Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add commands/fwornle/coding/graphifygit clone --depth 1 https://github.com/fwornle/codingWhat 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 | $0.00080 | $0.00682 |
| Opus 5 | $0.00040 | $0.00341 |
| Sonnet 5 | $0.00016 | $0.00136 |
| Haiku 4.5 | $0.00008 | $0.00068 |
Grade A, and why
graphify 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 yesterday.
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 — 55 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/graphify
Graphify turns this repo into a navigable code knowledge graph (tree-sitter AST → static
graph.json), served over an HTTP MCP endpoint. All Python runs inside the coding-services
container — the host graphify command (bin/graphify) forwards to it via docker exec.
- Graph output:
.data/graphify/graphify-out/graph.json(bind-mounted;built_at_commitstamps the indexed commit) - MCP endpoint:
http://localhost:3851/mcp(tools:query_graph,get_node,get_neighbors,shortest_path,graph_stats,god_nodes, …) - Host CLI:
graphify …(shim → container)
When to use
Prefer this over grepping the codebase for structural questions ("how does X work?", "what calls Y?", "trace the flow through Z", "what depends on this module?"). Query the graph first; fall back to grep only if the graph lacks the answer.
Querying (fast path — graph already built)
Use the MCP tools (mcp__graphify__query_graph, get_node, get_neighbors,
shortest_path, god_nodes) when available. Equivalent CLI via the shim:
graphify query "How does the ETM watchdog reclaim a stalled session?" # BFS, broad context
graphify query "what calls captureForegroundTokens" --dfs # DFS, trace a path
graphify path "ObservationWriter" "obs-api" # shortest path between two concepts
graphify explain "CodeGraphAgent" # plain-language node explanation
graphify god-nodes --top 20 # most-connected hubs
Rebuilding the graph
The dashboard shows how many commits behind the graph is and has a Re-index button. To rebuild from the CLI:
graphify update /workspace/coding # incremental (AST only, no LLM) — fast, use this most of the time
graphify extract /workspace/coding # full re-extract incl. docs/PDF semantic pass (routes docs LLM via the proxy)
graphify extract /workspace/coding --code-only # full code re-extract, no LLM/network
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.
- yesterday First seen · 55 lines · 80 tokens per session scan A 0d0879ad4e37
graphify is a command published in the GitHub repository fwornle/coding (2 stars, last pushed yesterday), licensed MIT. It adds 80 tokens to every session and 682 once invoked, about $0.0004 per session on Opus 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 commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.