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Mini Context Graph

GitHubOfficialFirst committed by fizznixDistributed by GitHub

  • Skill
  • Tool integration
  • Data and AI
  • MIT
  • Low risk
  • Has scripts

From the author

A persistent, compounding knowledge base combining Karpathy's LLM Wiki pattern with a structured knowledge graph. Ingest documents once — the LLM writes wiki pages, extracts entities/relations into the graph, and stores raw content for evidence retrieval. Knowledge accumulates and cross-references; it is never re-derived from scratch.
— the description by fizznix

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

Mini Context Graph implements a persistent knowledge management system designed to overcome the limitations of standard RAG. Instead of re-deriving knowledge from scratch, it maintains three synchronized layers: a wiki for human-readable synthesis, a knowledge graph for structural traversal, and an immutable raw source store for provenance. The agent acts as the curator, ingesting documents, extracting entities and relations, and continuously updating the wiki and graph to ensure knowledge accumulates and cross-references over time.

What it does

  • Ingests documents to extract entities and relations into a structured knowledge graph.
  • Maintains a persistent markdown wiki that synthesizes information and cross-references entities.
  • Stores raw source content immutably to provide verifiable evidence for every graph node and edge.
  • Supports structural graph traversal to answer complex queries without re-reading source documents.
  • Provides a linting workflow to identify contradictions, orphans, and stale claims within the knowledge base.
  • Enables fast-path retrieval by searching the wiki before performing deeper graph analysis.

When to use it

  • When you need a long-term, evolving knowledge base that grows more accurate with every document ingested.
  • When you require strict provenance and citations for every claim made by the agent.
  • When you need to perform structural analysis or relationship mapping across a large set of documents.
  • When you want to avoid the computational overhead of re-processing raw data for every query.
  • When you need to maintain a clean, human-readable wiki alongside a machine-navigable graph.

Advantages

  • Compounding knowledge: The system gets smarter and more interconnected as more data is added.
  • Verifiable: Every graph connection is linked to specific source text for auditability.
  • Hybrid retrieval: Combines the speed of wiki search with the depth of graph traversal.
  • Self-maintaining: Includes built-in linting to detect and resolve data inconsistencies.
  • Persistent: Knowledge is stored and refined, not generated from scratch on every request.

Usage examples

“Ingest the provided incident report and update the knowledge base.”
The agent extracts entities and relations, updates the graph, writes a summary wiki page, and links the new information to existing entity pages.
“Why does the system crash? Provide evidence.”
The agent searches the wiki for relevant context and performs a graph traversal to identify the cause, returning the answer with direct citations from the source documents.
“Run a health check on the wiki.”
The agent executes the linting workflow to identify and report on orphan pages, broken links, or potential contradictions in the knowledge base.

Install

  1. Download the skill as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/github/mini-context-graph/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip mini-context-graph.zip -d .agents/skills

Documentation by fizznix

Mini Context Graph Skill

The Core Idea

Standard RAG re-discovers knowledge from scratch on every query. This skill is different:

  1. Wiki layer — The LLM writes and maintains persistent markdown pages (summaries, entity pages, topic syntheses). Cross-references are already there. The wiki gets richer with every ingest.

  2. Graph layer — Entities and relations are extracted once and stored as a navigable knowledge graph. BFS traversal answers structural queries without re-reading sources.

  3. Raw source layer — Original documents are stored immutably with chunks. Provenance links tie every graph node and edge back to the exact text that supports it.

The LLM writes; the Python tools handle all bookkeeping.


Three Layers

LayerWhereWhat the LLM doesWhat Python does
Raw Sourcesdata/documents.jsonReads (never modifies)Stores chunks + metadata
Wikiwiki/ (markdown)Writes/updates pagesManages index.md + log.md
Graphdata/graph.jsonExtracts entities + relationsPersists, deduplicates, traverses

⚡ Quick Start for Agents

A complete runnable version of this workflow is in scripts/template_agent_workflow.py — copy and adapt it.

from scripts.contextgraph import ContextGraphSkill
from scripts.tools import wiki_store

skill = ContextGraphSkill()

# ===== INGEST WITH FULL RAG + WIKI =====
# 1. Read references/ingestion.md and references/ontology.md first
# 2. Extract entities and relations (LLM reasoning step)
entities = [
    {"name": "memory leak",   "type": "issue",  "supporting_text": "memory leaks cause crashes"},
    {"name": "system crash",  "type": "issue",  "supporting_text": "system crashes due to memory leaks"},
]
relations = [
    {"source": "memory leak", "target": "system crash", "type": "causes",
     "confidence": 1.0, "supporting_text": "System crashes due to memory leaks."},
]

result = skill.ingest_with_content(
    doc_id="doc_001",
    title="System Crash Analysis",
    source="/docs/incident_report.pdf",
    raw_content="System crashes due to memory leaks. Memory leaks occur when objects are not released.",
    entities=entities,
    relations=relations,
)
# result = {"doc_id": "doc_001", "chunk_count": 1, "nodes_added": 2, "edges_added": 1}

# 3. Write a wiki summary page for this document
wiki_store.write_page(
    category="summary",
    title="System Crash Analysis Summary",
    content="""---
title: System Crash Analysis
source_document: doc_001
tags: [summary, incident]
---

# System Crash Analysis

**Source:** incident_report.pdf

## Key Claims

- [[memory-leak]] causes [[system-crash]] (confidence: 1.0)

## Entities

- [[memory-leak]] (issue)
- [[system-crash]] (issue)
""",
    summary="Incident report: memory leaks cause system crashes.",
)

# ===== QUERY WITH EVIDENCE =====
result = skill.query_with_evidence("Why does the system crash?")
# Returns: {"query": ..., "subgraph": ..., "supporting_documents": [...], "evidence_chain": ...}

# ===== WIKI SEARCH (read wiki before answering) =====
pages = wiki_store.search_wiki("memory leak")
# Returns: [{slug, category, path, snippet}, ...]

Operations

Ingest

When a user provides a new document:

  1. Read references/ingestion.md — entity/relation extraction rules.

  2. Read references/ontology.md — type normalization rules.

  3. Extract entities and relations using your LLM reasoning.

  4. Call skill.ingest_with_content(...) — stores raw content + chunks + graph nodes + provenance.

  5. Write a wiki summary page using wiki_store.write_page(category="summary", ...).

  6. Update entity pages — for each new/updated entity, write or update wiki_store.write_page(category="entity", ...).

  7. Update topic pages if the document touches an existing synthesis topic.

  8. A single document ingest will typically touch 3–10 wiki pages.

Query

When a user asks a question:

  1. Check the wiki first — wiki_store.search_wiki(query) to find relevant pages. Read them.

  2. If the wiki has a good answer, synthesize from wiki pages (fast path).

  3. If deeper graph traversal is needed, call skill.query_with_evidence(query).

  4. Return the answer with evidence citations from supporting_documents.

  5. If the answer is valuable, file it back as a new wiki topic page.

Lint

Periodically health-check the wiki:

from scripts.tools import wiki_store
issues = wiki_store.lint_wiki()
# Returns: {orphan_pages, missing_pages, broken_wikilinks, isolated_pages}

Ask the LLM to review and fix: broken links, orphan pages, stale claims, missing cross-references. See references/lint.md for full lint workflow.


Ingestion Constraints

  • ❌ Do NOT hallucinate entities not present in the text

  • ❌ Do NOT add relations without explicit textual evidence

  • ❌ Do NOT add edges with confidence < 0.6

  • ✅ Provide supporting_text for every entity and relation — this enables provenance

  • ✅ Write a wiki summary page for every ingested document

  • ✅ Update existing entity pages when new information arrives

  • ✅ Flag contradictions in wiki pages when new data conflicts with old claims


Retrieval Constraints

  • 🔒 Traversal depth MUST NOT exceed 2 (config: MAX_GRAPH_DEPTH)

  • 🔒 Only edges with confidence ≥ 0.6 (config: MIN_CONFIDENCE)

  • 🔒 Maximum 50 nodes returned (config: MAX_NODES)

  • ❌ Do NOT fabricate nodes or edges not in the graph


Full Python API Reference

MethodPurposeWhen to Use
skill.ingest_with_content(doc_id, title, source, raw_content, entities, relations)Full RAG ingest: raw docs + graph + provenanceEvery new document
skill.add_node(name, node_type)Add single entity (no provenance)Quick additions without a source doc
skill.add_edge(source_name, target_name, relation, confidence)Add single relationQuick additions without a source doc
skill.query(query)Graph-only retrieval → subgraphStructural queries
skill.query_with_evidence(query)Graph + provenance → subgraph + source chunksQueries requiring citations
wiki_store.write_page(category, title, content, summary)Write/update a wiki pageAfter every ingest; after answering queries
wiki_store.read_page(category, title)Read a wiki pageBefore answering; for cross-referencing
wiki_store.search_wiki(query)Keyword search across wikiFast path before graph traversal
wiki_store.list_pages(category)List all wiki pagesGetting an overview
wiki_store.get_log(last_n)Read recent operationsUnderstanding wiki history
wiki_store.lint_wiki()Health checkPeriodic maintenance
documents_store.list_documents()List all ingested raw sourcesAudit / provenance checking
documents_store.search_chunks(query)Chunk-level searchFinding specific evidence

Design Philosophy

"The wiki is a persistent, compounding artifact. The cross-references are already there. The synthesis already reflects everything you've read." — Karpathy

LayerWhat HappensWho Owns It
LLM ReasoningExtraction, synthesis, writing wiki pagesAgent (.md guidance files)
Wiki PersistenceIndex, log, file I/Owiki_store.py
Graph PersistenceDedup, index, BFS traversegraph_store.py, retrieval_engine.py
Raw Source StorageImmutable docs + chunks + provenancedocuments_store.py

The human curates sources and asks questions. The LLM writes the wiki, extracts the graph, and answers with citations. Python handles all bookkeeping.

Files

scripts/tools/wiki_store.py

"""
wiki_store.py — Manages the persistent wiki layer.

Inspired by Karpathy's LLM Wiki pattern: the wiki is a directory of LLM-generated
markdown pages that the agent writes and maintains. This module provides the
deterministic file I/O and index/log management so the agent can focus on
reasoning, not bookkeeping.

Wiki structure (relative to project root):
    wiki/
        index.md        ← content-oriented catalog of all pages
        log.md          ← chronological append-only operation log
        entities/       ← one page per entity (person, concept, system, etc.)
        summaries/      ← source document summary pages
        topics/         ← cross-cutting synthesis and topic pages

The agent WRITES pages; this module handles the filesystem + index + log.
"""
from __future__ import annotations

import os
import re
import sys
from datetime import datetime, timezone
from pathlib import Path

sys.path.insert(0, str(Path(__file__).parent.parent))
import config

_WIKI_DIR = Path(os.environ.get("MINI_CONTEXT_GRAPH_WIKI_DIR", str(config.WIKI_DIR)))
_INDEX_FILE = _WIKI_DIR / "index.md"
_LOG_FILE = _WIKI_DIR / "log.md"

_CATEGORY_DIRS = {
    "entity": _WIKI_DIR / "entities",
    "summary": _WIKI_DIR / "summaries",
    "topic": _WIKI_DIR / "topics",
}

# ---------------------------------------------------------------------------
# Internal helpers
# ---------------------------------------------------------------------------

def _ensure_dirs() -> None:
    _WIKI_DIR.mkdir(parents=True, exist_ok=True)
    for d in _CATEGORY_DIRS.values():
        d.mkdir(parents=True, exist_ok=True)


def _now_iso() -> str:
    return datetime.now(timezone.utc).strftime("%Y-%m-%d")


def _slug(title: str) -> str:
    """Convert a title to a filesystem-safe slug."""
    slug = title.lower().strip()
    slug = re.sub(r"[^a-z0-9]+", "-", slug)
    return slug.strip("-")


def _page_path(category: str, slug: str) -> Path:
    base = _CATEGORY_DIRS.get(category, _WIKI_DIR)
    return base / f"{slug}.md"


# ---------------------------------------------------------------------------
# Index management
# ---------------------------------------------------------------------------

def _load_index() -> list[dict]:
    """Parse index.md into a list of entry dicts."""
    if not _INDEX_FILE.exists():
        return []
    entries = []
    for line in _INDEX_FILE.read_text().splitlines():
        # Expected table row: | [[slug]] | category | summary | date |
        if line.startswith("| [["):
            parts = [p.strip() for p in line.split("|") if p.strip()]
            if len(parts) >= 3:
                link = parts[0]  # [[slug]]
                category = parts[1] if len(parts) > 1 else ""
                summary = parts[2] if len(parts) > 2 else ""
                date = parts[3] if len(parts) > 3 else ""
                slug = re.sub(r"\[\[|\]\]", "", link)
                entries.append({
                    "slug": slug,
                    "category": category,
                    "summary": summary,
                    "date": date,
                })
    return entries


def _save_index(entries: list[dict]) -> None:
    """Rewrite index.md from the entries list."""
    _ensure_dirs()
    lines = [
        "# Wiki Index\n",
        "_Auto-managed by wiki_store. Do not edit the table manually._\n\n",
        "| Page | Category | Summary | Date |\n",
        "|------|----------|---------|------|\n",
    ]
    for e in entries:
        lines.append(
            f"| [[{e['slug']}]] | {e['category']} | {e['summary']} | {e['date']} |\n"
        )
    _INDEX_FILE.write_text("".join(lines))


def _append_log(operation: str, detail: str) -> None:
    """Append a timestamped entry to log.md."""
    _ensure_dirs()
    timestamp = datetime.now(timezone.utc).strftime("%Y-%m-%d")
    entry = f"\n## [{timestamp}] {operation} | {detail}\n"
    with open(_LOG_FILE, "a") as f:
        f.write(entry)


# ---------------------------------------------------------------------------
# Public API
# ---------------------------------------------------------------------------

def write_page(
    category: str,
    title: str,
    content: str,
    summary: str = "",
) -> str:
    """
    Write (or overwrite) a wiki page.

    The agent provides the full markdown content. This method handles:
    - Writes the .md file to the appropriate category subfolder.
    - Updates index.md with a one-line entry.
    - Appends an entry to log.md.

    Args:
        category: One of "entity", "summary", "topic".
        title:    Human-readable page title (used for slug + index).
        content:  Full markdown content the agent wrote.
        summary:  One-line summary for the index (optional; auto-extracted if empty).

    Returns:
        Relative path from wiki root (e.g. "entities/memory-leak.md").
    """
    _ensure_dirs()
    slug = _slug(title)
    path = _page_path(category, slug)

    # Auto-extract first non-heading, non-empty line as summary if not provided
    if not summary:
        for line in content.splitlines():
            stripped = line.strip()
            if stripped and not stripped.startswith("#"):
                summary = stripped[:100]
                break

    path.write_text(content)

    # Update index
    entries = _load_index()
    existing = next((e for e in entries if e["slug"] == slug), None)
    if existing:
        existing["summary"] = summary
        existing["date"] = _now_iso()
    else:
        entries.append({
            "slug": slug,
            "category": category,
            "summary": summary,
            "date": _now_iso(),
        })
    _save_index(entries)
    _append_log("write", title)

    return str(path.relative_to(_WIKI_DIR))


def read_page(category: str, title: str) -> str | None:
    """Read a wiki page's content. Returns None if not found."""
    slug = _slug(title)
    path = _page_path(category, slug)
    if not path.exists():
        return None
    return path.read_text()


def read_page_by_slug(slug: str) -> str | None:
    """Read a wiki page by slug, searching across all categories."""
    for d in list(_CATEGORY_DIRS.values()) + [_WIKI_DIR]:
        path = d / f"{slug}.md"
        if path.exists():
            return path.read_text()
    return None


def search_wiki(query: str) -> list[dict]:
    """
    Simple keyword search over all wiki pages.
    Returns list of {slug, category, path, snippet} sorted by relevance.
    """
    query_tokens = set(re.findall(r"[a-z0-9]+", query.lower()))
    if not query_tokens:
        return []

    results = []
    for category, base_dir in _CATEGORY_DIRS.items():
        if not base_dir.exists():
            continue
        for page_path in base_dir.glob("*.md"):
            content = page_path.read_text().lower()
            content_tokens = set(re.findall(r"[a-z0-9]+", content))
            overlap = len(query_tokens & content_tokens)
            if overlap > 0:
                # Extract a short snippet around first match
                first_token = next(iter(query_tokens & content_tokens), "")
                idx = content.find(first_token)
                snippet = content[max(0, idx - 30):idx + 80].replace("\n", " ").strip()
                results.append({
                    "slug": page_path.stem,
                    "category": category,
                    "path": str(page_path.relative_to(_WIKI_DIR)),
                    "score": overlap,
                    "snippet": snippet,
                })

    results.sort(key=lambda x: x["score"], reverse=True)
    return results


def list_pages(category: str | None = None) -> list[dict]:
    """List all wiki pages, optionally filtered by category."""
    entries = _load_index()
    if category:
        return [e for e in entries if e["category"] == category]
    return entries


def get_log(last_n: int = 20) -> list[str]:
    """Return the last N log entries from log.md."""
    if not _LOG_FILE.exists():
        return []
    lines = _LOG_FILE.read_text().splitlines()
    entries = [l for l in lines if l.startswith("## [")]
    return entries[-last_n:]


def lint_wiki() -> dict:
    """
    Health-check the wiki as described in Karpathy's LLM Wiki pattern.

    Checks for:
    - Orphan pages (in directory but not in index)
    - Missing pages (in index but file deleted)
    - Broken wikilinks ([[slug]] pointing to non-existent file)
    - Pages with no wikilinks (isolated pages)

    Returns:
        {
          "orphan_pages": [...],
          "missing_pages": [...],
          "broken_wikilinks": {slug: [broken_links]},
          "isolated_pages": [...],
        }
    """
    index_entries = {e["slug"] for e in _load_index()}
    file_slugs: dict[str, Path] = {}
    for d in _CATEGORY_DIRS.values():
        if d.exists():
            for p in d.glob("*.md"):
                file_slugs[p.stem] = p

    orphans = [s for s in file_slugs if s not in index_entries]
    missing = [s for s in index_entries if s not in file_slugs]

    broken_wikilinks: dict[str, list[str]] = {}
    isolated: list[str] = []
    all_slugs = set(file_slugs.keys())

    for slug, path in file_slugs.items():
        content = path.read_text()
        links = re.findall(r"\[\[([^\]]+)\]\]", content)
        if not links:
            isolated.append(slug)
        broken = [lnk for lnk in links if _slug(lnk) not in all_slugs]
        if broken:
            broken_wikilinks[slug] = broken

    return {
        "orphan_pages": orphans,
        "missing_pages": missing,
        "broken_wikilinks": broken_wikilinks,
        "isolated_pages": isolated,
    }

Requirements and compatibility

Written for: GitHub Copilot

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs Python, Needs Node.js

Security

What our scanner noticed· Low risk

  • Scripts that your agent may run

This skill includes scripts that your agent may run. Read them before you use it.

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

First committed by
GitHub avatar of fizznixfizznix
Publisher
GitHub (Official)
Distributed by
GitHub
Source
github/awesome-copilot · skills/mini-context-graph · commit 797337f
Licence
MIT License (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
Synced
Contributors
fizznix, stbenjam

AutoGineer is not affiliated with or endorsed by fizznix or GitHub.

Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download mini-context-graph.zip