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 5.5	$0.00086	$0.05552
Sonnet 5.5	$0.00043	$0.02776
Haiku 4.5	$0.00022	$0.01388

Measured 1mo ago against content hash ffdedcfedf42, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, 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 1mo 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.

1mo 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 (70 stars, last pushed 2mo ago), licensed MIT. It adds 216 tokens to every session and 13,880 once invoked, about $0.0009 per session on Opus 5.5. 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

knowledge-ingest

Ingest documents, articles, conversations, or supplied text into Knowledge with retained originals, sourced claims, entity updates, timelines, and cross-links; also learn durable lessons from a set of conversations, transcripts, or past sessions (/learn), including the Learn from conversations and Learn from Memory…

openclaw/openclaw · 79 tokens

forget

Remove a personal fact, preference, relationship detail, topic, or prior event from Muse's active memory and stop existing copies or automations from bringing it back. Use for explicit requests such as 'forget that', 'don't remember this about me', or 'remove that from your memory'. Do not use when 'forget it' merely…

asgeirtj/system_prompts_leaks · 74 tokens

mem0-oss-to-platform

Plan, then execute, a migration of a project from the mem0 open-source / self-hosted SDK (local Memory class) to the mem0 Platform / hosted SDK (MemoryClient). TRIGGER when: the developer wants to move, switch, or migrate mem0 off OSS/self-hosted to the hosted API, e.g. "migrate my mem0 setup to the platform", "switch…

mem0ai/mem0 · 217 tokens

Cortex

LifeOS memory, prior-session recall, and learning capture. USE WHEN capture this learning, document this learning, what did we learn recently, recent learnings, cortex, knowledge, knowledge base, search knowledge, what do we know about, archive, harvest, knowledge status, develop note, add to knowledge, add a people…

danielmiessler/LifeOS · 184 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