Graphify explain

Skill kornevdima/adlc/skills/graphify-explain

Plain-language explanation of one node in the project's graphify code graph: source file, type, community membership, and all incoming/outgoing edges with relations and confidence tags. Cheap (no LLM cost — direct graph lookup). Use for "what is X", "explain this function", "show me what touches Y" when Y is a single named symbol. Triggers on: "/graphify-explain", "explain node", "what is X (in the graph)", "describe node", "neighbors of", "what touches X", "show me X's connections" (when about code).From its SKILL.md

Install

npx -y skills add kornevdima/adlc --skill graphify-explain

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

2 stars2 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

SKILL.md

3.6 KB, as published. Nobody here has run it

graphify-explain: node + immediate neighbors

Wraps python -m graphify explain "X". Returns the node's metadata (source file, type, community) and its full edge list with relations and confidence tags. Cheapest and most-used graph query.

When to invoke

The project has graphify-out/graph.json.
The user names one specific thing and wants to know what it is or what it touches: "what is AppDelegate?", "explain getOAuthConfig", "show me everything connected to saveSession".
For "how does A reach B" use graphify-path; for open-ended exploration use graphify-query.

If graph.json is missing, tell the user to run /graphify-ingest first.

Steps

Step 1 — Resolve target path

TARGET="${1:-$PWD}"
TARGET=$(cd "$TARGET" && pwd)
GRAPH="$TARGET/graphify-out/graph.json"
[ -f "$GRAPH" ] || { echo "No code graph at $GRAPH. Run /graphify-ingest first."; exit 1; }

Step 2 — Resolve a working Python with graphifyy

PYTHON=""
PIN_FILE="$TARGET/graphify-out/.graphify_python"
if [ -f "$PIN_FILE" ]; then
    PINNED=$(cat "$PIN_FILE")
    if [ -x "$PINNED" ] && "$PINNED" -c "import graphify" 2>/dev/null; then
        PYTHON="$PINNED"
    fi
fi
if [ -z "$PYTHON" ]; then
    for cand in python3 python; do
        if command -v "$cand" >/dev/null 2>&1 && "$cand" -c "import graphify" 2>/dev/null; then
            PYTHON="$cand"; break
        fi
    done
fi
[ -z "$PYTHON" ] && { echo "graphifyy not installed. Run bin/setup-graphify.sh on this project."; exit 1; }

Step 3 — Run explain

cd "$TARGET" && "$PYTHON" -m graphify explain "$NODE"

Pass the user's name verbatim. graphify matches against node labels (substring); if multiple nodes match, it returns the highest-degree match — surface that to the user so they can pick a different name if they meant a different node.

Step 4 — Present the result

graphify's output structure:

Node: <label>
  ID: <node_id>
  Source: <file:line>
  Type: <code | document | paper | image>
  Community: <cluster_id>
  Degree: <total connections>

Connections (N):
  --relation [confidence]--> <neighbor>
  <-- relation [confidence] -- <inbound>
  ...

When presenting in chat:

Lead with the one-line summary: "X is a [type] in [file:line] in cluster N (label '<community label from labels.json>'), with K connections."
Group connections by relation (calls, references, imports, semantically_similar_to, contains, etc.).
Flag any INFERRED or AMBIGUOUS edges — those are the model's guesses, not literal source-derived facts.
If the user asked "what is X?", lean on the source file pointer and node label. If they asked "what touches X?", lead with the connections.

Cost

$0. Single graph lookup.

Cross-skill notes

Often the cheapest first step before deeper exploration. After explain, follow up with graphify-path (to trace from this node to another) or graphify-query (to widen the search to the whole community).
The community label resolution is via graphify-out/labels.json — read it to map the cluster_id graphify reports into the human name.