Apache-2.0, pure-markdown autonomous research system — 900+ skills, 4-layer hierarchy and 7 MCP integrations; documented for Claude Code and Codex, with no DeepSeek Harness path in the README (ecosystem).
DSH integration
Ecosystem-related
Author-claimed
Safety audit
Unaudited
Last verified
2026-08-21
License
Apache-2.0
01What can it help you accomplish?
Turn a research direction into an executable, machine-readable Research Spec
A Research Spec with checkbox progress tracking, quantified completion criteria, backtrack conditions and session recovery, generated after North Star crystallization
Researchers and AI engineers who want an autonomous AI research agent rather than a search assistant
Run systematic literature acquisition and research-gap discovery
Multi-pass academic paper discovery via Semantic Scholar with citation chaining and snowball sampling, plus 15+ gap detection methods
Academic and industry researchers who need systematic literature coverage instead of keyword search
Continue long research campaigns across sessions without losing state
Checkpoint-based context files (context/INDEX.md + ≥500-line checkpoints) that let a new agent session resume from the spec's checkbox state
Teams running multi-session deep-research campaigns that span days or weeks
02How to install into DeepSeek Harness
Prerequisites
- Node.js 22 or newer
- Claude Code or Codex as the executing agent runtime — the two runtimes documented in the README
- API keys for the MCP servers you enable (Brave, Apify; Tavily optional; Semantic Scholar key optional — public API works keyless at lower rate limits)
Installation steps
- 01
Clone the repo and install dependencies: `git clone https://github.com/yogsoth-ai/de-anthropocentric-research-engine.git`, `cd de-anthropocentric-research-engine`, `npm install` (pulls the bundled MCP servers)
$ git clone https://github.com/yogsoth-ai/de-anthropocentric-research-engine.git
- 02
Codex: run `./install/codex.sh` (or `./install/codex.sh --target /path/to/your/project`; Windows: `.\install\codex.ps1`) — it adds a marked DARE section to the target's AGENTS.md and copies the knowledge base into `.dare/skills`; use `--dry-run` to preview
- 03
Claude Code: `cp mcp.example.json .mcp.json` and fill in your API keys
- 04
Claude Code: copy (or symlink) every skill into your project's `.claude/skills/` or the user-level `~/.claude/skills/` (e.g. `mkdir -p .claude/skills && cp -R skills/* .claude/skills/`)
- 05
Claude Code: invoke the entry point `/de-anthropocentric-research-engine`; later execute a generated spec with `/executing-specs`
- 06
Optional: for experiment-execution install the `superpowers` and `ponytail` Claude Code plugins via the plugin marketplace; for ara-from-context run `npx @ara-commons/ara-skills`
$ npx @ara-commons/ara-skills
Verify the integration
Not specified by the author
03DSH integration and capability boundaries
Pure-markdown research-skill ecosystem; the README documents Claude Code (skills copied into `.claude/skills/`) and Codex (`install/codex.sh`) as the only runtimes and makes no DeepSeek Harness/dsh claim
Autonomous research orchestration (arsenal, not pipeline)
A research direction — cold-start from zero, warm-start from vague interest, or hot-start from a specific question→A crystallized North Star, then an executable Research Spec the agent executes stage-by-stage across 10 freely-composable packages, with explicit backtrack conditions
Writes Research Spec files (e.g. docs/de-anthropocentric/specs/) and context files during executionSystematic literature acquisition & gap discovery
A research topic or crystallized scope→Multi-pass paper discovery via Semantic Scholar (citation chaining, snowball sampling, cross-database verification) plus 15+ gap detection methods and 31+ ideation techniques
Network calls to Semantic Scholar and web-search MCP servers7 MCP integrations for external data access
Configured MCP servers: semantic-scholar, wiki-vault, brave-search, tavily-search, keenable, apify, alphaxiv→Paper lookup and citations, LLM-optimized web search, full-page scraping, and a persistent research knowledge graph (BM25 search, typed edges)
Requires API keys for some servers (BRAVE_API_KEY, APIFY_TOKEN; optional TAVILY_API_KEY, SS_API_KEY, VAULT_ROOT)keenable and alphaxiv connect to hosted remote endpoints (keyless public servers)Two Apify X-research Actors are paid and not loaded by the example configCheckpoint-based context management
A running research campaign spanning multiple sessions→Named context files with ≥500-line checkpoints, a context/INDEX.md registry, and automatic session recovery from the spec's checkbox state
Writes context files under context/ (gitignored at runtime)
04Who is it for? When not to use it?
Good for
- Researchers and AI engineers who want an autonomous AI research agent rather than a search assistant
- Academic and industry researchers who need systematic literature coverage instead of keyword search
- Teams running multi-session deep-research campaigns that span days or weeks
Not for
- DARE has no application code, runtime or framework — execution depends entirely on the host coding agent's ability to follow complex multi-step instructions; the README names Claude Code as the runtime, with Codex supported via the install script.
- The README documents no DeepSeek Harness (dsh) integration path — the Quick Start covers only Claude Code and Codex, so DSH users should treat DARE as an ecosystem research toolkit until a dsh path is published.
05Compatibility, maintenance and safety notes
- DARE has no application code, runtime or framework — execution depends entirely on the host coding agent's ability to follow complex multi-step instructions; the README names Claude Code as the runtime, with Codex supported via the install script.
- The README documents no DeepSeek Harness (dsh) integration path — the Quick Start covers only Claude Code and Codex, so DSH users should treat DARE as an ecosystem research toolkit until a dsh path is published.
- Research campaigns reach external services via the 7 MCP servers — Brave and Apify require keys (Tavily and Semantic Scholar keys optional), while Keenable and AlphaXiv connect to hosted public endpoints; wiki-vault writes to a local vault directory you configure.
Apache-2.0 · actively maintained (latest release v3.2.2, 2026-06-21; last push 2026-08-15)
06Frequently asked questions
Can I run DARE inside DeepSeek Harness (dsh)?
Not as documented. The README's Quick Start covers only two runtimes — Claude Code (copy/symlink skills into `.claude/skills/`) and Codex (`install/codex.sh` adding a DARE block to AGENTS.md). There is no dsh install path in the README, so treat DARE as an ecosystem research toolkit for now.
Is DARE an MCP server or native tooling?
Neither — DARE is a pure-skill architecture: 900+ markdown instruction files read and executed by the coding agent itself, with no application code or runtime. It *uses* 7 MCP servers (Semantic Scholar, Brave Search, Tavily, Keenable, AlphaXiv, Apify, Wiki Vault) for external data access.
What are the prerequisites?
Node.js 22 or newer, git, and a Claude Code or Codex runtime. `npm install` pulls the bundled MCP server packages. Some MCP servers need API keys (Brave, Apify; Tavily optional; Semantic Scholar works keyless at lower rate limits); Keenable and AlphaXiv are hosted, keyless public endpoints.
Where does my research data go?
MCP calls go to external services — Semantic Scholar, Brave/Tavily web search, Apify scraping, and the hosted Keenable and AlphaXiv endpoints. Wiki Vault stores a knowledge graph locally in an Obsidian-compatible vault directory you set via VAULT_ROOT. Two Apify X-scraper Actors are paid and not loaded by the example config.
What are the main limits?
Execution depends entirely on the host agent's ability to follow complex multi-step instructions. The experiment-execution package additionally needs the `superpowers` and `ponytail` Claude Code plugins, and ara-from-context requires the external `npx @ara-commons/ara-skills` compiler.
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08Data and sources
The entire system is 900+ markdown files — each one a self-contained instruction set that Claude Code reads and executes…
Skills are auto-discovered from a `.claude/skills/` directory — there is no `settings.json` path option.
This page is generated from the project’s public documentation, repository metadata and a structured parse of DSH Plugins; last verified on 2026-08-21. Found an error? Submit a correction.
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