deep-analyst

deep-analyst is an agent for Claude Code from MadAppGang/magus. It costs 108 tokens per session (3,207 once invoked), scanned A, original, MIT.

A guide and toolkit for madbench, a Go program that benchmarks coding agents by running scenarios and checking their results.

In plain words
What is it for?
It helps write bench files, prepare test data, choose checks, run controls, and tune or debug evaluation results.
Why use it?
It gives you a defined way to measure whether an agent completes coding tasks correctly and to investigate failed evaluations.

Agent for Claude Code

Written for Claude Code: shipped in a Claude Code plugin. Also seen: mentions subagents.

Part of the multimodel plugin — 2 skills, 2 commands, 1 agent shipped together

Good fit It helps write bench files, prepare test data, choose checks, run controls, and tune or debug evaluation results.

Compare 6 agents from other repositories ↓
Install with agentmods
npx agentmods add agents/madappgang/magus/deep-analyst
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.

Clone the repo
git clone --depth 1 https://github.com/MadAppGang/magus

Made for: Claude Code.

Or install multimodel, the plugin that ships this one along with the rest of its 2 skills, 2 commands, 1 agent.

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 deep-analyst

README.md
[![agentmods](https://agentmods.dev/badge/agents/madappgang/magus/deep-analyst/github.svg)](https://agentmods.dev/agents/madappgang/magus/deep-analyst)
Your own site
<a href="https://agentmods.dev/agents/madappgang/magus/deep-analyst"><img src="https://agentmods.dev/badge/agents/madappgang/magus/deep-analyst/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 deep-analyst

Your own site · 80×15
<a href="https://agentmods.dev/agents/madappgang/magus/deep-analyst"><img src="https://agentmods.dev/badge/agents/madappgang/magus/deep-analyst.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 108 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 3,207 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 1 finding. A grade says what 26 rules found in the file — not that it is safe.
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 5.1 $0.00108 $0.03207
Opus 5.5 $0.00043 $0.01283
Sonnet 5.5 $0.00022 $0.00641
Haiku 4.5 $0.00011 $0.00321

Measured 3d ago against content hash eebd67505992, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

deep-analyst scanned grade A with 1 finding 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 3d 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.

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

1. Fetch the raw source (curl via Bash, or the raw API), strip markup,
plugins/multimodel/agents/deep-analyst.md · 239 lines

How it starts

The opening of the file, as written. The whole thing — 239 lines — stays where its author put it; the contents beside it link to each section on GitHub.

<critical_constraints> <file_based_delivery> Files are the handoff, not your final message. When you are launched with run_in_background: true, the caller receives a launch receipt — your returned text never reaches them. Write everything that matters under a session directory and make your final message the <completion_message> in <formatting> — its Artifacts section carries the report path.

If the prompt provides SESSION_PATH, use it. Otherwise create one:
`ai-docs/sessions/deep-analyst-$(date +%Y%m%d-%H%M%S)/` — session scratch,
git-ignored, never cited as authority after this run.

</file_based_delivery>

<evidence_integrity> Quote only from raw bytes you fetched yourself. Summarizing fetch layers fabricate citations — they have been caught inventing quotes that do not exist on the page, and returning an unbylined page's wording as "ecosystem consensus". For every load-bearing quote: 1. Fetch the raw source (curl via Bash, or the raw API), strip markup, and grep for the exact string before shipping it. 2. Label the retrieval method next to the quote: raw (bytes you read) vs summarized (a fetch layer's paraphrase — never quotable). 3. Read the enclosing passage, not just the matching sentence — a quote that reads as a ruling can sit inside an argument for the opposite position, or inside a change that was accepted despite the complaint. 4. An HTTP 200 can carry a bot-challenge page instead of content. Status codes are not retrieval evidence; the grep is. When quoting code, decode the licence file at the cited ref — the platform's licence field is unreliable, and projects relicense after the commit you are citing. </evidence_integrity>

<absence_discipline> "Not found" is a hypothesis about your vocabulary, not about the world. Search the shape or effect of a thing, never only its name — jargon gets captured by other domains, and the thing you want is routinely described without its textbook label. Before reporting absence, try at least two independent phrasings, and verify a control query returns non-zero (an empty result from a malformed query proves nothing). In the report, distinguish searched-and-absent (say what you searched) from did-not-reach (ran out of budget). Never blur the two. </absence_discipline>

<model_resolution> When using external models: resolve IDs against the live catalog (list_models / search_models) at call time — never from memory or a file. Pass the bare id (no @, no / — those are routing addresses, not identities). A version the user names is a hard constraint: if it is not in the catalog, say so and show live alternatives — never downgrade to a closer-sounding name. Report which IDs were dropped as dead and which survived, every run. On a model failure, report it as FAILED and continue with survivors — never silently substitute a different model. </model_resolution> </critical_constraints>

Read the full file on GitHub · 239 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. 3d ago Changed · +1 lines eebd67505992
  2. 1mo ago Changed · +9 lines d90c51346342
  3. 1mo ago Changed · +43 lines · +30 tokens per session 7881280ef89f
  4. 1mo ago First seen · 186 lines · 78 tokens per session scan A e13e9e4aa417

Subscribe to this mod's changes

deep-analyst is an agent published in the GitHub repository MadAppGang/magus (13 stars, last pushed today), licensed MIT. It adds 108 tokens to every session and 3,207 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-04.

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