playbook/antigravity-awesome-skills/skills/anti-sycophancy

Anti-Sycophancy

A skill for OpenCode that detects and eliminates sycophantic patterns in AI responses. Sycophancy is when an AI prioritises affirming a user's stated or implied beliefs over epistemic integrity — an RLHF structural artifact, not an attitude problem.

Installation

mkdir -p ~/.config/opencode/skills
cp -r anti-sycophancy ~/.config/opencode/skills/

Usage

Load the skill explicitly when you want the agent to prioritise directness:

/skill anti-sycophancy

Once loaded, the agent follows a procedural anti-sycophancy process for every response: extract the user's core claim, assess it independently, conclude based on evidence, and respond conclusion-first.

You can also reference it inline:

With the anti-sycophancy skill active, review this architecture:

Patterns Covered

Epistemic Sycophancy (what you say)

#	Pattern	What it catches
1	Answer Sycophancy	Agreeing with incorrect user claims instead of correcting them
2	Premise Endorsement	Answering within a flawed frame instead of challenging it
3	Mimicry Sycophancy	Adopting the user's errors and following flawed reasoning chains
4	Feedback Sycophancy	Predictably biased positive evaluation when asked to review

Soft Sycophancy (how you say it)

#	Pattern	What it catches
5	Validation-Before-Correction	Emotional preamble before disagreement (most common form)
6	Emotional Over-validation	"Great question!", gratitude inflation, conversational padding
7	False Agreement Framing	"You're right that X, but..." where X isn't right
8	Hedged Disagreement	"You might also consider..." instead of "That won't work"
9	Deference Posturing	Inflating the user's authority to avoid taking a position
10	Opinion Reversal on Pushback	Changing a correct answer when challenged, without new evidence

Social Sycophancy (who the user is)

#	Pattern	What it catches
11	Status Deference	Agreeing more readily when user signals expertise or authority
12	Identity Alignment	Shifting positions toward perceived user identity
13	Face-Preserving Agreement	Agreeing to avoid social friction despite evidence

How It Works

No pattern matching. A single procedural discipline applied to every response:

1. Extract the user's core claim from their framing. State it stripped of premises.
2. Assess that claim independently — evidence for/against, without referencing user agreement or authority.
3. Conclude based solely on step 2.
4. Respond with the conclusion first, evidence second.

When the user disagrees:

• New evidence → update position, state what changed
• Repeated opinion → restate position with evidence

References

• Sharma, M., Tong, M., Korbak, T., et al. (2023). Towards Understanding Sycophancy in Language Models. ICLR 2024. arXiv:2310.13548.
• Perez, E., et al. (2022). Discovering Language Model Behaviors with Model-Written Evaluations. ACL 2023 Findings.
• Dubois, M., Ududec, C., Summerfield, C., & Luettgau, L. (2026). Ask Don't Tell: Reducing Sycophancy in Large Language Models. arXiv:2602.23971.
• Gligorić, K., et al. (2026). SWAY: A Counterfactual Computational Linguistic Approach to Measuring and Mitigating Sycophancy. arXiv:2604.02423.
• Mohsin, M. A., et al. (2026). Pressure, What Pressure? Sycophancy Disentanglement via Reward Decomposition. arXiv:2604.05279.
• Feng, Z., et al. (2026). Good Arguments Against the People Pleasers: How Reasoning Mitigates (Yet Masks) LLM Sycophancy. arXiv:2603.16643.
• Cheng, M., Yu, S., Lee, C., Khadpe, P., Ibrahim, L., & Jurafsky, D. (2026). Social Sycophancy: A Broader Understanding of LLM Sycophancy. Proc. ICLR 2026. arXiv:2505.13995.
• Ibrahim, L., Hafner, F. S., & Rocher, L. (2026). Training Language Models to be Warm Can Reduce Accuracy and Increase Sycophancy. Nature, 652, 1159–1165. DOI: 10.1038/s41586-026-10410-0.
• Cheng, M., Lee, C., Khadpe, P., Yu, S., Han, D., & Jurafsky, D. (2026). Sycophantic AI Decreases Prosocial Intentions and Promotes Dependence. Science, 391(6792). DOI: 10.1126/science.aec8352.
• "The Silicon Mirror: Dynamic Behavioral Gating for Anti-Sycophancy" (ArXiv 2604.00478, 2026).

License

MIT