aar-ipython-codegraph

An optional workflow for navigating source-like files exported from AAR programmable IPython workspaces using an already installed CodeGraph tool. IPython is an interactive Python environment, and AAR is the system that provides the workspace artifacts.

In plain words
What is it for?
Use it to resolve and verify selected workspace artifacts, create a safe local navigation folder, and map each file back to its original artifact and checksum.
Why use it?
It helps inspect relationships among exported cells, modules, tracebacks, and source files without making CodeGraph part of the AAR runtime.

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/phenomenoner/adaptive-agent-harness/aar-ipython-codegraph
Any agent
npx skills add phenomenoner/adaptive-agent-harness --skill aar-ipython-codegraph
Clone the repo
git clone --depth 1 https://github.com/phenomenoner/adaptive-agent-harness

Made for: Claude Code, Codex.

Per session 67 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 682 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 $0.00067 $0.00682
Opus 5 $0.00034 $0.00341
Sonnet 5 $0.00013 $0.00136
Haiku 4.5 $0.00007 $0.00068

Measured 2d ago against content hash 77ec795673a9, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

aar-ipython-codegraph 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.

profiles/codex/plugins/adaptive-agent-runtime/skills/aar-ipython-codegraph/SKILL.md · 52 lines

How it starts

The opening of the file, as written. The whole thing — 52 lines — stays where its author put it; the contents beside it link to each section on GitHub.

AAR IPython CodeGraph

This is an optional external workflow. It is not a workspace backend, broker provider, authority source, or part of AAR core. Never install CodeGraph, alter host configuration, or initialize an index merely because this skill is present; use it only when the caller chose the workflow and a CodeGraph command or tool is already available.

Establish the artifact boundary

  1. Use $aar-operations and aar_capabilities to confirm the current runtime, tool surface, artifact schema, and operation binding.
  2. Obtain complete artifact references from a programmable workspace result, checkpoint manifest, RLM trace, or operation receipt. Resolve each selected reference through aar_artifact_resolve with an explicit max_bytes limit and allow_redacted=true only when disclosure is authorized.
  3. Verify artifact ID, SHA-256 digest, media type, size, creating operation, and redaction state before decoding content_base64. Reject any mismatch.
  4. Materialize only source-like, non-secret content into a task-local navigation directory. Keep a manifest mapping each safe relative filename to its original AAR artifact reference and digest.

Do not index the live .aar database, SQLite WAL files, worker sockets, checkpoint stores, credential locations, private evidence trees, runtime homes, or an entire user directory. Do not materialize arbitrary pickle data. A checkpoint exclusion is not permission to disclose it.

Build or refresh the external graph

  • Prefer a current task-local .codegraph/ index. Use the callable CodeGraph exploration tool when the host exposes it; otherwise use the installed codegraph status, codegraph init, codegraph sync, and codegraph explore commands.
  • Initialize only the bounded materialized source root. Refresh after its manifest or source bytes change. Keep .codegraph/ ignored and outside AAR evidence, release, and checkpoint contracts.
  • Ask structural questions with exact artifact filenames, symbols, or call paths. Use the graph to navigate, then read the referenced artifact bytes before making a correctness claim.
  • Preserve artifact digests beside any answer derived from the graph. CodeGraph catalog, status, or index presence proves navigation metadata only; it does not prove artifact authenticity, runtime activation, execution, authority, or delivery.

Read the full file on GitHub · 52 lines

Files

What ships with it

1 file 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.

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 · 52 lines · 67 tokens per session scan A 77ec795673a9

Subscribe to this mod's changes

aar-ipython-codegraph is a skill published in the GitHub repository phenomenoner/adaptive-agent-harness (1 stars, last pushed 2d ago), licensed MIT. It adds 67 tokens to every session and 682 once invoked, about $0.0003 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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