**Skill** entry (not an article): **graphify** by **Safi Shamsi** (Graphify Labs, **Y Combinator S26**) turns an entire project — code, docs, PDFs, images, videos — into a **queryable knowledge graph**, invoked via `/graphify` from Claude Code, Cursor, Codex, Gemini CLI, GitHub Copilot, and about fifteen other clients. Observed on **August 6, 2026**: **103,187 stars**, **10,024 forks**, repository created on **April 3, 2026** — an extraordinary trajectory in four months. **Apache-2.0**, Python 3.10+, default branch **v8**. **The three design commitments fit in three lines of the README**: *« Code maps for free, fully local »* (code is parsed into **tree-sitter AST**, deterministic, no LLM, **nothing leaves the machine**); *« Every edge is explained »* (each edge is tagged **`EXTRACTED`** — explicit in the source — or **`INFERRED`** — resolved by graphify —, with a third value `AMBIGUOUS` appearing in the report); and *« Not a vector index »* — *« no embeddings, no vector store: a real graph you traverse »*. **Three outputs**: `graph.html` (interactive graph), `GRAPH_REPORT.md` (god nodes, surprising connections, suggested questions), and `graph.json` (persistent graph, queryable weeks later without re-reading the files). **Three query modes** replace grep: `query` (subgraph for a natural-language question), `path A B` (shortest path between two entities), and `explain` (neighborhood of a concept). **Coverage**: 36 tree-sitter grammars (~40 languages), plus Terraform, Apex, **MCP configurations**, package manifests, Office, Google Workspace, PDFs, images, and video/audio transcribed **locally** by faster-whisper. Communities detected via **Leiden**, labeled **without an LLM**. ⭐ **The most interesting benchmark result is not a win but a free one**: on LOCOMO, graphify achieves a **recall@10 of 0.497** versus 0.149 for supermemory and 0.048 for mem0, but **loses on QA accuracy** (45.3% versus 49.7%); on LongMemEval-S it scores **76%, tied with a dense RAG**; and the line that matters is *« Graph build — LLM credits: **0** »* where the field typically bills per token. ⚠️ **Points to record**: the `main` branch carries a v1-era README describing a different product (Claude Code skill only, the « 71.5× fewer tokens » claim); the PyPI package is named **`graphifyy`** with two *y*'s, until the `graphify` name is reclaimed; and a **query log** is written by default to `~/.cache/graphify-queries.log`, which can be disabled via an environment variable.
#skill#knowledge graph#knowledge graph
**Safi Shamsi** — créateur et mainteneur de graphify · et de **Graphify Labs** · société passée par **Y Combinator (promotion S26)** selon le badge du dépôt. Il maintient aussi le site d'annuaire `graphify.net` (cf. [[graphify-net-annuaire-ia-coding-2026-08-06]]) et publie un livre · *The Memory Layer* · sur les idées et l'architecture derrière le projet.
**Skill** entry (not an article): **hyperresearch** by **Jordan Gibbs** is a **deep research harness** that turns Claude Code into a document-research agent, shipped as a PyPI package (MIT, Python 3.11-3.13) installing **20 Claude Code skills** + a CLI + an MCP server + a local web UI. Observed on **August 3, 2026**: 1,568 stars, 170 forks, repo created April 9, 2026, last push August 1. **The core is a 16-step pipeline adaptive by tier** (`light` ~30-40 min → `full` ~1.5-2.5 h → `dissertation` 4-8 h and 25,000-80,000 words across 300-450 sources), which takes a prompt and returns a report **audited adversarially** with full provenance. **The central — and most instructive — architecture decision is documented alongside its failure mode**: the entry skill is a **thin router** containing no procedure at all, 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, the patcher and the polish auditor being **tool-locked to `[Read, Edit]` at the Claude Code allowlist level**, so that they *« physically cannot Write a new draft »*; and *« 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/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/Semantic Scholar **with retraction indicators**, internal PageRank) and an **independence audit** that groups syndicated copies — *« five reprints of one press release argue with the weight of one source »*. **Three mechanical gates before shipping**: citation integrity (any quoted passage must exist **verbatim** in a vault note), a retraction scan refreshed on every cited DOI, and citation-to-sentence linkage verification by a skeptical LLM. ⚠️ **Major reservation to flag**: the headline 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 all the same.
#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`.
**Skill** entry (not an article): `grill-with-docs` by Matt Pocock is a structured interview technique that "grills" an architecture plan by methodically confronting it against the project's business vocabulary (the `CONTEXT.md` glossary) and already-documented decisions (ADRs). Rather than rushing into implementation, it challenges assumptions one by one through a question/answer dialogue, cleans up terminology, checks consistency against the actual code, and captures decisions on the fly in the right artifacts. An upfront-design skill, inspired by Domain-Driven Design.