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 skills add AnthonyAlcaraz/agentic-graph-rag-skills --skill execution-graphgit clone --depth 1 https://github.com/AnthonyAlcaraz/agentic-graph-rag-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/anthonyalcaraz/agentic-graph-rag-skills/execution-graph)<a href="https://agentmods.dev/skills/anthonyalcaraz/agentic-graph-rag-skills/execution-graph"><img src="https://agentmods.dev/badge/skills/anthonyalcaraz/agentic-graph-rag-skills/execution-graph.svg" alt="Measured on agentmods" height="20"></a>What 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 5.1 | $0.00194 | $0.01953 |
| Opus 5 | $0.00097 | $0.00977 |
| Sonnet 5 | $0.00039 | $0.00391 |
| Haiku 4.5 | $0.00019 | $0.00195 |
Grade A, and why
execution-graph 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 6d ago.
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 — 147 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Execution Graph
Overview
The execution graph is the dynamic counterpart to the static workflow graph (Ch5). The workflow graph is the blueprint; the execution graph is the autobiography. Each query produces one execution graph instance — immutable after completion, queryable, the substrate for cognitive autopsy.
Each node represents one atomic operation:
- Node ID — unique per operation instance (span_id from OpenTelemetry in production)
- Node type —
LLM_Call,Tool_Invocation,Retrieval,Decision_Point - Timestamp — high-resolution for latency analysis
- Input payload — exact data received
- Output payload — exact data produced
- Performance metrics — latency_ms, token_count, cost_usd
- Parent-child edges —
TRIGGEREDrelationships establishing causal lineage
Two-phase write is the central primitive (the chapter's execution-graph example):
- Phase 1 (on-start): create node + link to parent. If the operation fails or crashes, the graph still captures what was about to happen.
- Phase 2 (on-complete): fill in output + latency + cost.
A simple one-phase write loses the causal structure when nodes fail — exactly the cases that need diagnosis most.
When to Use
- BEFORE building any Ch7 evaluation layer (0/1/2/3) — they all query the execution graph
- Multi-step agent workflows where you need to attribute a failure to a specific node (the chapter quote: "the error isn't lost in a sea of model parameters")
- Self-evolution loops — every evolution decision needs the execution graph as input
- DevOps incident reconstruction — answer "which node failed and what preceded it"
Phrases: "execution graph", "trace the agent's run", "cognitive autopsy", "causal chain", "which node failed", "self-evolution foundation".
When NOT to Use
- One-shot single-call agents — no graph to trace; just log the call
- Production systems where you already pipe OpenTelemetry → Neo4j and have execution-graph reconstruction working
- High-frequency request paths where the per-node Cypher write latency blocks the request — buffer to async writer or sample
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 6d ago First seen · 147 lines · 194 tokens per session scan A 5230c7bdac5d
execution-graph is a skill published in the GitHub repository AnthonyAlcaraz/agentic-graph-rag-skills (8 stars, last pushed 1mo ago), licensed MIT. It adds 194 tokens to every session and 1,953 once invoked, about $0.0010 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.
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