What is configs-targeting?

A workflow for deciding which AI configuration alternative different users or application contexts receive. Rules can use individual users, groups called segments, attributes, or percentage rollouts.

Use it to direct selected users to an alternative, split traffic by percentage, target groups or attributes, and set fallback behavior.

It removes the need to hard-code who receives each AI setup and supports gradual or guarded changes. Targeting rules are evaluated in a defined order.

configs-targeting — Skill for Claude Code and Codex by Bilal140202

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 agentcontrol-configs-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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/bilal140202/the-lord-of-the-skills/agentcontrol-configs-targeting/github.svg)](https://agentmods.dev/skills/bilal140202/the-lord-of-the-skills/agentcontrol-configs-targeting)

Your own site

<a href="https://agentmods.dev/skills/bilal140202/the-lord-of-the-skills/agentcontrol-configs-targeting"><img src="https://agentmods.dev/badge/skills/bilal140202/the-lord-of-the-skills/agentcontrol-configs-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.

Your own site · 80×15

<a href="https://agentmods.dev/skills/bilal140202/the-lord-of-the-skills/agentcontrol-configs-targeting"><img src="https://agentmods.dev/badge/skills/bilal140202/the-lord-of-the-skills/agentcontrol-configs-targeting.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 34 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 3,855 The whole file, excluding the scripts and references it only reads on demand.

Security scan B 2 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.

Model	Per session	Once invoked
Fable 5.1	$0.00034	$0.03855
Opus 5	$0.00017	$0.01928
Sonnet 5	$0.00007	$0.00771
Haiku 4.5	$0.00003	$0.00385

Measured 6d ago against content hash 53efb820653b, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade B, and why

configs-targeting scanned grade B with 2 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.

Reads agent configuration directoriesmediumAgent snooping

.claude/, .codex/, .gemini/ hold keys, settings and other credentials a mod has no legitimate need for.

2. **Check MCP config** - Claude: `~/.claude/config.json` -> `mcpServers.launchdarkly.env.LAUNCHDARKLY_API_KEY`

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

curl -X GET "https://app.launchdarkly.com/api/v2/projects/{projectKey}/ai-configs/{configKey}/targeting" \

skills/gondor/claude-code/LaunchDarkly__agent-skills/agentcontrol-configs-targeting-SKILL.md · 508 lines

How it starts

The opening of the file, as written. The whole thing — 508 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Config Targeting

Configure targeting rules for configs to control which variations serve to different contexts. Works the same for both completion and agent mode.

Prerequisites

LaunchDarkly account with AgentControl enabled
API access token with write permissions
Project key and environment key
Existing config with variations (use configs-create skill)

API Key Detection

Check environment variables - LAUNCHDARKLY_API_KEY, LAUNCHDARKLY_API_TOKEN, LD_API_KEY
Check MCP config - Claude: ~/.claude/config.json -> mcpServers.launchdarkly.env.LAUNCHDARKLY_API_KEY
Prompt user - Only if detection fails

Core Concepts

Evaluation Order

Targeting rules evaluate in this order (same as feature flags):

Individual targets - Specific context keys (highest priority)
Segment rules - Pre-defined segments
Custom rules - Attribute-based conditions (evaluated in order)
Default rule - Fallthrough for all others
Off variation - When targeting is disabled

Semantic Patch API

config targeting uses semantic patch instructions:

PATCH /api/v2/projects/{projectKey}/ai-configs/{configKey}/targeting
Content-Type: application/json; domain-model=launchdarkly.semanticpatch

Key Concepts

variationId: UUIDs, not keys. Always fetch targeting first to get IDs.
Weights: Thousandths (50000 = 50%, 100000 = 100%)
Clause logic: Multiple clauses = AND, multiple values = OR
Null attributes: Rules with null/missing attributes are skipped

Workflow

Step 1: Get Targeting (with Variation IDs)

curl -X GET "https://app.launchdarkly.com/api/v2/projects/{projectKey}/ai-configs/{configKey}/targeting" \
  -H "Authorization: {api_token}" \
  -H "LD-API-Version: beta"

Response includes variations array with _id (UUID) for each variation.

Step 2: Edit the Default Rule

Edit the default rule to serve the variation you created.

Important: The turnTargetingOn instruction does not work for configs. Use updateFallthroughVariationOrRollout instead.

Read the full file on GitHub · 508 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.

6d ago First seen · 508 lines · 34 tokens per session scan B 53efb820653b

Subscribe to this mod's changes

configs-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 34 tokens to every session and 3,855 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it B with 2 findings (reads agent configuration directories, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-06.

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