feature-flags

A command that creates and manages feature flags: switches that turn parts of an app on or off without releasing new code. It includes database storage, caching, rollout controls, and an admin screen.

In plain words
What is it for?
Use it to create, list, enable, disable, gradually roll out, and remove feature flags across staging or production environments.
Why use it?
It lets you release changes gradually, target specific users, or disable a problematic feature quickly. This reduces the need for emergency code changes or redeployments.

Command

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 commands/alphaaiservice/cortex/feature-flags
Clone the repo
git clone --depth 1 https://github.com/alphaaiservice/cortex
Per session 41 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 5,569 The whole file, excluding the scripts and references it only reads on demand.
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 $0.00041 $0.05569
Opus 5 $0.00020 $0.02785
Sonnet 5 $0.00008 $0.01114
Haiku 4.5 $0.00004 $0.00557

Measured yesterday against content hash 0acadb539355, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

feature-flags 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 yesterday.

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.

commands/feature-flags.md · 654 lines

How it starts

The opening of the file, as written. The whole thing — 654 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Feature Flag System

Action: $ARGUMENTS (default: init)

Parse $ARGUMENTS:

  • init — Generate the complete feature flag system (DB schema, service, API, admin UI)
  • create <flag-name> [--description "..."] [--rollout 0-100] — Create a new feature flag
  • list — Show all flags with status, rollout %, and usage stats
  • toggle <flag-name> [--env staging|production] [--on|--off] — Toggle a flag
  • cleanup — Find and remove stale/fully-rolled-out flags from code
  • No argument = init

Step 0: Detect Project Context

echo "=== Project Type ==="
ls package.json pyproject.toml build.gradle.kts pom.xml 2>/dev/null

echo "=== Backend Language ==="
if [ -f "pyproject.toml" ] || [ -f "requirements.txt" ]; then
  echo "Python/FastAPI detected"
elif [ -f "package.json" ] && grep -q "nestjs" package.json 2>/dev/null; then
  echo "NestJS detected"
elif [ -f "build.gradle.kts" ] || [ -f "pom.xml" ]; then
  echo "Spring Boot detected"
fi

echo "=== Existing Feature Flags ==="
grep -rn "feature.flag\|feature_flag\|FeatureFlag\|FEATURE_" --include="*.py" --include="*.ts" --include="*.java" . 2>/dev/null | grep -v node_modules | head -20

echo "=== Database ==="
grep -rn "mysql\|DATABASE_URL" .env* 2>/dev/null | head -5
grep -rn "redis\|REDIS" .env* 2>/dev/null | head -5

Step 1: Database Schema (Action: init)

MySQL Migration

Generate the feature flags table migration.

Python (Alembic):

"""create feature flags tables

Revision ID: xxxx
"""
from alembic import op
import sqlalchemy as sa
from sqlalchemy.dialects.mysql import JSON

def upgrade() -> None:
    op.create_table(
        'feature_flags',
        sa.Column('id', sa.Integer(), autoincrement=True, nullable=False),
        sa.Column('name', sa.String(100), nullable=False, unique=True, index=True),
        sa.Column('description', sa.Text(), nullable=True),
        sa.Column('enabled', sa.Boolean(), default=False, nullable=False),
        sa.Column('rollout_percentage', sa.Integer(), default=0, nullable=False),
        sa.Column('conditions', JSON, nullable=True),  # {"roles": ["admin"], "user_ids": [1,2,3], "regions": ["IN"]}
        sa.Column('kill_switch', sa.Boolean(), default=False, nullable=False),  # True = force OFF regardless
        sa.Column('stale_after', sa.DateTime(), nullable=True),  # When this flag should be reviewed
        sa.Column('created_by', sa.String(100), nullable=True),
        sa.Column('created_at', sa.DateTime(), server_default=sa.func.now(), nullable=False),
        sa.Column('updated_at', sa.DateTime(), server_default=sa.func.now(), onupdate=sa.func.now(), nullable=False),
        sa.PrimaryKeyConstraint('id'),
    )

    op.create_table(
        'feature_flag_overrides',
        sa.Column('id', sa.Integer(), autoincrement=True, nullable=False),
        sa.Column('flag_id', sa.Integer(), sa.ForeignKey('feature_flags.id', ondelete='CASCADE'), nullable=False),
        sa.Column('user_id', sa.Integer(), nullable=True),
        sa.Column('role', sa.String(50), nullable=True),
        sa.Column('enabled', sa.Boolean(), nullable=False),
        sa.Column('created_at', sa.DateTime(), server_default=sa.func.now(), nullable=False),
        sa.PrimaryKeyConstraint('id'),
        sa.UniqueConstraint('flag_id', 'user_id', name='uq_flag_user'),
    )

    op.create_table(
        'feature_flag_audit_log',
        sa.Column('id', sa.Integer(), autoincrement=True, nullable=False),
        sa.Column('flag_id', sa.Integer(), sa.ForeignKey('feature_flags.id', ondelete='CASCADE'), nullable=False),
        sa.Column('action', sa.String(50), nullable=False),  # created, enabled, disabled, rollout_changed, deleted
        sa.Column('old_value', sa.Text(), nullable=True),
        sa.Column('new_value', sa.Text(), nullable=True),
        sa.Column('changed_by', sa.String(100), nullable=True),
        sa.Column('changed_at', sa.DateTime(), server_default=sa.func.now(), nullable=False),
        sa.PrimaryKeyConstraint('id'),
    )

def downgrade() -> None:
    op.drop_table('feature_flag_audit_log')
    op.drop_table('feature_flag_overrides')
    op.drop_table('feature_flags')

Read the full file on GitHub · 654 lines

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. yesterday First seen · 654 lines · 41 tokens per session scan A 0acadb539355

Subscribe to this mod's changes

feature-flags is a command published in the GitHub repository alphaaiservice/cortex (1 stars, last pushed 25d ago), licensed MIT. It adds 41 tokens to every session and 5,569 once invoked, about $0.0002 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 2026-08-31.