by jordan-gibbs
Agent-driven research knowledge base. Agents collect, search, and synthesize web research into a persistent, searchable wiki.
# Add to your Claude Code skills
git clone https://github.com/jordan-gibbs/hyperresearchGuides for using ai agents skills like hyperresearch.
Last scanned: 5/26/2026
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}hyperresearch is an open-source ai agents skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by jordan-gibbs. Agent-driven research knowledge base. Agents collect, search, and synthesize web research into a persistent, searchable wiki. It has 467 GitHub stars.
Yes. hyperresearch passed SkillsLLM's automated security scan — a dependency vulnerability audit plus prompt-injection heuristics — with no high-severity issues. You can read the full report in the Security Report section on this page.
Clone the repository with "git clone https://github.com/jordan-gibbs/hyperresearch" and add it to your Claude Code skills directory (see the Installation section above).
hyperresearch is primarily written in Python. It is open-source under jordan-gibbs on GitHub, so you can review or fork the full source.
Yes. SkillsLLM lists many other AI Agents skills you can browse and compare side by side. Open the AI Agents category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh hyperresearch against similar tools.
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Hyperresearch turns Claude Code into a deep research agent. and currently leads the DeepResearch-Bench RACE leaderboard (benchmarked internally). A tier-adaptive 16-step pipeline produces adversarially-audited reports with full source provenance. Every fetched source lands in a persistent, searchable vault that compounds across sessions.
cd your-project
pip install hyperresearch && hyperresearch install
Then /hyperresearch <anything> in Claude Code.
Python 3.11–3.13. (3.14 not yet supported — use
pyenv install 3.13,uv venv -p 3.13, orpy -3.13 -m venv .venv.)Power users:
hyperresearch install --globalmakes/hyperresearchreachable from every Claude Code session anywhere, at the cost of ~15 lines in every session's system reminder. Per-project install (above) keeps unrelated CC sessions clean.
The entry skill is a thin router. It bootstraps the canonical research query, then invokes one step skill per pipeline phase via Claude Code's Skill tool. Each step's procedure is loaded fresh into context only when needed defeating context-rot problems that makes long pipelines silently drop steps.
| # | Step | What it does | Tiers |
|---|---|---|---|
| 1 | Decompose | Canonical query → atomic items + coverage matrix + tier classification | both |
| 2 | Width sweep | Multi-perspective search plan + parallel fetcher waves (Haiku) | both |
| 3 | Contradiction graph | Pair contradictions across the corpus into ranked clusters | full |
| 4 | Loci analysis | Two parallel loci-analysts → scored loci with source budgets | full |
| 5 | Depth investigation | K parallel depth-investigators → interim notes with committed positions | full |
| 6 | Cross-locus reconcile | Reconcile committed positions → comparisons.md | full |
| 7 | Source tensions | Extract expert disagreements → source-tensions.json | full |
| 8 | Corpus critic | "What source would overturn this?" + targeted gap-fill fetch | full |
| 9 | Evidence digest | Top claims + verbatim quotes → evidence-digest.md | full |
| 10 | Triple draft | Per-angle source curation + 3 parallel draft sub-orchestrators (light: single draft) | both |
| 11 | Synthesize | Plan + outline + spawn synthesizer subagent → final_report.md | full |
| 12 | Critics | 4 adversarial critics in parallel → findings JSONs | full |
| 13 | Gap-fetch | Targeted fetch wave for critic-identified vault gaps | full |
| 14 | Patcher | Surgical Edit hunks applied to draft (tool-locked Read+Edit) | full |
| 15 | Polish | Hygiene + filler pass (tool-locked Read+Edit subagent) | both |
| 16 | Readability audit | Recommender writes JSON suggestions; orchestrator selectively applies | both |
In your prompt, you can request one of two tiers and the rest of the pipeline scales accordingly. Full mode is default.
| Tier | Steps that run | Typical time |
|---|---|---|
light |
bounded factual queries, surveys, comparisons — 1 → 2 → 10 → 15 → 16 | ~30–40 min |
full |
deep argumentative analysis with adversarial review — all 16 steps | ~1.5–2.5 hours |
Patch, never regenerate. After step 11 produces the synthesized report (or step 10 for light tier), the only modifications are surgical Edit hunks. The patcher and polish auditor are tool-locked to [Read, Edit] at the Claude Code allowlist level so they physically cannot Write a new draft. Per-hunk caps make "just rewrite it" mechanically impossible. Critic findings that don't fit a small hunk escalate as structural issues.
Canonical research query is gospel. The verbatim user prompt is persisted to research/query-<vault_tag>.md once and re-read by every subsequent step and every spawned subagent. Wrapper requirements (save paths, citation format, terminal sections) are a separate contract.
| Agent | Model | Role |
|---|---|---|
hyperresearch-fetcher |
Sonnet | URL fetching via crawl4ai; runs 8–12 in parallel per wave |
hyperresearch-source-analyst |
Sonnet (1M ctx) | End-to-end digest of any single long source >5000 words |
hyperresearch-loci-analyst |
Sonnet | Reads the width corpus, returns 1–8 depth loci with rationale |
hyperresearch-depth-investigator |
Sonnet | Investigates one locus, writes one interim note with a committed position |
hyperresearch-corpus-critic |
Sonnet | "What source would overturn the current direction?" pre-draft gap analysis |
hyperresearch-draft-orchestrator |
Opus | One per draft angle; reads its curated source list and writes one draft |
hyperresearch-synthesizer |
Opus | Reads all 3 drafts, writes the final report (two-pass write, Read+Write locked) |
hyperresearch-dialectic-critic |
Opus | Counter-evidence the draft missed |
hyperresearch-depth-critic |
Opus | Shallow spots interim notes could fill |
hyperresearch-width-critic |
Opus | Topical corners the corpus supports but the draft ignores |
hyperresearch-instruction-critic |
Opus | Structural mismatches against the prompt's atomic items |
hyperresearch-patcher |
Opus | Tool-locked [Read, Edit]. Applies critic findings as surgical Edit hunks |
hyperresearch-polish-auditor |
Opus | Tool-locked [Read, Edit]. Cuts filler, strips hygiene leaks |
hyperresearch-readability-recommender |
Opus | Writes JSON suggestions for paragraph rhythm and list/table conversion |
Hyperresearch is not a one-shot report generator like most other Deep research harnesses. Every fetched source lands in a SQLite-indexed vault that every future research session can reuse.
hyperresearch search "ion-trap gate fidelity" -j # Full-text search
hyperresearch search "quantum" --include-body -j # Full-body search
hyperresearch note show <id1> <id2> <id3> -j # Batch-read notes
hyperresearch graph hubs -j # Most-connected notes
hyperresearch graph backlinks <id> -j # Reverse links
hyperresearch lint -j # Health check (broken links, missing tags)
Markdown is truth, SQLite is cache. Notes live as plain markdown with YAML frontmatter in research/notes/. The SQLite index is fully rebuildable. Delete it and hyperresearch sync reconstructs it from the markdown. The vault is inspectable in any editor, version-controllable in git, and readable without the tool installed.
PDFs fetch directly. hyperresearch fetch auto-detects PDF URLs (arXiv, NBER, SSRN, direct .pdf links) and extracts full text via pymupdf. Raw PDFs land in research/raw/<note-id>.pdf and the note's raw_file: frontmatter links back.
Provenance breadcrumbs. Every fetched source carries a --suggested-by link back to whatever surfaced it. The chain forms a rooted tree from seed fetches; the provenance lint rule catches disconnected components.
scaffold-prompt lint blocks if the scaffold doesn't open with the user's exact prompt[Read, Edit]. They cannot regenerate the draftpatch-surgery lint surfaces any critical finding the patcher couldn't applytier, content_type, and type are SQLite CHECK-constrained vocabularies; corrupted frontmatter cannot poison the indexFetch from LinkedIn, Twitter, paywalled sites or anything you can log into:
hyperresearch setup # Browser opens. Log into your sites. Done.
LinkedIn, Twitter, Facebook, Instagram, and TikTok automatically use a visible browser to avoid session kills.
For any topic with a research literature, hit academic APIs BEFORE web search. They return citation-ranked canonical papers; web search returns derivative commentary.
https://api.semanticscholar.org/graph/v1/paper/searchhttps://export.arxiv.org/api/queryhttps://api.openalex.org/workshttps://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgiAfter the academic sweep, run web searches for context, news, non-academic angles, and at least one adversarial search ("criticism of X", "limitations of X").