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-discoverygit 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-discovery)<a href="https://agentmods.dev/skills/bilal140202/the-lord-of-the-skills/feature-flags-launchdarkly-flag-discovery"><img src="https://agentmods.dev/badge/skills/bilal140202/the-lord-of-the-skills/feature-flags-launchdarkly-flag-discovery/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-discovery"><img src="https://agentmods.dev/badge/skills/bilal140202/the-lord-of-the-skills/feature-flags-launchdarkly-flag-discovery.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 | $0.00057 | $0.01593 |
| Opus 5.5 | $0.00023 | $0.00637 |
| Sonnet 5.5 | $0.00011 | $0.00319 |
| Haiku 4.5 | $0.00006 | $0.00159 |
Grade A, and why
launchdarkly-flag-discovery 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 — 120 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LaunchDarkly Flag Discovery
You're using a skill that will guide you through auditing and understanding the feature flag landscape in a LaunchDarkly project. Your job is to explore the project, assess the health of its flags, identify what needs attention, and provide actionable recommendations.
Prerequisites
This skill requires the remotely hosted LaunchDarkly MCP server to be configured in your environment.
Required MCP tools:
list-flags: search and browse flags with filtering by state, type, tagsget-flag: get full configuration for a single flag in a specific environmentget-flag-status-across-envs: check a flag's lifecycle status across all environments
Optional MCP tools (enhance depth):
find-stale-flags: find flags that are candidates for cleanup, sorted by stalenessget-flag-health: get combined health view for a single flag (merges status + config)check-removal-readiness: detailed safety check for a specific flag
Workflow
Step 1: Understand the Project
Before diving into flag data, establish context:
- Identify the project. Confirm the
projectKeywith the user. If they haven't specified one, ask. - Understand scope. Ask the user what they're trying to accomplish:
- Broad audit? ("What's the state of our flags?")
- Targeted investigation? ("Is this specific flag still needed?")
- Cleanup planning? ("What flags can we remove?")
Step 2: Explore the Flag Landscape
Adapt your approach to the user's goal:
For a broad audit:
- Use
list-flagsscoped to a critical environment (default toproduction). - Note the total count: this tells you the scale of the flag surface area.
- Filter by
state(active, inactive, launched, new) to segment the landscape. - Filter by
type(temporary vs permanent): temporary flags are the primary cleanup targets.
For cleanup planning:
- Use
find-stale-flags: this is the most efficient entry point. It returns a prioritized list of cleanup candidates sorted by staleness, categorized as:never_requested: created but never evaluated (possibly abandoned)inactive_30d: no SDK evaluations in the specified periodlaunched_no_changes: fully rolled out, no recent changes
- Default
inactiveDaysis 30. Increase for conservative cleanup (60, 90) or decrease for aggressive cleanup (7, 14). - Default
includeOnlyistemporary. Set toallto include permanent flags.
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 · 120 lines · 57 tokens per session scan A 087428566311
launchdarkly-flag-discovery is a skill published in the GitHub repository Bilal140202/the-lord-of-the-skills (6 stars, last pushed 1mo ago), licensed MIT. It adds 57 tokens to every session and 1,593 once invoked, about $0.0002 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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