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 skills/pair-systems-inc/goodmem-claude-code-plugin/helpnpx skills add PAIR-Systems-Inc/goodmem-claude-code-plugin --skill helpgit clone --depth 1 https://github.com/PAIR-Systems-Inc/goodmem-claude-code-pluginWhat 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 | $0.00046 | $0.00976 |
| Opus 5 | $0.00023 | $0.00488 |
| Sonnet 5 | $0.00009 | $0.00195 |
| Haiku 4.5 | $0.00005 | $0.00098 |
Grade A, and why
goodmem:help 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 3d 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 — 84 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GoodMem Plugin — Help & Overview
GoodMem is memory infrastructure for AI agents — store, retrieve, and manage memories with vector embeddings, semantic search, and RAG.
Credential Setup
Before calling any GoodMem MCP tool, you MUST ensure credentials are configured. Follow this sequence:
- Try calling
goodmem_system_infoto check if credentials are already set (from environment variables). - If it fails with a connection or auth error, ask the user for:
- Base URL — their GoodMem server address (e.g.,
https://your-server.example.com) - API key — starts with
gm_
- Base URL — their GoodMem server address (e.g.,
- Call
goodmem_configurewith the providedbase_urlandapi_key. - Confirm the connection succeeded before proceeding.
Credentials persist for the entire session. The user can call goodmem_configure again to switch servers.
If the user has set GOODMEM_BASE_URL and GOODMEM_API_KEY environment variables before starting Claude Code, credentials are picked up automatically — no setup needed.
TLS errors? If goodmem_system_info fails with a certificate verification error (e.g., "self-signed certificate", "unable to verify the first certificate", or "CERT_HAS_EXPIRED"), the GoodMem server is using a self-signed or private CA certificate. Tell the user to set one of these environment variables before launching Claude Code:
NODE_EXTRA_CA_CERTS=/path/to/rootCA.pem— adds the CA to the trusted store (recommended)NODE_TLS_REJECT_UNAUTHORIZED=0— disables certificate verification (local dev only)
Available Skills
goodmem:mcp — MCP Tools Reference
Reference for all GoodMem MCP tools. Use this when you need to look up tool names, parameters, or behavior. The MCP tools let you operate GoodMem directly — create embedders, store memories, run retrieval — all via natural language.
goodmem:python — Python SDK Reference
Complete API reference for the goodmem Python package. Use this when writing Python code that integrates with GoodMem. Includes method signatures, convenience shortcuts, model identifiers, and code examples.
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.
- 3d ago First seen · 84 lines · 46 tokens per session scan A cfd0813e023d
goodmem:help is a skill published in the GitHub repository PAIR-Systems-Inc/goodmem-claude-code-plugin (8 stars, last pushed 5d ago), licensed MIT. It adds 46 tokens to every session and 976 once invoked, about $0.0002 per session on Opus 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.
Other skills, from other repositories
agent-platform-rag-engine-management
Manage and query Agent Platform RAG Engine Corpora and retrieve grounded contexts using the Google GenAI SDK. Use when listing RAG corpora or files, inspecting a corpus, retrieving contexts, or generating content grounded in a RAG corpus. Do not use for standard database queries (use SQL/Spanner skills), Google…
9router-embeddings
Generate vector embeddings via 9Router /v1/embeddings using OpenAI / Gemini / Mistral / Voyage / Nvidia / GitHub embedding models for RAG, semantic search, similarity. Use when the user wants embeddings, vectors, RAG, semantic search, or to embed text.
potpie-source-ingestion
Use when the user explicitly asks to ingest, refresh, or deeply understand a repository, PR, issue, ticket, runbook, incident report, document, or web link into Potpie. The harness performs todo-driven discovery, uses local/GitHub/integration tools and read-only subagents when available, builds evidence-backed…
embedding-strategies
Select and optimize embedding models for semantic search and RAG applications. Use when choosing embedding models, implementing chunking strategies, or optimizing embedding quality for specific domains.
generate-rag-dataset
Generate a synthetic evaluation dataset from your RAG knowledge base. Creates diverse Q&A pairs with expected answers and relevant context, ready for LangWatch experiments and platform import. Use when you need test data for your RAG pipeline.
embeddings
Vector embeddings configuration and semantic search.