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 tonyghiani/ai-essentials --skill deep-researchgit clone --depth 1 https://github.com/tonyghiani/ai-essentialsWrote 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/tonyghiani/ai-essentials/deep-research)<a href="https://agentmods.dev/skills/tonyghiani/ai-essentials/deep-research"><img src="https://agentmods.dev/badge/skills/tonyghiani/ai-essentials/deep-research.svg" alt="Measured on agentmods" 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 | $0.00118 | $0.00954 |
| Opus 5 | $0.00059 | $0.00477 |
| Sonnet 5 | $0.00024 | $0.00191 |
| Haiku 4.5 | $0.00012 | $0.00095 |
Grade A, and why
deep-research 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 7d 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 — 100 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Deep Research
Become an expert on a topic before producing any output. Research first, report second, then ask conceptual questions before acting.
Inputs
The user provides one or both of:
- A topic (what to become expert on)
- One or more
@foldermentions: primary docs/specs, and optionally secondary code folders for validation
If the user gives only a topic, ask which folders are the source of truth before reading anything.
Workflow
Step 1 — Triage, then load primary sources
Start with a lightweight pass before full reads:
- List all files in the primary folder(s).
- Read first:
README,ARCHITECTURE.md,AGENTS.md, index files, and any doc the user pointed at explicitly. - Identify entry points, public exports, and the docs that define the system's vocabulary.
- Skip or deprioritize: generated code, vendored deps, build output, and snapshot fixtures unless the user asked about them.
Then read remaining primary files end-to-end. Don't grep or sample for files you've committed to reading.
If the folder is large (> 30 files or > 100k tokens estimated), tell the user and ask whether to:
- Read all of it (slower, more accurate)
- Read a triaged subset (you propose the list after Step 1 triage, user confirms)
Step 2 — Validate against secondary sources
If the user provided code folders for validation, cross-check claims in the docs against the actual implementation. Surface any discrepancies you find — outdated docs are common and worth flagging.
Step 3 — Produce a structured report
Use this exact template:
# [Topic] — architecture report
## What it is
[1-2 sentences. The elevator pitch in your own words.]
## The pieces
- **[Component A]** — what it does, where it lives (`path/to/it`)
- **[Component B]** — ...
## How they fit together
[Walk the happy path. Use a flow:
Input → Component A (does X) → Component B (does Y) → Output]
## Key concepts and terminology
- **[Term]**: [precise definition as used in this codebase, not the generic one]
- ...
## What I'm unsure about
[Numbered list of conceptual questions where the docs were ambiguous or
contradicted the code. Each question is specific and answerable.]
## What looks outdated
[Specific doc paragraphs or code comments that don't match current behavior.
Include file:line references.]
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.
- 7d ago First seen · 100 lines · 118 tokens per session scan A ab84fbbd9c3b
deep-research is a skill published in the GitHub repository tonyghiani/ai-essentials (6 stars, last pushed 1mo ago), licensed MIT. It adds 118 tokens to every session and 954 once invoked, about $0.0006 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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