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.
npx skills add Bilal140202/the-lord-of-the-skills --skill feature-flags-launchdarkly-flag-targetinggit clone --depth 1 https://github.com/Bilal140202/the-lord-of-the-skillsWrote 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.
[](https://agentmods.dev/skills/bilal140202/the-lord-of-the-skills/feature-flags-launchdarkly-flag-targeting)<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.
<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>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 | $0.00067 | $0.02065 |
| Opus 5.5 | $0.00027 | $0.00826 |
| Sonnet 5.5 | $0.00013 | $0.00413 |
| Haiku 4.5 | $0.00007 | $0.00206 |
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 1mo 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.
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 changestoggle-flag: turn targeting on or off for a flag in an environmentupdate-rollout: change the default rule (fallthrough) variation or percentage rolloutupdate-targeting-rules: add, remove, or modify custom targeting rulesupdate-individual-targets: add or remove specific users/contexts from individual targeting
Optional MCP tools:
copy-flag-config: copy targeting configuration from one environment to anothercreate-approval-request: create an approval request when direct changes are blockedlist-approval-requests: check on pending approval requests for a flagapply-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:
- Flag is OFF -> Serve the
offVariationto everyone. Nothing else matters. - Individual targets -> If the context matches a specific target list, serve that variation. Highest priority.
- Custom rules -> Evaluate rules top-to-bottom. First matching rule wins.
- 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.
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.
- 1mo ago First seen · 137 lines · 67 tokens per session scan A 3a880adb69cd
launchdarkly-flag-targeting is a skill published in the GitHub repository Bilal140202/the-lord-of-the-skills (6 stars, last pushed 1mo ago), licensed MIT. It adds 67 tokens to every session and 2,065 once invoked, about $0.0003 per session on Opus 5.5. 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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