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Agent skill

Code Deep Research

by DemonDamon in DemonDamon/AgenticX

A skill your agent uses when deeply researching an open-source GitHub repository for AgenticX adoption, selective mechanism internalization, gap analysis, or an evidence-backed implementation…

Apache-2.0Auto-check passedResearch & Science

Install Code Deep Research

skills CLI
$ npx skills add DemonDamon/AgenticX --skill code-deep-research -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install DemonDamon/AgenticX code-deep-research --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/DemonDamon/AgenticX.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.cursor/skills/code-deep-research .claude/skills/code-deep-research && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
code-deep-research
GitHub stars
294
Token cost
~767 tokens
SKILL.md length
381 words
Files
3
Skills in repo
17
Repo updated
First seen
Licence
Apache-2.0

At a glance

A skill your agent uses when deeply researching an open-source GitHub repository for AgenticX adoption, selective mechanism internalization, gap analysis, or an evidence-backed implementation…

  • Works in 3 steps: Read WORKFLOW.md completely. → Copy its S0–S8 status ledger into… → Read TEMPLATES.md before creating…
  • Deeply researching an open-source GitHub repository for AgenticX adoption
  • SKILL.md covers Overview, When to Use, Required References and Non-Negotiable Rules, plus 3 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Code Deep Research is an agent skill from DemonDamon/AgenticX. Use when deeply researching an open-source GitHub repository for AgenticX adoption, selective mechanism internalization, gap analysis, or an evidence-backed implementation proposal.

Its SKILL.md is about 770 tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files (for example `TEMPLATES.md` and `WORKFLOW.md`).

It sits in Research & Science, covering Deep research. It works with GitHub. The repository describes itself as: AgenticX is a unified, production-ready multi-agent platform — Python SDK + CLI (agx) + Studio server + Machi desktop app. Features Meta-Agent orchestration, 15+ LLM providers… The licence is Apache-2.0.

When your agent uses it

  • Deeply researching an open-source GitHub repository for AgenticX adoption
  • Selective mechanism internalization
  • An evidence-backed implementation proposal

Example prompts

  • “/code-deep-research”

Workflow steps

3 steps, taken from the first numbered list in SKILL.md.

  1. Read WORKFLOW.md completely.
  2. Copy its S0–S8 status ledger into research/codedeepresearch//meta.md.
  3. Read TEMPLATES.md before creating research artifacts.

What it can do on your machine

Read from SKILL.md and the folder at commit c1af2c7. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Code Deep Research loads about 767 tokens when it runs. Until then it costs about 50 tokens; SKILL.md has 381 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~50
When it runs · the whole SKILL.md, loaded when a task matches
~767

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from DemonDamon/AgenticX at commit c1af2c7, republished under its Apache-2.0 licence (© DemonDamon). 381 words, ~767 tokens.

Download SKILL.mdSave it as .claude/skills/code-deep-research/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.
name
code-deep-research
description
Use when deeply researching an open-source GitHub repository for AgenticX adoption, selective mechanism internalization, gap analysis, or an evidence-backed implementation proposal.

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:

  1. Read WORKFLOW.md completely.
  2. Copy its S0–S8 status ledger into research/codedeepresearch/<repo_name>/meta.md.
  3. 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.
Show full SKILL.md (141 more words)Show less

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.

Do not promote a Gap to force a desired verdict.

Completion Rule

Only claim completion when S0–S7 are completed or legitimately skipped, all quality gates in WORKFLOW pass, and S8 is then marked complete. Final chat output must lead with the verdict and link the written artifacts.

Common Failures

  • Starting with DeepWiki instead of locking the upstream SHA.
  • Treating README or MCP-generated code as implementation evidence.
  • Reading only upstream code and assuming AgenticX lacks the capability.
  • Turning every upstream feature into a P0.
  • Forcing PoC/MVP sections when the correct verdict is DO_NOT_ADOPT.
  • Leaving stale evidence from an older upstream SHA in current artifacts.

© DemonDamon, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 2 other files in .cursor/skills/code-deep-research of DemonDamon/AgenticX.

  • SKILL.md
  • TEMPLATES.md
  • WORKFLOW.md

Open the folder on GitHubat commit c1af2c7

Compare with similar skills

Code Deep Research next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Code Deep Research compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Code Deep Research this skillDemonDamon/AgenticX294—~767Automated safety check: PassApache-2.0
GitHub Deep Researchbytedance/deer-flow83k5 repos~1.3kAutomated safety check: PassMIT
Rival Search MCPdamionrashford/RivalSearchMCP1321 repos~796Automated safety check: PassMIT
Inno Code SurveyLigphiDonk/Oh-my--paper738—~3.6kAutomated safety check: PassMIT
Researcherunderstudy-ai/understudy461—~1.2kAutomated safety check: PassMIT
Deep ResearchCitrus-bit/Anaxa120—~1.9kAutomated safety check: PassMIT

Similar skills

  • GitHub Deep Research

    bytedance/deer-flow

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  • Deep Research

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    120 GitHub stars~1.9k tokensUpdated 1 mo ago
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Works with

Categories

Questions about Code Deep Research

What does Code Deep Research do?

A skill your agent uses when deeply researching an open-source GitHub repository for AgenticX adoption, selective mechanism internalization, gap analysis, or an evidence-backed implementation…. Code Deep Research is an agent skill from DemonDamon/AgenticX. Use when deeply researching an open-source GitHub repository for AgenticX adoption, selective mechanism internalization, gap analysis, or an evidence-backed implementation proposal.

When should I use Code Deep Research?

Code Deep Research fits situations like: deeply researching an open-source GitHub repository for AgenticX adoption; selective mechanism internalization; an evidence-backed implementation proposal.

How do I install Code Deep Research in Claude Code?

Run `npx skills add DemonDamon/AgenticX --skill code-deep-research -a claude-code`. Or copy the skill folder (.cursor/skills/code-deep-research in DemonDamon/AgenticX) into .claude/skills/code-deep-research in your project. Claude Code loads it when a task matches its description.

How do I install Code Deep Research in Codex?

Run `npx skills add DemonDamon/AgenticX --skill code-deep-research -a codex`. Or copy the skill folder (.cursor/skills/code-deep-research in DemonDamon/AgenticX) into .agents/skills/code-deep-research in your project. Codex loads it when a task matches its description.

Can I use Code Deep Research in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add DemonDamon/AgenticX --skill code-deep-research -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/code-deep-research, .gemini/skills/code-deep-research, .github/skills/code-deep-research and .opencode/skills/code-deep-research in your project.

What does Code Deep Research need to run?

SKILL.md names no scripts, command-line tools or credentials: Code Deep Research is instructions for the agent only.

Does Code Deep Research access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Code Deep Research safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Code Deep Research use?

Code Deep Research is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Code Deep Research use?

About 767 tokens (SKILL.md is roughly 3.1k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Code Deep Research?

Skills that share tags, products or a category with Code Deep Research: GitHub Deep Research (bytedance/deer-flow, 83k stars), Rival Search MCP (damionrashford/RivalSearchMCP, 132 stars), Inno Code Survey (LigphiDonk/Oh-my--paper, 738 stars) and Researcher (understudy-ai/understudy, 461 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Code Deep Research?

DemonDamon (a GitHub user) maintains it in DemonDamon/AgenticX, which has 294 GitHub stars. The repository holds 17 skills in this directory. The repository was last updated on October 8, 2026.

Source: DemonDamon/AgenticX on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.