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 stevepridemore/graph-memory --skill graph-findgit clone --depth 1 https://github.com/stevepridemore/graph-memoryWrote 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/stevepridemore/graph-memory/graph-find)<a href="https://agentmods.dev/skills/stevepridemore/graph-memory/graph-find"><img src="https://agentmods.dev/badge/skills/stevepridemore/graph-memory/graph-find/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/stevepridemore/graph-memory/graph-find"><img src="https://agentmods.dev/badge/skills/stevepridemore/graph-memory/graph-find.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 | $0.00036 | $0.00262 |
| Opus 5.5 | $0.00014 | $0.00105 |
| Sonnet 5.5 | $0.00007 | $0.00052 |
| Haiku 4.5 | $0.00004 | $0.00026 |
Grade A, and why
graph-find 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.
What it actually says
The user wants to find specific patterns in the memory graph.
Arguments: $ARGUMENTS
Based on the argument:
contradictions — Call graph_contradictions MCP tool. Show each pair of conflicting facts with their evidence and detection date. Offer to resolve them.
stale — Call graph_entities MCP tool with min_confidence: 0.0, sort_by: "confidence", limit: 20. Show the weakest entities that may need reinforcement or pruning.
orphans — Call graph_entities MCP tool with sort_by: "last_seen", limit: 50. Filter the response for entities where edge_count is 0. Show these disconnected entities.
strongest — Call graph_entities MCP tool with sort_by: "confidence", limit: 20. Show the highest-confidence entities — the core of the knowledge graph.
If no argument is provided, show a brief menu of what's available and ask what they'd like to find.
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 · 22 lines · 36 tokens per session scan A f0fd433a52e2
graph-find is a skill published in the GitHub repository stevepridemore/graph-memory (2 stars, last pushed 7d ago), licensed MIT. It adds 36 tokens to every session and 262 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.
Other skills, from other repositories
ori-memory
Persistent agent memory with learning retrieval. Knowledge graph on markdown files — capture insights, decisions, research, and learnings during work, then retrieve them weeks or months later. Use when knowledge is too valuable to lose but too much to inject into every prompt.
goodmem:mcp
Workflow guide for GoodMem MCP server tools. Use when an agent needs to understand what GoodMem MCP tools are available and how to use them. Parameter signatures are provided by the MCP protocol itself — do not hardcode them here.
goodmem:help
Overview of all available GoodMem skills and what a user needs to get started. Invoke when a user asks what GoodMem skills are available, how to get started, or what they need to set up.
goodmem:java
Use when the user wants to write Java code that interacts with GoodMem - creating embedders, spaces, memories, retrieval, pagination, streaming, or async workflows with the goodmem-java SDK.
goodmem:python
Use when the user wants to write Python code that interacts with GoodMem — creating embedders, spaces, memories, retrieval, managing API keys, etc. Provides the complete SDK reference for the goodmem Python package.
capture-knowledge
A guide for saving useful, reusable knowledge from conversations and documents into a structured personal knowledge base. It separates original source material from concise pages about individual concepts.