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 skills/richfrem/agent-plugins-skills/graph-executionnpx skills add richfrem/agent-plugins-skills --skill graph-executiongit clone --depth 1 https://github.com/richfrem/agent-plugins-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/richfrem/agent-plugins-skills/graph-execution)<a href="https://agentmods.dev/skills/richfrem/agent-plugins-skills/graph-execution"><img src="https://agentmods.dev/badge/skills/richfrem/agent-plugins-skills/graph-execution.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 | $0.00033 | $0.01253 |
| Opus 5 | $0.00016 | $0.00626 |
| Sonnet 5 | $0.00007 | $0.00251 |
| Haiku 4.5 | $0.00003 | $0.00125 |
Grade A, and why
graph-execution 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 today.
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 — 101 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Graph-Planned Execution (Deterministic State Machine Protocol)
Provides a deterministic, graph-planned execution primitive for tasks requiring formal state tracking, non-negotiable human approval gates, isolated worktree sandboxing, and guaranteed safe rollback.
1. Overview: Loops vs. Graphs
While standard agent loops rely on conversational context to decide next steps, Graph Execution models tasks as a formal Directed Acyclic Graph (DAG) or finite state machine:
stateDiagram-v2
[*] --> TRIAGE: Task / Error Trigger
TRIAGE --> PLAN: 4-Box Automation Gate Passed
PLAN --> AWAITING_APPROVAL: Transaction Manifest Formulated
AWAITING_APPROVAL --> AUTHORIZED: User Explicit Approval ("Proceed")
AUTHORIZED --> CREATE_WORKTREE: Worktree Sandbox Initialized
CREATE_WORKTREE --> EXECUTE: Mutation Attempt (1-3)
EXECUTE --> VERIFY_GATE: Objective Verifier Run
VERIFY_GATE --> PRE_COMMIT_RECEIPT: Verifier Passed (exit 0)
VERIFY_GATE --> PLAN: Verifier Failed (Attempts < 3)
VERIFY_GATE --> ROLLBACK: Verifier Failed (Attempts == 3)
PRE_COMMIT_RECEIPT --> COMMIT: Staged Tree Validated & Committed
ROLLBACK --> FINAL_RECEIPT: Layer 2 Transferred & Worktree Removed
COMMIT --> FINAL_RECEIPT: Local Cycle Successful
FINAL_RECEIPT --> [*]: Terminal Receipt Logged
2. The Core Invariants
- State in Files, Not Prompts:
The active node, attempt count, and operation bounds are stored in
.agent/learning/evolution_state.jsonand mediated by a deterministic controller script (e.g.,evolution_state.py). - Proposal Mode Invariant (Read-Only Planning):
Prior to explicit user approval (
AWAITING_APPROVAL), the agent executes zero state-changing operations, creates no git worktrees, and runs no mutations. - Transactional Worktree Sandboxing:
Mutations are executed inside an isolated git worktree (
../worktree-evolution-<cid>), keeping the main checkout 100% clean during execution. - Verifier Sovereignty: The agent cannot modify the test scripts, holdout sets, or evaluation criteria that judge its own work. Pre-execution hashes of verifiers are locked.
- Asymmetric Persistence:
If a task fails after maximum retry attempts (e.g., 3 attempts):
- Code changes are safely rolled back and the worktree is deleted.
- Failure learnings, negative constraints, and reproduction notes are permanently preserved in Layer 2 Markdown playbooks and debt logs before worktree teardown.
- Integrity Receipts:
No commit is permitted without an Evolution Integrity Receipt (
EVO-INTEGRITY-<cid>-<hash>) binding the exact staged git tree to the audit event sequence.
What ships with it
3 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.
- today First seen · 101 lines · 33 tokens per session scan A 83ed0b2c2523
graph-execution is a skill published in the GitHub repository richfrem/agent-plugins-skills (6 stars, last pushed today), licensed MIT. It adds 33 tokens to every session and 1,253 once invoked, about $0.0002 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 2026-09-03.
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