Borrowing it
Nothing to install: this file belongs to loerei/chronicle-mcp. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/loerei/chronicle-mcp/main/.agents/skills/initialize-knowledge-graph/SKILL.mdgit clone --depth 1 https://github.com/loerei/chronicle-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/skills/loerei/chronicle-mcp/initialize-knowledge-graph)<a href="https://agentmods.dev/skills/loerei/chronicle-mcp/initialize-knowledge-graph"><img src="https://agentmods.dev/badge/skills/loerei/chronicle-mcp/initialize-knowledge-graph/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/loerei/chronicle-mcp/initialize-knowledge-graph"><img src="https://agentmods.dev/badge/skills/loerei/chronicle-mcp/initialize-knowledge-graph.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 | $0.00059 | $0.00561 |
| Opus 5.5 | $0.00024 | $0.00224 |
| Sonnet 5.5 | $0.00012 | $0.00112 |
| Haiku 4.5 | $0.00006 | $0.00056 |
Grade A, and why
initialize-knowledge-graph 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 — 42 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Initialize Knowledge Graph
Automate the discovery of domain terminology and codebase structure, translating them into persistent nodes (Entities), edges (Relations), and facts (Observations) inside the Memory MCP server.
This ensures the agent starts with high-signal context and complete terminology alignment without requiring manual explanation.
Process
1. Discovery & Research
Scan the codebase to gather high-level domain information and structural entry points:
- Domain Docs: Look for
CONTEXT.md,CONTEXT-MAP.md, and any files indocs/adr/. - Glossary & Jargon: Extract canonical terms, abbreviations, and descriptions defined in these files.
- Codebase Structure: Scan major modules, entry points, or directory layout using
get_repo_outlineorget_file_tree.
2. Formulate Entity List
Before writing to the graph, list all identified entities and their relationships. Group them into:
- Concepts: Core business domains or components (e.g.
Character card,Persona card). - Actors/Roles: Active agents or user roles in the system (e.g.
User,Bot). - Structures: Physical or logical data layouts (e.g.
Lorebook,Database).
3. Populate the Graph (Memory Ingestion)
Proactively call the memory MCP server mutation tools to build the knowledge graph structure:
- Create Entities: Call
create_entitiesto add all mapped concepts and actors. Include a brief, precise description in each node's observations. - Set Relations: Call
create_relationsto establish structural and semantic connections. - Configure Aliases (CRITICAL): For any acronyms, shortcuts, or alternate names, create a separate Entity and link it to the canonical Entity using an
is_alias_oforsynonym_ofrelation.- Example: Create
Authas a Concept, and establishAuth$\rightarrow$is_alias_of$\rightarrow$Authentication.
- Example: Create
4. Verify & Confirm
Validate that the knowledge graph is correctly populated:
- Call
read_graphor perform asearch_nodesquery on major terms to verify the nodes are correctly linked and queryable. - Present a concise, professional summary of the initialized entities and relationships to the user.
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 · 42 lines · 59 tokens per session scan A a06b02859067
initialize-knowledge-graph is a skill published in the GitHub repository loerei/chronicle-mcp (0 stars, last pushed 1mo ago), licensed MIT. It adds 59 tokens to every session and 561 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
knowledge-ingest
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forget
Remove a personal fact, preference, relationship detail, topic, or prior event from Muse's active memory and stop existing copies or automations from bringing it back. Use for explicit requests such as 'forget that', 'don't remember this about me', or 'remove that from your memory'. Do not use when 'forget it' merely…
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Cortex
Operate Cortex, the LifeOS memory system — the typed Knowledge Archive (People, Companies, Ideas, Research with typed related: links) plus recall of prior work sessions, ISAs, and conversations. Search, add, harvest, develop, ingest, distill, graph-navigate, recall. USE WHEN cortex, knowledge, knowledge base, search…
memory
Use when the user asks to remember, recall, forget, update, search, or inspect durable OpenSquilla memory, including profile facts in USER.md and long-term notes in MEMORY.md or memory//.md.
ha-data-stores
Map of Hope Agent's local data stores and safe read-only query workflow. Use when the user asks where Hope Agent stores data, wants to inspect sessions/messages/memory/logs/background jobs/knowledge indexes/settings, asks the model to query local app data, or debugging requires checking persisted state. Trigger…