A research system that builds and maintains a searchable wiki from sources such as notes, highlights, web links, repositories, and PDFs. It keeps a catalog, written research pages, and the original source files in separate layers.

Use it to start research on a new topic, add sources to an existing topic, query the collected material, and produce organized summaries and conclusions.

It turns scattered research material into a reusable knowledge base instead of leaving it as disconnected files. It can answer questions from existing research or add and deepen a topic over time.

research — Skill for Claude Code by hugobowne

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent

npx skills add hugobowne/show-us-your-agent-skills --skill research

Clone the repo

git clone --depth 1 https://github.com/hugobowne/show-us-your-agent-skills

Made for: Claude Code.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

README.md

[![agentmods](https://agentmods.dev/badge/skills/hugobowne/show-us-your-agent-skills/research/github.svg)](https://agentmods.dev/skills/hugobowne/show-us-your-agent-skills/research)

Your own site

<a href="https://agentmods.dev/skills/hugobowne/show-us-your-agent-skills/research"><img src="https://agentmods.dev/badge/skills/hugobowne/show-us-your-agent-skills/research/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

Your own site · 80×15

<a href="https://agentmods.dev/skills/hugobowne/show-us-your-agent-skills/research"><img src="https://agentmods.dev/badge/skills/hugobowne/show-us-your-agent-skills/research.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 216 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 13,880 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits

NVIDIA SkillSpector pass 7 Sept 2026

How audits are shown

Origin original No closer match found in the catalogue.

Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

Model	Per session	Once invoked
Fable 5.1	$0.00216	$0.13880
Opus 5	$0.00108	$0.06940
Sonnet 5	$0.00043	$0.02776
Haiku 4.5	$0.00022	$0.01388

Measured 13d ago against content hash ffdedcfedf42, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

research scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 13d ago.

The scan reads SKILL.md. This mod also ships 7 executable files (scripts/build_index_md.py, scripts/build_index_yaml.py, scripts/dedup_findings.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/research/SKILL.md · 717 lines

How it starts

The opening of the file, as written. The whole thing — 717 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Second Brain Research

You are a research orchestrator. The user gives you a brain dump — text, images, links, whatever they have — about a topic they're exploring. Your job is to mine their Second Brain (Obsidian vault, Readwise highlights, NotebookLM collections, web seeds, GitHub repos, and dropped PDFs) and maintain an LLM-curated wiki that compounds over time.

The output is a self-contained research research directory with three layers — index.yaml / index.md (catalog), wiki/ (synthesis), and raw/ (immutable sources). Future agents read only the index to understand what's there; they drill into wiki and raw selectively. The full data contract lives in CONVENTIONS.md.

Step 0 — Detect ingest mode

Before processing the brain dump, classify the user's intent. Three modes:

Mode	Trigger	Pipeline
query	Existing research dir + user asks a question / wants to load context ("what do I have on X", "load my research on Y", filter / drill questions)	Read-only path. See Query path below. Optional Q&A save-back.
init	New topic — no matching `<working_memory_dir>/research-<topic-slug>/` exists	Full pipeline: Steps 1 → 8
append-deep	Existing research dir + user provides a brain dump or vague extension request ("ingest more on X", "find me more sources for my X research")	Step 1, then a short Step 4 (1–2 rounds at most), then Steps 5 → 8. The reranker compares against existing wiki for novelty; sources already in `index.yaml` are deduplicated.
append-trusted	Existing research dir + user points at a specific file/URL/PDF ("add this PDF to my X research")	Skip rounds entirely. Process the seed via Step 1 + Step 6 + Step 6.2 + Step 6.3 (just the source_writer for the new source) + Step 6.4 (incremental wiki update) + Steps 6.5 → 8.

How to decide:

Compute the candidate topic_slug from the user's words (kebab-case).
Check <working_memory_dir>/research-*/ for a directory whose index.yaml has a topic that matches (semantic match — the user might say "agent loops" when the dir is "agent-harnesses"). If there's a strong match, the mode is append-*. If there's no clear match, ask the user via AskUserQuestion: "Add to existing research on <topic> or start a new dir?"
If the user dropped a single file with no other framing AND a research dir already exists for the topic → append-trusted.
If the user wrote a brain dump AND a research dir exists → append-deep.
Otherwise → init.

Read the full file on GitHub · 717 lines

Files

What ships with it

18 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

13d ago First seen · 717 lines · 216 tokens per session scan A ffdedcfedf42

Subscribe to this mod's changes

research is a skill published in the GitHub repository hugobowne/show-us-your-agent-skills (67 stars, last pushed 1mo ago), licensed MIT. It adds 216 tokens to every session and 13,880 once invoked, about $0.0011 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Memory and context Research

media-ingest

Ingest video, audio, PDF, book, screenshot, and GitHub repo content into the brain. Multi-format handling with entity extraction and backlink propagation. Covers video-ingest, youtube-ingest, and book-ingest subtypes.

garrytan/gbrain · 52 tokens

mem0-oss-to-platform

Plan and then execute a migration of a project from the mem0 open-source / self-hosted SDK (the local Memory class) to the mem0 Platform / hosted / managed SDK (the MemoryClient class). Use this whenever a developer wants to move, switch, or migrate their mem0 usage off OSS/self-hosted to the hosted API — e.g.…

mem0ai/mem0 · 273 tokens

Cortex

Operate Cortex, the LifeOS memory system — the typed Knowledge Archive (People, Companies, Ideas, Research with typed related: links) plus recall of prior work sessions, ISAs, and conversations. Search, add, harvest, develop, ingest, distill, graph-navigate, recall. USE WHEN cortex, knowledge, knowledge base, search…

danielmiessler/LifeOS · 196 tokens

memory

Use when the user asks to remember, recall, forget, update, search, or inspect durable OpenSquilla memory, including profile facts in USER.md and long-term notes in MEMORY.md or memory//.md.

opensquilla/opensquilla · 44 tokens

ha-data-stores

Map of Hope Agent's local data stores and safe read-only query workflow. Use when the user asks where Hope Agent stores data, wants to inspect sessions/messages/memory/logs/background jobs/knowledge indexes/settings, asks the model to query local app data, or debugging requires checking persisted state. Trigger…

shiwenwen/hope-agent · 115 tokens

establishing-project-context

Use when the user asks to establish shared project language, or project work exposes a conflicting, renamed, or deprecated domain term that needs active semantic modeling. Routine small tasks stay on the fast path.

GanyuanRan/Aegis · 45 tokens