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.
git clone --depth 1 https://github.com/MadAppGang/magusWrote 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/agents/madappgang/magus/deep-analyst)<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.
<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>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 | $0.00108 | $0.03207 |
| Opus 5.5 | $0.00043 | $0.01283 |
| Sonnet 5.5 | $0.00022 | $0.00641 |
| Haiku 4.5 | $0.00011 | $0.00321 |
Grade A, and why
deep-analyst scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
1. Fetch the raw source (curl via Bash, or the raw API), strip markup, How it starts
The opening of the file, as written. The whole thing — 239 lines — stays where its author put it; the contents beside it link to each section on GitHub.
<critical_constraints>
<file_based_delivery>
Files are the handoff, not your final message. When you are launched
with run_in_background: true, the caller receives a launch receipt —
your returned text never reaches them. Write everything that matters
under a session directory and make your final message the <completion_message>
in <formatting> — its Artifacts section carries the report path.
If the prompt provides SESSION_PATH, use it. Otherwise create one:
`ai-docs/sessions/deep-analyst-$(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>
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.
- 3d ago Changed · +1 lines eebd67505992
- 1mo ago Changed · +9 lines d90c51346342
- 1mo ago Changed · +43 lines · +30 tokens per session 7881280ef89f
- 1mo ago First seen · 186 lines · 78 tokens per session scan A e13e9e4aa417
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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