# zettelforge - Doramagic AI Context Pack > Positioning: a pre-install experience and judgment asset. It helps the host AI get off to a good start, but it does not mean the project has already been installed, run, or validated. ## Sufficiency Principle - **Sufficiency over compression**: The AI Context Pack should be sufficient for the host AI to understand the project's value, capability boundaries, entrypoints, risks, and evidence sources before starting work; it may be layered, but it does not aim for the shortest possible summary. - **Compression policy**: Compress only noise and duplication, never context that affects judgment or the quality of the work. ## How the Host AI Should Use This You are reading the AI Context Pack that Doramagic compiled for zettelforge. Treat it as pre-work context: help the user understand who it fits, what it can do, how to start, what must be verified after install, and where the risks are. Do not claim that you have already installed, run, or executed the target project. ## Claim Consumption Rules - **Fact source**: Repo Evidence + Claim/Evidence Graph; the Human Wiki only supplies salience, terminology, and narrative structure. - **Minimum status for a fact**: `supported` - `supported`: May be used as a project fact, but the answer must cite the claim_id and evidence path. - `weak`: Usable only as a low-confidence lead; the user must be asked to keep verifying. - `inferred`: Usable only for risk notes or open questions; must not be packaged as a project fact. - `unverified`: Must not be used as fact; state clearly that evidence is insufficient. - `contradicted`: Must show the conflicting sources and must not force a single version on the user's behalf. ## Who It Fits Best - **Developers already using host AIs such as Claude/Codex/Cursor/Gemini**: The README or plugin config mentions multiple host AIs. Evidence: `README.md` Claim: `clm_0003` supported 0.86 - **Users who want to bring professional workflows into a host AI**: The repo contains Skill documents. Evidence: `docs/archive/SKILL.md` Claim: `clm_0004` supported 0.86 ## What It Can Do - **AI Skill / Agent Instruction Asset Library** (Previewable before install): The project contains Skill or Agent instruction files that a host AI can read, useful for bringing professional workflows into hosts like Claude, Codex, or Cursor. Evidence: `docs/archive/SKILL.md` Claim: `clm_0001` supported 0.86 - **Command-Line Startup or Install Flow** (Verify after install): The project documentation contains runnable commands; real use requires running them in a local or host environment. Evidence: `README.md`, `docs/tutorials/01-quickstart.md` Claim: `clm_0002` supported 0.86 ## How to Start - `pip install zettelforge` Evidence: `README.md` Claim: `clm_0005` supported 0.86 - `git clone https://github.com/rolandpg/zettelforge.git` Evidence: `docs/tutorials/01-quickstart.md` Claim: `clm_0006` supported 0.86 - `pip install -e .` Evidence: `docs/tutorials/01-quickstart.md` Claim: `clm_0007` supported 0.86 ## Continue-or-Stop Decision Card - **Current recommendation**: Sandbox trial only - **Why**: The project has signals of install commands, host configuration, or local writes; do not go straight into your primary environment—trial it in isolation first. ### 30-Second Read - **What to do now**: Sandbox trial only - **Minimum safe next step**: Run Prompt Preview first; if you still want to install, trial only in an isolated environment - **Do not trust yet**: Real output quality cannot be trusted before install. - **Continuing will touch**: Command execution, Host AI configuration, Local environment or project files ### What You Can Trust Now - **Target-audience signal: Developers already using host AIs such as Claude/Codex/Cursor/Gemini** (supported): Backed by a supported claim or project evidence, but that still is not the same as real install results. Evidence: `README.md` Claim: `clm_0003` supported 0.86 - **Target-audience signal: Users who want to bring professional workflows into a host AI** (supported): Backed by a supported claim or project evidence, but that still is not the same as real install results. Evidence: `docs/archive/SKILL.md` Claim: `clm_0004` supported 0.86 - **Capability exists: AI Skill / Agent Instruction Asset Library** (supported): You can trust that the project contains signals of this capability; whether it fits your specific task still needs trial or after-install verification. Evidence: `docs/archive/SKILL.md` Claim: `clm_0001` supported 0.86 - **Capability exists: Command-Line Startup or Install Flow** (supported): You can trust that the project contains signals of this capability; whether it fits your specific task still needs trial or after-install verification. Evidence: `README.md`, `docs/tutorials/01-quickstart.md` Claim: `clm_0002` supported 0.86 - **There are Quick Start / install-command signals** (supported): You can trust that the docs mention a startup or install entrypoint; do not run it directly in your primary environment because of that. Evidence: `README.md` Claim: `clm_0005` supported 0.86 ### What You Cannot Trust Yet - **Real output quality cannot be trusted before install.** (unverified): Prompt Preview can only show how it guides you; it cannot prove result quality in the real project. - **Host AI version compatibility cannot be trusted before install.** (unverified): Host loading rules and version differences across Claude, Cursor, Codex, Gemini, and others must be verified in a real environment. - **That it will not pollute your existing host AI's behavior cannot be trusted directly.** (inferred): Skill, plugin, and AGENTS/CLAUDE/GEMINI instructions may change the host AI's default behavior. Evidence: `docs/archive/SKILL.md` - **Safe rollback cannot be assumed by default.** (unverified): Unless the project clearly provides uninstall and recovery instructions, verify in an isolated environment first. - **After a real install, is it compatible with the user's current host AI version?** (unverified): Compatibility can only be verified in the actual host environment. - **Does the project's output quality meet the user's specific task?** (unverified): The pre-install preview can only show flow and boundaries; it cannot replace real evaluation. - **Do the install commands require network access, permissions, or global writes?** (unverified): This affects install risk in both enterprise and personal environments. Evidence: `README.md` ### What Continuing Will Touch - **Command execution**: Package managers, network downloads, the local plugin directory, project config, or the user's home directory. Why: Running the very first command can already change your environment; decide whether it is worth running first. Evidence: `README.md`, `docs/tutorials/01-quickstart.md` - **Host AI configuration**: The plugin, Skill, or rule-loading config of hosts like Claude/Codex/Cursor/Gemini/OpenCode. Why: Host configuration changes how the AI works afterward and may conflict with the user's existing rules. Evidence: `docs/archive/SKILL.md` - **Local environment or project files**: Install results, plugin caches, project config, or local dependency directories. Why: The write scope and rollback path cannot be proven before install and need isolated verification. Evidence: `README.md`, `docs/tutorials/01-quickstart.md` - **Host AI context**: The AI Context Pack, Prompt Preview, Skill routing, risk rules, and project facts. Why: Importing context affects the host AI's later judgment, so avoid packaging unverified items as facts. ### Minimum Safe Next Steps - **Run Prompt Preview first**: Use a pre-install interactive trial to judge whether the way of working fits; it needs no authorization or environment change. (applies when: Applies to any project, especially when output quality is unknown.) - **Trial-install only in an isolated directory or a test account**: Avoid letting install commands pollute your primary host AI, real projects, or home directory. (applies when: When there are signals of command execution, plugin config, or local writes.) - **Back up your host AI configuration first**: Skill, plugin, and rule files may change the default behavior of Claude/Cursor/Codex. (applies when: When there is a plugin manifest, a Skill, or a host rule entrypoint.) - **After install, verify just one minimal task**: Verify loading, compatibility, output quality, and rollback first, then decide whether to use it deeply. (applies when: When moving from a trial into a real workflow.) ### Exit Plan - **Preserve the pre-install state**: Record the original host config and project state so you can later judge whether it is recoverable. - **Be ready to remove the host plugin / Skill / rule entrypoint**: If behavior is off after the trial install, you can restore the host AI to its pre-trial state. - **Record the install commands and written paths**: Without clear uninstall instructions, you at least need to know which directories or configs to clean up manually. - **If there is no rollback path, do not enter your primary environment**: No rollback is a blocker before continuing; do not proceed on trust or luck. ## What Can Only Be Previewed - Explain who the project fits and what it can do - Demonstrate a typical conversation flow based on project docs - Help the user decide whether it is worth installing or researching further ## What Must Be Verified After Install - Actually installing the Skill, plugin, or CLI - Running scripts, modifying local files, or accessing external services - Verifying real output quality, performance, and compatibility ## Boundary & Risk Decision Card - **Mistaking the pre-install preview for a real run**: The user may overestimate how much configuration, permission, and compatibility verification the project has already done. Mitigation: Clearly separate prompt_preview_can_do from runtime_required. Claim: `clm_0008` inferred 0.45 - **Command execution will modify the local environment**: Install commands may write to the user's home directory, the host plugin directory, or project configuration. Mitigation: Run in an isolated environment or a test account first. Evidence: `README.md`, `docs/tutorials/01-quickstart.md` Claim: `clm_0009` supported 0.86 - **To confirm**: After a real install, is it compatible with the user's current host AI version?. Why: Compatibility can only be verified in the actual host environment. - **To confirm**: Does the project's output quality meet the user's specific task?. Why: The pre-install preview can only show flow and boundaries; it cannot replace real evaluation. - **To confirm**: Do the install commands require network access, permissions, or global writes?. Why: This affects install risk in both enterprise and personal environments. ## Pre-Work Working Context ### Loading Order - First read how_to_use.host_ai_instruction to establish the boundaries of this pre-install judgment asset. - Read claim_graph_summary to confirm facts come from the Claim/Evidence Graph, not the Human Wiki narrative. - Then read intended_users, capabilities, and quick_start_candidates to judge whether the user is a match. - When you need to carry out a concrete task, check role_skill_index first, then evidence_index. - For real install, file modification, network access, performance, or compatibility questions, turn to risk_card and boundaries.runtime_required. ### Task Routes - **AI Skill / Agent Instruction Asset Library**: Use role_skill_index / evidence_index to help the user pick a usable role, Skill, or workflow first. Boundary: Can be experienced via a pre-install Prompt. Evidence: `docs/archive/SKILL.md` Claim: `clm_0001` supported 0.86 - **Command-Line Startup or Install Flow**: State that this is an after-install capability first, then give a pre-install checklist. Boundary: Must be verified after a real install or run. Evidence: `README.md`, `docs/tutorials/01-quickstart.md` Claim: `clm_0002` supported 0.86 ### Context Scale - Total files: 325 - Important-file coverage: 40/325 - Evidence index entries: 80 - Role / Skill entries: 1 ### Handling Insufficient Evidence - **missing_evidence**: State that evidence is insufficient and ask the user for the target file, a README section, or after-install verification records; do not fill in facts. - **out_of_scope_request**: State that the task is beyond the current AI Context Pack's evidence scope and suggest the user check the Human Manual or verify after a real install. - **runtime_request**: Provide a pre-install checklist and command sources, but do not run commands for the user or claim they have been run. - **source_conflict**: Show the conflicting sources side by side, mark them as unverified, and do not force a single version. ## Prompt Recipes ### Fit assessment - Goal: Judge whether this project fits the user's current task. - Expected output: A fit conclusion, key reasons, evidence citations, what can be previewed before install, what must be verified after install, and a next-step recommendation. ```text Based on the AI Context Pack for zettelforge, ask me 3 necessary questions first, then judge whether it fits my task. The answer must cover: who it fits, what it can do, what it cannot do, whether it is worth installing, and where the evidence comes from. Every project fact must cite evidence_refs, source_paths, or a claim_id. ``` ### Pre-install experience - Goal: Let the user feel the core workflow before installing, while avoiding packaging the preview as real capability or a marketing promise. - Expected output: An experience script with boundary labels, an after-install verification checklist, and a cautious recommendation; with no real-run promises or strong marketing language. ```text Treat zettelforge as a pre-install experience asset, not an already-installed tool or a real runtime environment. Output exactly four parts: 1. Ask me 3 necessary questions first. 2. Give an "experience script": use the three labels [Previewable before install], [Must verify after install], and [Insufficient evidence] to show how it might guide the workflow. 3. Give an after-install verification checklist: list which capabilities can only be confirmed after a real install, real host loading, and a real project run. 4. Give a cautious recommendation: only "worth researching/trialing further", "add information before deciding", or "not recommended to continue"; do not endorse the project. Hard boundaries: - Do not claim you have installed, run, executed tests, modified files, or produced real results. - Do not write promise-like phrasing such as "auto-adapts", "guarantees passing", "perfect fit", or "strongly recommend installing". - If you describe how it works after install, you must use a conditional such as "if installed successfully and the host loads the Skill correctly, it might...". - The experience script may only be written as "example lines / hypothetical flow": use "might ask / might suggest / might show", not "has written, has generated, has passed, is running, is generating". - Prompt Preview does not hand out install commands; if the user is ready to trial, only prompt them to read Quick Start and the Risk Card first and to verify in an isolated environment. - Every project fact must come from a supported claim, evidence_refs, or source_paths; inferred/unverified items can only be risks or open questions. ``` ### Role / Skill selection - Goal: Pick the best-matching asset from the project's roles or Skills. - Expected output: A list of candidate roles or Skills, each with an applicable scenario, evidence paths, risk boundary, and whether after-install verification is needed. ```text Read role_skill_index and recommend 3-5 of the most relevant roles or Skills for my target task. For each recommendation, state the applicable scenario, likely output, risk boundary, and evidence_refs. ``` ### Risk pre-check - Goal: Identify environment, permission, rule-conflict, and quality risks before installing or adopting. - Expected output: A checklist of environment, permission, dependency, license, host-conflict, quality risk, and unknown items. ```text Based on risk_card, boundaries, and quick_start_candidates, give me a pre-install risk pre-check list. Do not run commands for me; only explain what I should check, why, and what impact a failure would have. ``` ### Host AI kickoff instruction - Goal: Turn the project context into a host AI instruction for the start of a conversation. - Expected output: A pre-work instruction with clear boundaries and clear evidence citations, suitable to copy to a host AI. ```text Based on the AI Context Pack for zettelforge, generate a pre-work instruction I can paste to my host AI. This instruction must obey not_runtime=true and must not claim the project has been installed, run, or produced real results. ``` ## Role / Skill Index - Indexed 1 role / Skill / project-doc entries. - **zettelforge** (skill): ZettelForge v2.0.0 — Production CTI agentic memory system. Hybrid TypeDB STIX 2.1 ontology + LanceDB vector search . Zero external AI dependencies: fastembed for embeddings, llama-cpp-python for LLM. 75% accuracy on CTI queries, 18% on LOCOMO. Use when agents need persistent memory, threat intel retrieval, entity extraction, graph traversal, or RAG synthesis. Activation hint: When the user's task is highly relevant to the workflow described by “zettelforge”, use it for a pre-install experience first, then decide whether to install. Evidence: `docs/archive/SKILL.md` ## Evidence Index - Indexed 80 evidence entries. - **Archive** (documentation): Historical documents that no longer reflect the current release but are preserved so prior claims can be audited. Evidence: `docs/archive/README.md` - **ZettelForge** (documentation): The only agentic memory system built for cyber threat intelligence. Evidence: `README.md` - **ZettelForge v2.0.0: Agentic Memory System** (skill_instruction): ZettelForge v2.0.0: Agentic Memory System Evidence: `docs/archive/SKILL.md` - **Why SQLite + LanceDB Not One or the Other** (documentation): Why SQLite + LanceDB Not One or the Other Evidence: `docs/explanation/architecture.md` - **Design philosophy: dual-hemisphere CTI memory** (documentation): Design philosophy: dual-hemisphere CTI memory Evidence: `docs/explanation/design-philosophy-dual-hemisphere.md` - **The Two-Phase Extraction Pipeline** (documentation): ZettelForge's memory evolution pipeline — activated via remember ..., evolve=True — implements a Mem0-inspired two-phase process that solves the fundamental problem of append-only memory systems: redundancy, contradiction, and noise. The MCP server and web API enable evolution by default. The underlying remember with extraction method can also be called directly for programmatic use. Evidence: `docs/explanation/two-phase-pipeline.md` - **The Zettelkasten Philosophy in ZettelForge** (documentation): The Zettelkasten Philosophy in ZettelForge Evidence: `docs/explanation/zettelkasten-philosophy.md` - **Contributing to ZettelForge** (documentation): Thank you for your interest in contributing to ZettelForge! This document provides guidelines for contributing to the project. Evidence: `CONTRIBUTING.md` - **License** (source_file): Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files the "Software" , to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: Evidence: `LICENSE` - **Architecture** (documentation): Visual diagram: docs/architecture-diagram.mmd docs/architecture-diagram.mmd Deep explanation: docs/explanation/architecture.md docs/explanation/architecture.md Evidence: `ARCHITECTURE.md` - **ZettelForge Threat Model** (documentation): Document ID: THREAT-001 Classification: Internal Tier 2 Last Updated: 2026-04-25 Framework: STRIDE GOV-011 SSDL Requirement Scope: Community Edition v2.5.x MIT-licensed codebase Compliance Mapping: FedRAMP SA-3, SA-8, SA-11, SA-15; NIST 800-171 3.11, 3.13, 3.14 Evidence: `docs/THREAT_MODEL.md` - **Human Evaluation Rubric for ZettelForge Briefings** (documentation): Human Evaluation Rubric for ZettelForge Briefings Evidence: `docs/human-evaluation-rubric.md` - **ZettelForge Documentation** (documentation): Your SOC's most expensive asset walks out the door every day. Evidence: `docs/index.md` - **ZettelForge Skill Package - Summary** (documentation): ZettelForge Skill Package - Summary Evidence: `docs/archive/PACKAGE_SUMMARY.md` - **Threat Recall Logo Philosophy** (documentation): Threat Recall needs a mark that feels analytical without becoming decorative: a compact system where detection, memory, and cyber threat context are all present in a few precise lines. The visual language should be restrained, technical, and quiet. It should feel like a tool built for analysts who trust clarity over noise. Evidence: `docs/assets/threatrecall-logo-philosophy.md` - **Getting ZettelForge onto "awesome" lists — assessment & submission kit** (documentation): Getting ZettelForge onto "awesome" lists — assessment & submission kit Evidence: `docs/awesome-lists/SUBMISSION_GUIDE.md` - **ZettelForge — Brand Identity** (documentation): Canonical reference for agents, designers, and contributors producing any visual artifact for ZettelForge: diagrams, READMEs, slides, social cards, docs, UI. The brand has one direction: Neural Dark. Light mode exists only as a utility swap for print/GitHub-light-reader parity. Do not invent alternate "themes," "accents," or seasonal variants. Evidence: `docs/brand/brandIdentity.md` - **Epistemic Tiers and Confidence** (documentation): Not all intelligence is created equal. A verified CISA advisory carries more weight than an anonymous forum post, which carries more weight than an LLM's speculation. ZettelForge tracks this through two complementary mechanisms: epistemic tiers and confidence scores. Evidence: `docs/explanation/epistemic-tiers.md` - **LLM budgets, timeouts, and what they cost you** (documentation): LLM budgets, timeouts, and what they cost you Evidence: `docs/explanation/llm-budgets-and-timeouts.md` - **How STIX 2.1 Maps to ZettelForge** (documentation): ZettelForge implements a focused subset of STIX 2.1 in TypeDB. This page explains what was included, what was excluded, and why. Evidence: `docs/explanation/stix-in-zettelforge.md` - **Build an Extension Package** (documentation): ZettelForge discovers installed extension packages at startup via zettelforge.extensions.load extensions . An extension is any Python package that registers itself under the zettelforge.extensions namespace or is importable as zettelforge enterprise . Evidence: `docs/how-to/build-extensions.md` - **Tune LanceDB Vector Search** (documentation): Configure LanceDB vector search for optimal retrieval quality. Adjust embedding model, index parameters, similarity threshold, and entity boost to balance precision and recall for your CTI workload. Evidence: `docs/how-to/configure-lancedb.md` - **Configure OpenCTI Integration** (documentation): Connect ZettelForge with zettelforge-enterprise extension to an OpenCTI instance for bi-directional STIX 2.1 sync. Pull threat intelligence from OpenCTI into ZettelForge memory; push ZettelForge notes and analyst annotations back to OpenCTI as reports. Evidence: `docs/how-to/configure-opencti.md` - **Configure PII Detection and Redaction** (documentation): Configure PII Detection and Redaction Evidence: `docs/how-to/configure-pii.md` - **Configure Sigma Rule Ingestion** (documentation): Ingest Sigma detection rules SigmaHQ format into ZettelForge memory. Each rule is parsed, validated against the vendored SigmaHQ JSON schema, mapped to a SigmaRule entity with typed knowledge graph relations, and persisted as a memory note. Evidence: `docs/how-to/configure-sigma-ingestion.md` - **Set Up TypeDB for ZettelForge Enterprise** (documentation): Set Up TypeDB for ZettelForge Enterprise Evidence: `docs/how-to/configure-typedb.md` - **Configure YARA Rule Ingestion** (documentation): Ingest YARA rules into ZettelForge memory. Each rule is parsed via plyara, validated against the vendored CCCS Canadian Centre for Cyber Security metadata schema, mapped to a YaraRule entity with typed knowledge graph relations, and persisted as a memory note. Evidence: `docs/how-to/configure-yara-ingestion.md` - **Ingest a Long Threat Report** (documentation): Ingest threat reports of any length using remember report . ZettelForge chunks content on sentence boundaries, runs the two-phase extraction pipeline on each chunk, deduplicates against existing notes, and stores published-date metadata for temporal queries. Evidence: `docs/how-to/ingest-news-report.md` - **Integrate ZettelForge into an AI Agent** (documentation): Integrate ZettelForge into an AI Agent Evidence: `docs/how-to/integrate-llm-agent.md` - **Integrate with CrewAI** (documentation): Integrate ZettelForge as a persistent memory backend for CrewAI agents. Three CrewAI-compatible tools are provided: recall blended vector + graph search , remember persist findings with auto-extraction , and synthesize LLM-generated answers over stored memory . Evidence: `docs/how-to/integrate-with-crewai.md` - **Integrate with LangChain** (documentation): Use ZettelForge as a LangChain-compatible retriever in any RAG pipeline. The ZettelForgeRetriever wraps MemoryManager.recall and converts ZettelForge MemoryNote objects into LangChain Document objects with rich metadata. Evidence: `docs/how-to/integrate-with-langchain.md` - **Maintain LanceDB Indexes** (documentation): On write-heavy shards, the dominant cost of MemoryStore. index in lance is LanceDB walking an unbounded version chain on each insert. cleanup old versions collapses this chain to restore insert performance. Evidence: `docs/how-to/maintain-lancedb.md` - **Migrate JSONL Data to SQLite** (documentation): Use this guide when you are upgrading an existing ZettelForge install from v2.1.x JSONL to v2.2.x SQLite default and want to carry your notes, knowledge graph, and entity index forward. Evidence: `docs/how-to/migrate-jsonl-to-sqlite.md` - **Passive OSINT Enrichment** (documentation): ZettelForge ships a passive OSINT executor that runs registered collectors, validates each tuple against the ontology, canonicalizes entity values, and persists nodes and edges into the knowledge graph. Evidence: `docs/how-to/passive-osint-enrichment.md` - **Query What Tools an APT Group Uses** (documentation): Retrieve tool-usage relationships for a threat actor using blended vector + graph retrieval, synthesis, and direct graph traversal. Evidence: `docs/how-to/query-apt-tools.md` - **Reproduce the Published Benchmarks** (documentation): Every benchmark referenced in BENCHMARK REPORT.md https://github.com/rolandpg/zettelforge/blob/master/benchmarks/BENCHMARK REPORT.md ships as an adapter script in benchmarks/ . This guide shows how to run each one against a fresh install and compare your results. Evidence: `docs/how-to/reproduce-benchmarks.md` - **Resolve Threat Actor Aliases** (documentation): Map threat actor aliases to canonical names automatically. ZettelForge's AliasResolver tries TypeDB alias-of relations first, then falls back to a local JSON file with hardcoded mappings. Evidence: `docs/how-to/resolve-aliases.md` - **Run Temporal Queries** (documentation): Query how entities change over time using ZettelForge's temporal graph index. Track entity timelines, detect superseded intelligence, and retrieve all changes since a given timestamp. Evidence: `docs/how-to/run-temporal-query.md` - **Set Up the MCP Server** (documentation): The ZettelForge MCP server exposes the full memory system as tools through the Model Context Protocol https://modelcontextprotocol.io MCP . Any MCP-compatible AI agent — Claude Code, OpenClaw, Cline, or a custom client — can call zettelforge remember , zettelforge recall , and five other tools over stdio transport. Evidence: `docs/how-to/set-up-mcp-server.md` - **Store Threat Intelligence About an Actor** (documentation): Store Threat Intelligence About an Actor Evidence: `docs/how-to/store-threat-actor.md` - **Troubleshoot ZettelForge** (documentation): A short decision tree for the most common failures, grouped by the phase they occur in. Each entry links back to the authoritative behaviour in code or config. Evidence: `docs/how-to/troubleshoot.md` - **Upgrade ZettelForge** (documentation): Use this as a checklist whenever you move between minor releases. For the full list of changes per release see CHANGELOG.md https://github.com/rolandpg/zettelforge/blob/master/CHANGELOG.md . Evidence: `docs/how-to/upgrade.md` - **Use Detection Rules and the Explainer** (documentation): Use Detection Rules and the Explainer Evidence: `docs/how-to/use-detection-rules.md` - **Use the ZettelForge Web Management Interface** (documentation): Use the ZettelForge Web Management Interface Evidence: `docs/how-to/use-web-interface.md` - **Awesome-list submission drafts** (documentation): Ready-to-paste entries for the four highest-ROI awesome-lists ZettelForge belongs on. Each entry follows the target list's existing format conventions. Open one PR per list, link them all back here. Evidence: `docs/marketing/awesome-list-submissions.md` - **The Memory Problem** (documentation): A narrative log of ZettelForge's first three weeks — March 28 to April 16, 2026. Written April 16, 2026. Evidence: `docs/narrative/2026-04-16-the-memory-problem.md` - **Architecture Deep Dive** (documentation): This document provides a comprehensive technical reference for ZettelForge v2.4.0, covering all major subsystems, their interactions, and implementation details. Evidence: `docs/reference/architecture-deep-dive.md` - **Configuration Reference** (documentation): Configuration values are resolved with highest priority first: Evidence: `docs/reference/configuration.md` - **Detection Rules Schema Reference** (documentation): Module: zettelforge.detection.base , zettelforge.sigma.entities , zettelforge.yara.entities , zettelforge.detection.explainer Evidence: `docs/reference/detection-rules-schema.md` - **Community vs Enterprise Editions** (documentation): Module: zettelforge.edition , zettelforge.extensions Evidence: `docs/reference/editions.md` - **Entity Indexer Concurrency Reference** (documentation): Entity Indexer Concurrency Reference Evidence: `docs/reference/entity-indexer-concurrency.md` - **Governance Controls Reference** (documentation): Modules: zettelforge.governance validator , zettelforge.prompt injection guard Evidence: `docs/reference/governance-controls.md` - **Knowledge Graph Edge Schema Reference** (documentation): Knowledge Graph Edge Schema Reference Evidence: `docs/reference/kg-edge-schema.md` - **MCP Protocol Reference** (documentation): The ZettelForge MCP server implements the Model Context Protocol https://modelcontextprotocol.io specification protocol version 2024-11-05 over stdio transport using JSON-RPC 2.0. Evidence: `docs/reference/mcp-protocol.md` - **MemoryManager API Reference** (documentation): Parameter Type Default Description :---------- :----- :-------- :------------ jsonl path Optional str None Path to JSONL note store. Falls back to ~/.amem/notes.jsonl . lance path Optional str None Path to LanceDB directory. Falls back to ~/.amem/lance/ . Evidence: `docs/reference/memory-manager-api.md` - **Module Inventory** (documentation): Complete reference of all 57 core modules in ZettelForge v2.4.0. Evidence: `docs/reference/module-inventory.md` - **Retrieval Policies Reference** (documentation): Modules: zettelforge.intent classifier , zettelforge.vector retriever , zettelforge.graph retriever , zettelforge.blended retriever Evidence: `docs/reference/retrieval-policies.md` - **Sigma Schema Reference** (documentation): Module: zettelforge.sigma.parser , zettelforge.sigma.schemas Evidence: `docs/reference/sigma-schema-reference.md` - **STIX Ontology Schema Reference** (documentation): Schema files: src/zettelforge/schema/stix core.tql , src/zettelforge/schema/stix rules.tql Evidence: `docs/reference/stix-schema.md` - **Web API Reference** (documentation): Server: FastAPI at http://localhost:8088 . Evidence: `docs/reference/web-api.md` - The remaining 20 evidence entries are in `AI_CONTEXT_PACK.json` or `EVIDENCE_INDEX.json`. ## Rules the Host AI Must Follow - **Treat this asset as pre-work context, not a runtime environment.**: The AI Context Pack contains only an evidence-backed understanding of the project, not the project's executable state. Evidence: `docs/archive/README.md`, `README.md`, `docs/archive/SKILL.md` - **When answering the user, distinguish what can be previewed from what can only be verified after install.**: The consumer value of the pre-install experience comes from reducing bad installs and misjudgments, not from pretending to be a real run. Evidence: `docs/archive/README.md`, `README.md`, `docs/archive/SKILL.md` ## Questions the User Should Answer First - Which host AI or local environment do you plan to use it in? - Do you just want to experience the workflow first, or are you ready to actually install? - What matters most to you: install cost, output quality, or conflicts with your existing rules? ## Acceptance Checks - Every capability claim can be traced back to a file path in evidence_refs. - AI_CONTEXT_PACK.md does not package previews as a real run. - The user can understand who it fits, what it can do, how to start, and the risk boundaries within 3 minutes. --- ## Doramagic Context Augmentation The following sections strengthen the repository context for a host AI. Human Manual data is a reading route, and pitfall notes become operating constraints. ## Human Manual Outline Usage rule: this is only a reading route and salience signal, not factual authority. Concrete claims must still return to repo evidence or Claim Graph. Host AI hard rules: - Do not treat page titles, section order, summaries, or importance values as factual project evidence. - When explaining the Human Manual outline, state that it is only a reading route or salience signal. - Capability, installation, compatibility, runtime state, and risk claims must cite repo evidence, source paths, or Claim Graph. - **Overview & System Architecture**: importance `high` - source_paths: README.md, ARCHITECTURE.md, docs/explanation/architecture.md, docs/explanation/design-philosophy-dual-hemisphere.md, docs/explanation/two-phase-pipeline.md - **Memory Pipeline: Extraction, Storage & Retrieval**: importance `high` - source_paths: src/zettelforge/memory_manager.py, src/zettelforge/memory_store.py, src/zettelforge/memory_updater.py, src/zettelforge/memory_evolver.py, src/zettelforge/vector_memory.py - **Detection Rules, OSINT & Integrations**: importance `high` - source_paths: src/zettelforge/detection/__init__.py, src/zettelforge/detection/base.py, src/zettelforge/detection/consumers.py, src/zettelforge/detection/explainer.py, src/zettelforge/sigma/__init__.py - **Operations, Security & Deployment**: importance `high` - source_paths: src/zettelforge/memory_defense.py, src/zettelforge/governance_validator.py, src/zettelforge/config.py, src/zettelforge/backend_factory.py, src/zettelforge/edition.py ## Repo Inspection Evidence - repo_clone_verified: true - repo_inspection_verified: true - repo_commit: `c65ee7b204231a76dd5a51bc0b6d19131122801e` - inspected_files: `Dockerfile`, `README.md`, `pyproject.toml`, `docs/THREAT_MODEL.md`, `docs/archive/PACKAGE_SUMMARY.md`, `docs/archive/README.md`, `docs/archive/SKILL.md`, `docs/assets/cf-analytics.js`, `docs/assets/threatrecall-logo-philosophy.md`, `docs/awesome-lists/SUBMISSION_GUIDE.md`, `docs/awesome-lists/zettelforge.yml`, `docs/brand/brandIdentity.md`, `docs/explanation/architecture.md`, `docs/explanation/design-philosophy-dual-hemisphere.md`, `docs/explanation/epistemic-tiers.md`, `docs/explanation/llm-budgets-and-timeouts.md`, `docs/explanation/stix-in-zettelforge.md`, `docs/explanation/two-phase-pipeline.md`, `docs/explanation/zettelkasten-philosophy.md`, `docs/how-to/build-extensions.md` Host AI hard rules: - Without repo_clone_verified=true, do not claim that the source code has been read. - Without repo_inspection_verified=true, do not write README, docs, or package-file conclusions as facts. - Without quick_start_verified=true, do not claim that the Quick Start path has run successfully. ## Doramagic Pitfall Constraints These rules come from Doramagic discovery, validation, or compilation findings. The host AI must treat them as operating constraints, not background notes. ### Constraint 1: Capability evidence risk requires verification - Trigger: README/documentation is current enough for a first validation pass. - Host AI rule: Reproduce the official install and quickstart path in an isolated environment. - Why it matters: May increase setup, validation, or first-run risk for the user. - Evidence: capability.assumptions | https://github.com/ThreatRecall/zettelforge - Hard boundary: Do not present this pitfall as solved, verified, or ignorable unless later evidence explicitly closes it. ### Constraint 2: Security or permission risk requires verification - Trigger: no_demo - Host AI rule: Reproduce the official install and quickstart path in an isolated environment. - Why it matters: May increase setup, validation, or first-run risk for the user. - Evidence: downstream_validation.risk_items | https://github.com/ThreatRecall/zettelforge - Hard boundary: Do not present this pitfall as solved, verified, or ignorable unless later evidence explicitly closes it. ### Constraint 3: Security or permission risk requires verification - Trigger: no_demo - Host AI rule: Reproduce the official install and quickstart path in an isolated environment. - Why it matters: May increase setup, validation, or first-run risk for the user. - Evidence: risks.scoring_risks | https://github.com/ThreatRecall/zettelforge - Hard boundary: Do not present this pitfall as solved, verified, or ignorable unless later evidence explicitly closes it. ### Constraint 4: Maintenance risk requires verification - Trigger: issue_or_pr_quality=unknown。 - Host AI rule: Reproduce the official install and quickstart path in an isolated environment. - Why it matters: May increase setup, validation, or first-run risk for the user. - Evidence: evidence.maintainer_signals | https://github.com/ThreatRecall/zettelforge - Hard boundary: Do not present this pitfall as solved, verified, or ignorable unless later evidence explicitly closes it.