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 agentmods add commands/codebytemirza/linkedin-post-mcp/index-managementgit clone --depth 1 https://github.com/codebytemirza/linkedin-post-mcpWrote 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/commands/codebytemirza/linkedin-post-mcp/index-management)<a href="https://agentmods.dev/commands/codebytemirza/linkedin-post-mcp/index-management"><img src="https://agentmods.dev/badge/commands/codebytemirza/linkedin-post-mcp/index-management.svg" alt="Measured on agentmods" 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 | $0.00000 | $0.00809 |
| Opus 5 | $0.00000 | $0.00404 |
| Sonnet 5 | $0.00000 | $0.00162 |
| Haiku 4.5 | $0.00000 | $0.00081 |
Grade A, and why
index-management 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 2d 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.
This is a copy
100% identical to index-management — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 125 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Index Management
Overview
Create, inspect, and drop search indexes. Wait for indexing to complete after data changes. Indexes automatically track Redis keys matching a specified prefix.
Good For
- Creating indexes over existing or new Redis data
- Inspecting index schema and configuration
- Rebuilding or dropping indexes
- Ensuring data consistency after bulk writes
Examples
Create an Index
import { Redis, s } from "@upstash/redis";
const redis = Redis.fromEnv();
// JSON index with nested schema
const index = await redis.search.createIndex({
name: "products",
prefix: "product:",
dataType: "json",
schema: s.object({
name: s.string(),
price: s.number("F64"),
metadata: s.object({
brand: s.facet(),
tags: s.keyword(),
}),
}),
});
// Hash index (flat schema only)
const hashIndex = await redis.search.createIndex({
name: "sessions",
prefix: "session:",
dataType: "hash",
schema: {
userId: { type: "TEXT" as const },
lastActive: { type: "DATE" as const },
},
});
Create with Options
const index = await redis.search.createIndex({
name: "articles",
prefix: ["article:", "post:"], // multiple prefixes
dataType: "json",
language: "english", // stemming language
skipInitialScan: false, // scan existing keys (default)
existsOk: true, // don't error if index already exists
schema: s.object({
title: s.string(),
body: s.string().noStem(),
publishedAt: s.date().fast(),
}),
});
Get a Reference to an Existing Index
// If you already created the index and just need a reference
const index = redis.search.index({
name: "products",
schema: s.object({
name: s.string(),
price: s.number("F64"),
}),
});
// Without schema (untyped - no filter/select type safety)
const untypedIndex = redis.search.index({ name: "products" });
Describe an Index
const description = await index.describe();
// {
// name: "products",
// dataType: "json",
// prefixes: ["product:"],
// language: "english",
// schema: { name: { type: "TEXT" }, price: { type: "F64", fast: true } }
// }
// Returns null if index doesn't exist
const missing = await redis.search.index({ name: "nonexistent" }).describe();
// null
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.
- 2d ago First seen · 125 lines · 0 tokens per session scan A ed83292d3c41
index-management is a command published in the GitHub repository codebytemirza/linkedin-post-mcp (0 stars, last pushed 22d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 809 tokens. A static security scan graded it A with 0 findings. It is 100% identical to index-management, differing in 0 lines, and is treated as a copy.
Other commands, from other repositories
index-management
Create, inspect, and drop search indexes. Wait for indexing to complete after data changes. Indexes automatically track Redis keys matching a specified prefix.
v-memory-refresh
(Re)index docs/superpowers prose into the local V-memory cache so recall is current. Incremental by file hash; runs fully offline (FTS5, pure stdlib). Optionally enable the semantic lane with a one-time bootstrap. Run it after pulling new docs, or when /v:remember looks stale.
laravel-performance-cache
Add caching to reduce work; use the laravel:performance-caching skill exactly as written.
refresh-cache
Clear a Redis/diskcache namespace by sport or tool.
semantic-caching
Redis semantic caching for LLM applications. Use when caching LLM responses by vector similarity, cutting cost on repeated queries, or building a multi-level cache. Triggers on semantic cache, Redis cache, vector similarity cache, LLM cache, response caching, cache warming, TTL cache.
frappe-cache
Manage Redis cache - clear, monitor, and optimize Frappe cache performance.