launchdarkly-flag-targeting

launchdarkly-flag-targeting is a skill for Claude Code, Codex from Bilal140202/the-lord-of-the-skills. It costs 67 tokens per session (2,065 once invoked), scanned A, original, MIT.

A workflow for controlling which users or environments receive a LaunchDarkly feature flag. It supports switches, percentage-based rollouts, rules, individual users, and copying settings between environments.

In plain words
What is it for?
Use it to turn a flag on or off, release it to a percentage of users, add targeting rules, target individuals, or copy targeting between environments.
Why use it?
It avoids changing targeting without first checking the flag's current state and verifies the resulting configuration.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to turn a flag on or off, release it to a percentage of users, add targeting rules, target individuals, or copy targeting between environments.

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Install with agentmods
npx agentmods add skills/bilal140202/the-lord-of-the-skills/feature-flags-launchdarkly-flag-targeting
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.

Any agent
npx skills add Bilal140202/the-lord-of-the-skills --skill feature-flags-launchdarkly-flag-targeting
Clone the repo
git clone --depth 1 https://github.com/Bilal140202/the-lord-of-the-skills

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for launchdarkly-flag-targeting

README.md
[![agentmods](https://agentmods.dev/badge/skills/bilal140202/the-lord-of-the-skills/feature-flags-launchdarkly-flag-targeting/github.svg)](https://agentmods.dev/skills/bilal140202/the-lord-of-the-skills/feature-flags-launchdarkly-flag-targeting)
Your own site
<a href="https://agentmods.dev/skills/bilal140202/the-lord-of-the-skills/feature-flags-launchdarkly-flag-targeting"><img src="https://agentmods.dev/badge/skills/bilal140202/the-lord-of-the-skills/feature-flags-launchdarkly-flag-targeting/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.

agentmods 80×15 button for launchdarkly-flag-targeting

Your own site · 80×15
<a href="https://agentmods.dev/skills/bilal140202/the-lord-of-the-skills/feature-flags-launchdarkly-flag-targeting"><img src="https://agentmods.dev/badge/skills/bilal140202/the-lord-of-the-skills/feature-flags-launchdarkly-flag-targeting.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 67 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,065 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
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 5.1 $0.00067 $0.02065
Opus 5 $0.00034 $0.01033
Sonnet 5 $0.00013 $0.00413
Haiku 4.5 $0.00007 $0.00206

Measured 6d ago against content hash 3a880adb69cd, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

launchdarkly-flag-targeting 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 6d 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.

skills/gondor/claude-code/LaunchDarkly__agent-skills/feature-flags-launchdarkly-flag-targeting-SKILL.md · 137 lines

How it starts

The opening of the file, as written. The whole thing — 137 lines — stays where its author put it; the contents beside it link to each section on GitHub.

LaunchDarkly Flag Targeting & Rollout

You're using a skill that will guide you through changing who sees what for a feature flag. Your job is to understand the current state of the flag, figure out the right targeting approach for what the user wants, make the changes safely, and verify the resulting state.

Prerequisites

This skill requires the remotely hosted LaunchDarkly MCP server to be configured in your environment.

Required MCP tools:

  • get-flag: understand current state before making changes
  • toggle-flag: turn targeting on or off for a flag in an environment
  • update-rollout: change the default rule (fallthrough) variation or percentage rollout
  • update-targeting-rules: add, remove, or modify custom targeting rules
  • update-individual-targets: add or remove specific users/contexts from individual targeting

Optional MCP tools:

  • copy-flag-config: copy targeting configuration from one environment to another
  • create-approval-request: create an approval request when direct changes are blocked
  • list-approval-requests: check on pending approval requests for a flag
  • apply-approval-request: apply an already-approved approval request

Core Concept: Evaluation Order

Before making any targeting changes, understand how LaunchDarkly evaluates flags. This determines what your changes actually do:

  1. Flag is OFF -> Serve the offVariation to everyone. Nothing else matters.
  2. Individual targets -> If the context matches a specific target list, serve that variation. Highest priority.
  3. Custom rules -> Evaluate rules top-to-bottom. First matching rule wins.
  4. Default rule (fallthrough) -> If nothing else matched, serve this variation or rollout.

This means: if you add a targeting rule but the flag is OFF, nobody sees the change. If you set a percentage rollout on the default rule but there's an individual target, that targeted user bypasses the rollout.

Workflow

Step 1: Understand Current State

Before changing anything, check what's already configured.

Read the full file on GitHub · 137 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. 6d ago First seen · 137 lines · 67 tokens per session scan A 3a880adb69cd

Subscribe to this mod's changes

launchdarkly-flag-targeting is a skill published in the GitHub repository Bilal140202/the-lord-of-the-skills (4 stars, last pushed 6d ago), licensed MIT. It adds 67 tokens to every session and 2,065 once invoked, about $0.0003 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 2026-09-06.

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