loop-graph

A planning tool that turns a long, multi-round request into a graph of agent steps and saved work records. It also prepares prompts for handing the work between hosts.

In plain words
What is it for?
Use it for custom projects with checkpoints, independent reviews, work across different computers or agents, and durable progress records. It is not intended for the predefined code-cleanup, delivery, or research workflows covered by related tools.
Why use it?
It keeps complex work aligned with one agreed standard and preserves its state between rounds. An independent supervisor checks claimed results so the work does not gradually drift.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/levi-qiao/longgraph-skill/loop-graph
Any agent
npx skills add levi-qiao/longgraph-skill --skill loop-graph
Clone the repo
git clone --depth 1 https://github.com/levi-qiao/longgraph-skill

Made for: Claude Code, Codex.

Per session 84 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 4,999 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00084 $0.04999
Opus 5 $0.00042 $0.02499
Sonnet 5 $0.00017 $0.01000
Haiku 4.5 $0.00008 $0.00500

Measured 2d ago against content hash 5a80851e182b, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

loop-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 2d 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.

skills/loop-graph/SKILL.md · 226 lines

How it starts

The opening of the file, as written. The whole thing — 226 lines — stays where its author put it; the contents beside it link to each section on GitHub.

loop-graph — a graph of agent nodes, not a drifting loop

Module boundary

This is the shared compiler, not a goal-specific workflow. It owns the graph shape, the five runtime artifact schemas, generic interview mechanics, and host handoff. A focused preset owns only its North Star, proof, interview questions, recommended work shape, method guards, knobs, and slug; it never forks templates or adds a runtime node.

Use ../loop-converge/SKILL.md for code cleanup, ../loop-deliver/SKILL.md for requirement delivery, and ../loop-research/SKILL.md for evidence-led solution selection. Use this skill directly only when a custom pack is genuinely needed. The exact contract is in docs/preset-contract.md.

What it does & why

Turns a vague long-horizon request ("make it production-ready", "accuracy above baseline", "finish the migration") into a small graph of agent nodes that stays on-spec across many rounds:

  • an executor node — drives the work round by round against a single scoreboard;
  • a supervisor node — audits the work from outside the executor's context (re-verifying claimed-done against the shared standards) and corrects course before drift compounds.

Why a graph, not a loop: an agent grinding a long task is inside the context that drifted, so it rationalizes scope creep and calls half-done work "done". The nodes talk only through durable, inspectable state (ledger, git tree, directives file) — never a shared, polluted context.

Each node runs on its own timer, and no node ever wakes another. The executor fires, closes several verified rounds on warm context, and ends. The supervisor fires on a slower cadence, audits, appends corrections, and ends. A correction is picked up on the executor's next fire. Two independent clocks and one file edge — no dispatch, no resume prompt, no liveness protocol, and therefore nothing that can leave the run waiting for a wake that never comes.

Read the full file on GitHub · 226 lines

Changes

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.

  1. 2d ago First seen · 226 lines · 84 tokens per session scan A 5a80851e182b

Subscribe to this mod's changes

loop-graph is a skill published in the GitHub repository levi-qiao/longgraph-skill (67 stars, last pushed 13d ago), licensed MIT. It adds 84 tokens to every session and 4,999 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 2026-08-30.

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