What is code-deep-research?

A research workflow for assessing whether an open-source GitHub project should be adopted in AgenticX.

Use it to produce an evidence-based adopt, partially adopt, or do not adopt recommendation for a GitHub repository.

It requires checking the upstream code at a fixed commit and comparing it with verified local code before recommending a change.

How DemonDamon/AgenticX configures Cursor

Reuse

Borrowing it

Nothing to install: this file belongs to DemonDamon/AgenticX. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file

curl -O https://raw.githubusercontent.com/DemonDamon/AgenticX/main/.cursor/skills/code-deep-research/SKILL.md

Clone the repo

git clone --depth 1 https://github.com/DemonDamon/AgenticX

Made for: Cursor.

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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/demondamon/agenticx/code-deep-research/github.svg)](https://agentmods.dev/skills/demondamon/agenticx/code-deep-research)

Your own site

<a href="https://agentmods.dev/skills/demondamon/agenticx/code-deep-research"><img src="https://agentmods.dev/badge/skills/demondamon/agenticx/code-deep-research/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.

Your own site · 80×15

<a href="https://agentmods.dev/skills/demondamon/agenticx/code-deep-research"><img src="https://agentmods.dev/badge/skills/demondamon/agenticx/code-deep-research.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 37 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 685 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits

NVIDIA SkillSpector pass 7 Sept 2026

How audits are shown

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.

Model	Per session	Once invoked
Fable 5.1	$0.00037	$0.00685
Opus 5.5	$0.00015	$0.00274
Sonnet 5.5	$0.00007	$0.00137
Haiku 4.5	$0.00004	$0.00068

Measured 2mo ago against content hash 04f0b7c3e9f4, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

code-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 2mo 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.

.cursor/skills/code-deep-research/SKILL.md · 68 lines

How it starts

The opening of the file, as written. The whole thing — 68 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Code Deep Research

Overview

Research an upstream repository from a locked commit, compare it with verified AgenticX code, and make an evidence-backed adoption decision. The local upstream clone is the source of truth; MCP tools accelerate discovery but never replace source verification.

When to Use

Use for:

/codedeepresearch requests.
“调研这个 GitHub 项目能否集成进 AgenticX”。
Comparing an upstream framework, SDK, agent runtime, tool, memory, planner, UI, or protocol with AgenticX.
Producing ADOPT, SELECTIVE_ADOPT, or DO_NOT_ADOPT recommendations.

Do not use for:

Implementing an already-approved plan.
General web research without a required GitHub repository.
A quick API lookup or a review of one known source file.

Required References

Before taking research actions:

Read WORKFLOW.md completely.
Copy its S0–S8 status ledger into research/codedeepresearch/<repo_name>/meta.md.
Read TEMPLATES.md before creating research artifacts.

Do not infer the workflow from this summary alone.

Non-Negotiable Rules

Clone the repository into research/codedeepresearch/<repo_name>/upstream/ and lock its SHA.
A failed clone blocks normal research. Do not emit Gap, Proposal, P0/P1, or an adoption verdict.
Verify implementation claims against local source using SHA + path + line range + symbol.
DeepWiki, GitHub MCP, and ZRead are optional accelerators. Discover their schemas before use; failure must follow the documented fallback.
ZRead quota failure never makes a successful local-source study “incomplete”.
Do not modify AgenticX production code during research.
Do not invent benchmarks, user needs, issue numbers, runtime validation, or repository behavior.
Do not create an implementation plan or code task unless the user separately requests implementation.

Decision Rule

Determine Gap priority first, then derive the verdict:

ADOPT: at least one valid P0.
SELECTIVE_ADOPT: no P0, but at least one evidence-backed P1 with a real user problem.
DO_NOT_ADOPT: only P2/NO-GAP, unvalidated demand, or insufficient value.

Read the full file on GitHub · 68 lines

Files

What ships with it

2 files 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.

2mo ago First seen · 68 lines · 37 tokens per session scan A 04f0b7c3e9f4

Subscribe to this mod's changes

code-deep-research is a skill published in the GitHub repository DemonDamon/AgenticX (988 stars, last pushed yesterday), licensed Apache-2.0. It adds 37 tokens to every session and 685 once invoked, about $0.0001 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 2026-08-30.

Building agents Research agent-framework agentic-workflows ai-agent ai-orchestration chatbot multi-agent-system

code-deep-research next to 6 skills from other repositories
Mod	Type	Stars	Last push	Licence	Scan	Tokens	Works with
code-deep-researchDemonDamon/AgenticX	Skill	988	yesterday	Apache-2.0	A	37	Cursor
skill-creatorHKUDS/nanobot	Skill	50k	today	MIT	A	13	Claude Code, Codex
tmuxHKUDS/nanobot	Skill	50k	today	MIT	A	20	Claude Code, Codex
weatherHKUDS/nanobot	Skill	50k	today	MIT	A	8	Claude Code, Codex
githubHKUDS/nanobot	Skill	50k	today	MIT	A	19	Claude Code, Codex
nanobot-documentHKUDS/nanobot	Skill	50k	today	MIT	A	19	Claude Code, Codex
memoryHKUDS/nanobot	Skill	50k	today	MIT	A	12	Claude Code, Codex

AgenticX: Skill for Cursor