Technical documentation page from **GPT Researcher**, *"Context Filter"*, rewritten on **September 26, 2026** by **Assaf Elovic**, the project's creator, the day he made **Jev** (a model from **TypeSafe**) the default context filter. About **2,200 words**, including a reproducible benchmark. **(A)** The design thesis: the context filter is *"the biggest lever on report quality after the research itself"*: *"what it discards, the writer never sees"*. **(B)** The mechanism: each scraped page is split into **1,000-character** chunks. Jev scores each chunk on a four-level usefulness scale (0 to 3), and only chunks at **≥ 1.5** are kept, ten at most. Without an API key, a local **BM25** fallback with a relative threshold takes over. **(C)** The benchmark: 28 tasks replayed on the same pages. **73%** relevant passages for Jev versus **46%** for embeddings, **15-10-3** in head-to-head, at the same cost (~$0.115 per report). **(D)** The most transferable finding: *"the threshold matters more than the ranking"*. Without a threshold, Jev drops back to 50%. The gain comes from less noise, not more content. Extends, on the web-search side, the comparison of retrieval strategies from [[comparethemarket-context-retrieval-ai-code-review-gkg-rag-2026-03-06]].
#context filter#context filter#passage selection
Assaf Elovic · créateur de GPT Researcher · auteur de la page (documentation officielle docs.gptr.dev, non signée).
**Skill** entry: **hyperresearch** by **Jordan Gibbs** is a **deep research harness** that turns Claude Code into a documentary research agent, shipped as a PyPI package (MIT, Python 3.11-3.13) installing **20 Claude Code skills**, a CLI, an MCP server, and a local web UI. Observed on **August 3, 2026**: 1,568 stars, 170 forks, repo created on April 9, 2026, last push on August 1. **The core is a 16-step pipeline adaptive by tiers** — `light` (~30-40 min), `full` (~1.5-2.5 h), `dissertation` (4-8 h, 25,000-80,000 words across 300-450 sources) — which takes a prompt and returns an adversarially audited report with full provenance. **The central architecture decision is documented alongside its failure mode**: the entry skill is a **thin router** with no procedure, each step living in its own skill loaded **fresh at the moment it is invoked**, because the previous version was *« one 1200-line skill that got compacted away by the time Layer 4 needed its triple-draft procedure. The orchestrator forgot the procedure, wrote a single draft, and produced a flat-scoring report. »* **Two load-bearing principles.** *« Patch, never regenerate »*: after synthesis, only surgical `Edit` touch-ups are possible, with the patcher and the polish auditor tool-locked to `[Read, Edit]` at the Claude Code allowlist level, so that they *« physically cannot Write a new draft »*. *« Canonical research query is gospel »*: the verbatim prompt is persisted once in `query.md` and re-read by every step and every subagent. **Sixteen subagents** with configurable role and model (fetchers and cite-checker on Sonnet, critics, synthesizer, and patcher on Opus). **The vault** is a persistent markdown store indexed in SQLite — *« Markdown is truth, SQLite is cache »* — with a note lifecycle (`draft → review → evergreen`, `stale → deprecated → archive`), traceable provenance, a composite quality score (source type, citation authority via OpenAlex and Semantic Scholar with retraction flags, internal PageRank), and an **independence audit** that groups syndicated copies together — *« five reprints of one press release argue with the weight of one source »*. **Three mechanical gates before shipping**: citation integrity (every quoted citation must exist **verbatim** in a vault note), a retraction sweep refreshed on every cited DOI, and a citation-to-sentence link check by a skeptical LLM. **Reservation to flag**: the opening claim — *« currently leads the DeepResearch-Bench RACE leaderboard »* — is contradicted by its own footnote, *« forward-looking projection from a stratified pilot… Third party validation is pending »*. A projection is not a ranking, yet the chart places it ahead of Gemini and OpenAI Deep Research.
#skill#deep research#research harness
**Jordan Gibbs** — auteur et mainteneur du dépôt `jordan-gibbs/hyperresearch`. Le projet est distribué sous **licence MIT** et publié sur **PyPI** (`pip install hyperresearch`). Signaux d'adoption au 3 août 2026 : **1 568 étoiles** · **170 forks** · 13 issues ouvertes · dépôt créé le **9 avril 2026** et poussé le **1er août 2026** — soit une traction rapide sur moins de quatre mois. Topics déclarés : `agents` · `agentskills` · `claude-code` · `deep-research` · `deep-research-agent`.