knowledge-skills/framing-effect"><img src="https://agentmods.dev/badge/skills/deciqai/knowledge-skills/framing-effect.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00104 | $0.01727 |
| Opus 5 | $0.00052 | $0.00864 |
| Sonnet 5 | $0.00021 | $0.00345 |
| Haiku 4.5 | $0.00010 | $0.00173 |
Grade A, and why
framing-effect 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 8d 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 — 121 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Framing Effect
Overview
The framing effect: logically equivalent descriptions of the same decision produce different choices depending on whether outcomes are cast as gains or losses. Frame determines whether the brain enters gain-mode or loss-mode; the choice follows from the mode.
Three types: risky-choice (gain vs. loss on probabilities), attribute ("95% fat free" vs. "5% fat"), goal ("do X to gain Y" vs. "skip X, lose Y"). Real frames often compound all three.
Corrective: force both framings. If the decision is stable across frames, the frame is not driving it. If it flips, inspect why.
Composes with loss-aversion-prospect-theory, anchoring, pricing-strategy, signaling-games, critical-thinking.
When to Use
- Decision presented with strong gain-only or loss-only language
- Statistic shown in one form only (survival rate without mortality, or vice versa)
- Evaluating persuasive communication: ad, political message, medical recommendation, pitch
- Team moving toward a decision whose framing was not chosen neutrally
- You suspect manipulation by frame choice
- Framing a market or capex bet as "visionary investment" vs "bubble/overbuild" (e.g. AI capex, AI valuations, or AI-adoption spend cast as gain vs loss on identical facts)
Not when: the alternative frame is genuinely misleading (not logically equivalent); analysis cost exceeds decision stakes.
Coaching Novices (Adaptive Front Door)
- Engine mode: user has a concrete framed decision → run The Process directly.
- Coach mode: user is unfamiliar → guide step by step.
In Coach mode, respond one step at a time. Each [WAIT] is a hard stop — output only that step's question, then stop.
- One-line: before accepting a framed decision, restate it in the opposite frame — do you choose differently?
- Check fit: if the alternative frame is not equivalent, single-frame may be appropriate.
- Elicit their real case: what's being decided? What gain/loss/do/don't language is in use?
[WAIT — do not advance until user responds]
- Run the audit one step at a time with their input.
[WAIT — do not advance until user responds]
- Close by naming what the frame was doing and what the frame-independent choice is.
[WAIT — do not advance until user responds]
What ships with it
3 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.
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.
- 8d ago First seen · 121 lines · 104 tokens per session scan A 99da998cd24d
framing-effect is a skill published in the GitHub repository deciqAI/knowledge-skills (10 stars, last pushed 10d ago), licensed MIT. It adds 104 tokens to every session and 1,727 once invoked, about $0.0005 per session on Opus 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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