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-askgit 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-ask)<a href="https://agentmods.dev/skills/stevepridemore/graph-memory/graph-ask"><img src="https://agentmods.dev/badge/skills/stevepridemore/graph-memory/graph-ask/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-ask"><img src="https://agentmods.dev/badge/skills/stevepridemore/graph-memory/graph-ask.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 | $0.00043 | $0.01829 |
| Opus 5.5 | $0.00017 | $0.00732 |
| Sonnet 5.5 | $0.00009 | $0.00366 |
| Haiku 4.5 | $0.00004 | $0.00183 |
Grade A, and why
graph-ask 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 — 177 lines — stays where its author put it; the contents beside it link to each section on GitHub.
The user wants to ask a natural language question about the memory graph. You will generate the Cypher query yourself and execute it via the graph_cypher MCP tool.
Arguments: $ARGUMENTS
GRAPH SCHEMA
All entity nodes carry both :Entity and their type label (e.g., :Entity:Person). All entities have: id (STRING), name (STRING), subtype (STRING), confidence (FLOAT), times_mentioned (INTEGER), first_seen (DATETIME), last_seen (DATETIME), source_file (STRING).
Node labels (8 types):
- Person: role, relationship_to_user, organization, email
- Project: status, stack, description, directory, start_date
- Preference: domain, key, value, times_confirmed
- Concept: category, user_expertise, description
- Decision: what, why, context, reversible (BOOLEAN), status, decided_date
- Fact: domain, content, source, verified (BOOLEAN)
- Event: description, event_date (DATETIME), duration, outcome, location, status
- Object: object_type, description, status, url, version
Relationship types (17 total, all have weight FLOAT and last_confirmed DATETIME):
- WORKS_ON (Person->Project): role, since
- PREFERS (Person->Preference): strength
- KNOWS_ABOUT (Person->Concept): depth
- DEPENDS_ON (Project->Project): dependency_type
- USES_TECH (Project->Concept): role
- DECIDED_FOR (Decision->any)
- SUPERSEDES (Decision->Decision): reason, superseded_date
- CONTRADICTS (any->any): description, detected_date, resolved (BOOLEAN), resolution
- RELATED_TO (any->any): relationship_type (similar_to, part_of, enables, impacts, depends_on, alternative_to, derived_from, implements, extends, configured_by)
- ALIAS_OF (any->any)
- PARTICIPATED_IN (Person->Event): role
- OCCURRED_DURING (Event->Project)
- PRODUCED (Event->Decision|Object|Fact)
- TRIGGERED_BY (Event->Event)
- USES (Project|Person->Object): purpose
- HOSTED_ON (Object->Object)
- PRODUCED_BY (Object->Project|Event)
QUERY TEMPLATES
Use these tested templates for common question patterns. Fill in the $param values and adjust as needed.
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 · 177 lines · 43 tokens per session scan A b721b499a677
graph-ask is a skill published in the GitHub repository stevepridemore/graph-memory (2 stars, last pushed 7d ago), licensed MIT. It adds 43 tokens to every session and 1,829 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 2026-08-31.
Other skills, from other repositories
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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
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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.