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 yeaight7/agent-powerups --skill local-rag-mcpgit clone --depth 1 https://github.com/yeaight7/agent-powerupsWrote 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/yeaight7/agent-powerups/local-rag-mcp)<a href="https://agentmods.dev/skills/yeaight7/agent-powerups/local-rag-mcp"><img src="https://agentmods.dev/badge/skills/yeaight7/agent-powerups/local-rag-mcp/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/yeaight7/agent-powerups/local-rag-mcp"><img src="https://agentmods.dev/badge/skills/yeaight7/agent-powerups/local-rag-mcp.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 | $0.00029 | $0.00703 |
| Opus 5.5 | $0.00012 | $0.00281 |
| Sonnet 5.5 | $0.00006 | $0.00141 |
| Haiku 4.5 | $0.00003 | $0.00070 |
Grade A, and why
local-rag-mcp 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 — 63 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Local RAG MCP
When to use
Use when the task requires semantic search over a local document corpus and an appropriate local RAG MCP server is available. Prefer standard grep/glob for simple pattern matching — RAG adds value for conceptual queries and cross-document synthesis.
Requirements / Checks
- Verify a local RAG MCP server is configured (
apx mcp listor check MCP settings). - Do NOT attempt to install or spin up Docker containers for vector databases without explicit user permission.
- Confirm whether the embedding provider is local or remote — if remote (e.g., OpenAI), warn the user before ingesting sensitive content.
Workflow
-
Identify need — determine whether the query requires semantic retrieval (conceptual, cross-document) vs. standard grep/glob (exact pattern, single file).
-
Check configuration — verify the connection to the local RAG MCP server. If it fails, surface the error rather than falling back silently.
-
Inventory corpus — use status or list tools to see what's already indexed before ingesting anything.
-
Ingest (only if necessary) — ingest only files explicitly approved for this corpus. Include clear source metadata (file path, ingest timestamp). Exclude:
.envfiles, credential files, SSH keys, and files outside the workspace. -
Query strategy:
- Start with the user's exact terms; do not paraphrase into broader concepts.
- Add one specific disambiguating detail if initial results are too broad.
- Keep result limits small first (top 5); expand only if results are insufficient.
-
Expand around hits — if a top result lacks surrounding context, fetch neighboring chunks before drawing conclusions.
-
Synthesize with citations — in your response, distinguish between retrieved evidence (cite source and chunk) and your own inference.
-
Clean up — delete stale or incorrectly ingested sources when requested; do not accumulate unrelated documents.
Tool Interface (illustrative — actual names depend on your server)
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 63 lines · 29 tokens per session scan A c0a03428ff79
local-rag-mcp is a skill published in the GitHub repository yeaight7/agent-powerups (7 stars, last pushed 28d ago), licensed Apache-2.0. It adds 29 tokens to every session and 703 once invoked, about $0.0001 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 2026-08-31.
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