Agent-Skills.md

Agent Skills: Mini Context Graph Skill

|

UncategorizedID: github/awesome-copilot/mini-context-graph

Repository

github/awesome-copilot
githubLicense: MIT
38,9014,927

Install this agent skill to your local

pnpm dlx add-skill https://github.com/github/awesome-copilot/tree/HEAD/skills/mini-context-graph

Skill Files

Browse the full folder contents for mini-context-graph.

Download Skill

Loading file tree…

skills/mini-context-graph/SKILL.md

Skill Metadata

Name
mini-context-graph
Description
|

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

| Layer | Where | What the LLM does | What Python does | |-------|-------|-------------------|-----------------| | Raw Sources | data/documents.json | Reads (never modifies) | Stores chunks + metadata | | Wiki | wiki/ (markdown) | Writes/updates pages | Manages index.md + log.md | | Graph | data/graph.json | Extracts entities + relations | Persists, 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

| Method | Purpose | When to Use | |--------|---------|-------------| | skill.ingest_with_content(doc_id, title, source, raw_content, entities, relations) | Full RAG ingest: raw docs + graph + provenance | Every 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 relation | Quick additions without a source doc | | skill.query(query) | Graph-only retrieval → subgraph | Structural queries | | skill.query_with_evidence(query) | Graph + provenance → subgraph + source chunks | Queries requiring citations | | wiki_store.write_page(category, title, content, summary) | Write/update a wiki page | After every ingest; after answering queries | | wiki_store.read_page(category, title) | Read a wiki page | Before answering; for cross-referencing | | wiki_store.search_wiki(query) | Keyword search across wiki | Fast path before graph traversal | | wiki_store.list_pages(category) | List all wiki pages | Getting an overview | | wiki_store.get_log(last_n) | Read recent operations | Understanding wiki history | | wiki_store.lint_wiki() | Health check | Periodic maintenance | | documents_store.list_documents() | List all ingested raw sources | Audit / provenance checking | | documents_store.search_chunks(query) | Chunk-level search | Finding 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

| Layer | What Happens | Who Owns It | |-------|-----------|-------------| | LLM Reasoning | Extraction, synthesis, writing wiki pages | Agent (.md guidance files) | | Wiki Persistence | Index, log, file I/O | wiki_store.py | | Graph Persistence | Dedup, index, BFS traverse | graph_store.py, retrieval_engine.py | | Raw Source Storage | Immutable docs + chunks + provenance | documents_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.