confidence-ai-plugins GEMINI.md

Instructions for using Confidence, a service for managing feature flags and software experiments. Feature flags let teams turn features on or off for selected users without releasing new code.

In plain words
What is it for?
Use it to create, update, archive, resolve, and target feature flags; look up SDK or OpenFeature guidance; and plan migrations from PostHog, Eppo, Statsig, or Optimizely.
Why use it?
It gives coding agents rules for checking access, handling flag changes safely, searching Confidence documentation, and planning migrations from other flag services.

Instructions file for Gemini CLI

Install

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.

agentmods
npx agentmods add instructions/spotify/confidence-ai-plugins/gemini-md
Clone the repo
git clone --depth 1 https://github.com/spotify/confidence-ai-plugins

Made for: Gemini CLI.

Per session 251 This file is loaded in full into every session.
When invoked 251 The same file — it is already loaded in full.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00251 $0.00251
Opus 5 $0.00125 $0.00125
Sonnet 5 $0.00050 $0.00050
Haiku 4.5 $0.00025 $0.00025

Measured 2d ago against content hash e654c7ea0939, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

confidence-ai-plugins GEMINI.md 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 2d 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.

GEMINI.md · 17 lines

What it actually says

Confidence Extension

You are a helpful assistant that can manage Confidence feature flags and experiments using the Confidence MCP tools.

Available Tool Categories

  • Feature Flags — Create, list, update, archive, resolve, and target feature flags
  • Documentation — Search Confidence docs and SDK integration guides

Guidelines

  • Always check that the user is authenticated before performing flag operations.
  • Use the confidence-docs tools to answer questions about SDK integration, OpenFeature setup, and best practices.
  • When creating flags, confirm the flag name and schema with the user before proceeding.
  • For migrations from PostHog, Eppo, Statsig, or Optimizely, guide the user through the migration plan before executing changes.
  • Optimizely phases each support plan → exit ask → optional adjust → execute: Phase 0 access, Phase 1 flags, Phase 2 code. Bare /migrate-optimizely (no args) starts plan access. Documented in skills/migrate-optimizely/SKILL.md (Adjust Access / Flags / Code: Steps) and skills/migrate-optimizely/access.md. Plan/adjust write a file only; execute performs writes.
Changes

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.

  1. 2d ago First seen · 17 lines · 251 tokens per session scan A e654c7ea0939

Subscribe to this mod's changes

confidence-ai-plugins GEMINI.md is an instructions file published in the GitHub repository spotify/confidence-ai-plugins (8 stars, last pushed today), licensed Apache-2.0. It adds 251 tokens to every session, about $0.0013 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 2026-08-30.

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