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 phenomenoner/adaptive-agent-harness --skill aar-ipython-codegraphgit clone --depth 1 https://github.com/phenomenoner/adaptive-agent-harnessWrote 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/phenomenoner/adaptive-agent-harness/aar-ipython-codegraph)<a href="https://agentmods.dev/skills/phenomenoner/adaptive-agent-harness/aar-ipython-codegraph"><img src="https://agentmods.dev/badge/skills/phenomenoner/adaptive-agent-harness/aar-ipython-codegraph/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.
<a href="https://agentmods.dev/skills/phenomenoner/adaptive-agent-harness/aar-ipython-codegraph"><img src="https://agentmods.dev/badge/skills/phenomenoner/adaptive-agent-harness/aar-ipython-codegraph.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00067 | $0.00682 |
| Opus 5.5 | $0.00027 | $0.00273 |
| Sonnet 5.5 | $0.00013 | $0.00136 |
| Haiku 4.5 | $0.00007 | $0.00068 |
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 1mo 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 — 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
- Use
$aar-operationsandaar_capabilitiesto confirm the current runtime, tool surface, artifact schema, and operation binding. - Obtain complete artifact references from a programmable workspace result, checkpoint manifest,
RLM trace, or operation receipt. Resolve each selected reference through
aar_artifact_resolvewith an explicitmax_byteslimit andallow_redacted=trueonly when disclosure is authorized. - Verify artifact ID, SHA-256 digest, media type, size, creating operation, and redaction state
before decoding
content_base64. Reject any mismatch. - 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 installedcodegraph status,codegraph init,codegraph sync, andcodegraph explorecommands. - 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.
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.
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.
- 1mo ago First seen · 52 lines · 67 tokens per session scan A 77ec795673a9
aar-ipython-codegraph is a skill published in the GitHub repository phenomenoner/adaptive-agent-harness (1 stars, last pushed 1mo ago), licensed MIT. It adds 67 tokens to every session and 682 once invoked, about $0.0003 per session on Opus 5.5. 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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