graph

graph is a skill for Claude Code from naimkatiman/continuous-improvement. It costs 19 tokens per session (1,345 once invoked), scanned A, a copy of graph, MIT.

A runtime for executing repeatable multi-step workflows described as a directed graph, where each step and dependency is declared in advance. It records progress in a journal so an interrupted run can resume instead of starting over.

In plain words
What is it for?
Use it for deterministic pipelines such as build-then-test, with steps that can run after their prerequisites and pause before later actions when required.
Why use it?
It makes dependencies and execution results auditable and allows long-running work to recover after a crash or restart. It is not intended for exploratory work or workflows that need adaptive replanning.

Skill for Claude Code

Written for Claude Code: argument-hint in frontmatter.

Part of the oh-my-claudecode plugin — 36 skills, 17 agents shipped together

Good fit Use it for deterministic pipelines such as build-then-test, with steps that can run after their prerequisites and pause before later actions when required.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/naimkatiman/continuous-improvement/graph
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.

Any agent
npx skills add naimkatiman/continuous-improvement --skill graph
Clone the repo
git clone --depth 1 https://github.com/naimkatiman/continuous-improvement

Made for: Claude Code.

Or install oh-my-claudecode, the plugin that ships this one along with the rest of its 36 skills, 17 agents.

Wrote 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.

agentmods badge for graph

README.md
[![agentmods](https://agentmods.dev/badge/skills/naimkatiman/continuous-improvement/graph/github.svg)](https://agentmods.dev/skills/naimkatiman/continuous-improvement/graph)
Your own site
<a href="https://agentmods.dev/skills/naimkatiman/continuous-improvement/graph"><img src="https://agentmods.dev/badge/skills/naimkatiman/continuous-improvement/graph/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for graph

Your own site · 80×15
<a href="https://agentmods.dev/skills/naimkatiman/continuous-improvement/graph"><img src="https://agentmods.dev/badge/skills/naimkatiman/continuous-improvement/graph.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 19 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,345 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin 100% copy Near-identical to another mod 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 5.1 $0.00019 $0.01345
Opus 5 $0.00010 $0.00673
Sonnet 5 $0.00004 $0.00269
Haiku 4.5 $0.00002 $0.00135

Measured 2d ago against content hash c0ebe6ddc524, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

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.

Origin

This is a copy

100% identical to graph — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

third-party/oh-my-claudecode/skills/graph/SKILL.md · 116 lines

How it starts

The opening of the file, as written. The whole thing — 116 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Graph Skill

Run a deterministic orchestration graph from a declarative JSON descriptor. The runtime consumes the sealed-descriptor and pure-scheduler contracts in src/graph/* and executes through an independent OS process (omc graph run), so crash recovery (kill mid-run, rerun, resume from journal) works for real.

Usage

/oh-my-claudecode:graph <descriptor.json>
/oh-my-claudecode:graph "build then test then ask me before deploy"   (author the descriptor first)

The execution surface is always the CLI subcommand:

omc graph run <descriptor.json> [--runs-root <dir>]

Run it via the Bash tool for non-interactive graphs. Progress lines stream as [run], [node], [ok], [fail], [join], [done].

When To Use

  • Repeatable multi-step pipelines with explicit dependencies (DAG)
  • Work that must survive interruption: kill/restart resumes from journal
  • Auditable runs: OCC journal + projection snapshots under .omc/graph-runs/<run_id>/

When NOT to use: exploratory one-off work (use conversation or /team); anything needing adaptive re-planning mid-run (graphs are deterministic).

Workflow

  1. Descriptor given -> go to step 3.

  2. Pipeline described -> author the descriptor JSON (schema below), write it next to the project (suggest .omc/graphs/<name>.json) and show it to the user before running. run_id must be unique per logical pipeline; rerunning with the same run_id RESUMES, not restarts.

  3. Approval nodes: if the descriptor contains any "kind": "human-approval" node, do NOT run it through the Bash tool (stdin is not interactive there; EOF fails closed to denied). Tell the user to run interactively instead:

    ! omc graph run <file>
    

    The ! prefix runs it inside this session with live stdin so y/n works.

  4. Run and relay progress. Exit codes (normative): 0 succeeded | 1 terminal failed | 19 another writer owns this run (busy) 20 corrupt/tampered journal (fail-closed) | 21 descriptor drift on resume | 70 runtime crash (unmapped error)

  5. Resume: rerunning the same command after a crash replays committed transitions and continues. Completed nodes never re-execute.

Read the full file on GitHub · 116 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 · 116 lines · 19 tokens per session scan A c0ebe6ddc524

Subscribe to this mod's changes

graph is a skill published in the GitHub repository naimkatiman/continuous-improvement (7 stars, last pushed 3d ago), licensed MIT. It adds 19 tokens to every session and 1,345 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to graph, differing in 0 lines, and is treated as a copy.

Related

Other skills, from other repositories

opensrc

Fetch dependency source code to give AI agents deeper implementation context. Use when the agent needs to understand how a library works internally, read source code for a package, fetch implementation details for a dependency, or explore how an npm/PyPI/crates.io package is built. Triggers include "fetch source for"…

vercel-labs/opensrc · 103 tokens

nopus-configure

Configure nopus complexity sensitivity, extra-simple rewrites, rewrite evidence, and Pi response hiding for this user.

Vistyy/nopus · 27 tokens

nopus-simplify

Rewrite the immediately preceding assistant response with clearer and more direct prose when the user invokes this skill.

Vistyy/nopus · 25 tokens

agentpush

Bridge an imported agentpush MCP server (Telegram, WhatsApp, etc.) with live agentproto sessions via the daemon's transmitter subsystem: transmitmessage sends outbound and binds the recipient to a session, inboundwatcherstart polls agentpush for new messages, and inboundendpointcreate/POST /inbound(/:slug) route…

agentproto/ts · 119 tokens

bureau

Drive Bureau — the browser stack's installable capability server: a stealth Firefox (Camofox) + daemon that exposes browser tools as MCP-over-HTTP on :8830, plus a CLI for saved browser identities (sessions), social capture / search, adapter health probes, declarative workflows, and Guilde connection. Use when working…

agentproto/ts · 131 tokens

agentproto-apps

Operate and build Agentproto apps — AIP-42 app bundles (defineApp().emit(dir), APP.md + agents/ + workflows/ + ui/) and their daemon lifecycle (appinstall, appapply, apprun, appstatus, appstop) plus the app-scoped durable data plane (appdataread/write/list/migrate). Covers serving one with a UI (agentproto app serve →…

agentproto/ts · 178 tokens