research

research is a skill for Claude Code, Codex from agno-agi/context. It costs 102 tokens per session (1,425 once invoked), scanned A, original, Apache-2.0.

A research workflow that checks existing internal notes, researches current external information, writes a sourced brief, and files it into a knowledge base. It labels information from the web separately from existing notes.

In plain words
What is it for?
Use it to investigate a topic, answer a focused question, create a durable research brief, and store that brief in the knowledge base. The input says it can search connected knowledge, CRM, Slack, and the web.
Why use it?
It reduces duplicated research and leaves the result available for later use instead of only producing a one-time answer. It also keeps sources and internal knowledge distinct.

Skill for Claude CodeCodex

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.

agentmods
npx agentmods add skills/agno-agi/context/research
Any agent
npx skills add agno-agi/context --skill research
Clone the repo
git clone --depth 1 https://github.com/agno-agi/context

Made for: Claude Code, Codex.

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.

agentmods badge for research

README.md
[![agentmods](https://agentmods.dev/badge/skills/agno-agi/context/research.svg)](https://agentmods.dev/skills/agno-agi/context/research)
Your own site
<a href="https://agentmods.dev/skills/agno-agi/context/research"><img src="https://agentmods.dev/badge/skills/agno-agi/context/research.svg" alt="Measured on agentmods" height="20"></a>
Per session 102 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,425 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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.

ModelPer sessionOnce invoked
Fable 5 $0.00102 $0.01425
Opus 5 $0.00051 $0.00713
Sonnet 5 $0.00020 $0.00285
Haiku 4.5 $0.00010 $0.00143

Measured 4d ago against content hash 50edcb79210d, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, 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 4d ago.

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 · 96 lines

How it starts

The opening of the file, as written. The whole thing — 96 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Research

Runtime skill — a playbook the deployed @context agent runs for its owner, invoked in natural language. Not a coding-agent workflow; those live in .agents/skills/.

Turn a topic into a durable, sourced brief filed in the knowledge base. Like process-today, this one writes — it ends in an update_knowledge call, not just a summary. It still never acts on the outside world: it reads and files, and nothing leaves.

Procedure

  1. Pin the topic and the question. Settle what the owner actually wants to know ("research how teams handle multi-tenant RBAC" → the question is how do teams do this, and where does it leave us). If the topic is genuinely ambiguous and a wrong guess wastes the work, ask one clarifying question.
  2. Sweep what we already know first — internal before web. Always do this, even for a topic that looks purely external: it's how you avoid duplicating a page that already exists and how you learn where the brief should land. A quick sweep that comes back empty is fine; skipping it is not.
    • query_knowledge — route through the index to any spec or page already on the topic. If one exists, the brief extends it; it doesn't shadow it.
    • query_crm — contacts, notes, projects, meetings tagged to the topic (our relationship and history with it).
    • query_slack (when connected) — recent threads where the team discussed it; often the freshest internal context.
  3. Go to the web for current and external background. query_web for what we can't know from our own notes: public facts, recent developments, how others approach it. Keep queries to the public subject, not the owner's private notes. Skip the web only when the topic is purely internal and the internal sweep already answers it.
  4. Read the codebase when the topic is framework-related. If the question touches how @context is built or how Agno does something (a toolkit, a provider, the scheduler, AgentOS), read the repo's files (list_files, read_file) to see how it uses it, and query_web for anything the code doesn't answer.
  5. Synthesize a clear, sourced brief. Lead with the answer. Attribute every claim to where it came from, and keep web background visibly separate from our own record: tag lines (from web), (on file), or (auto-summarized, unverified) for anything you distilled but haven't confirmed. Never blend an unverified web claim into our known facts.
  6. File it with update_knowledge. Filing is frictionless (no approval gate), so land it directly:
    • Topic already covered → file into that spec's right sub-file (a decision → decisions.md, design detail → design.md, status → the README.md) and keep the status table current.
    • New topic that's a real spec → a new folder-per-spec following _template/, plus a row in the root README.md index.
    • A standalone write-up that isn't a spec → a loose prose page ("what we know about X"). The write sub-agent carries the provenance labels through, so keep them in the prose you hand it.
  7. Make the brief findable — don't orphan your own research. Filing isn't done until the new page or spec has a row in the root README.md index. A new spec earns its row through the write tool's own rules; for a new loose page, make a second update_knowledge call that adds a one-line index row pointing at the path you just wrote. Skipping this lands the brief as exactly the kind of orphan the knowledge-review sweep exists to flag.
  8. Report what you filed and where. One line per file touched (what + path), then one line on what you leaned on — "web: 4 sources; on file: the pgvector-standard spec" — so the owner can see the brief's footing at a glance. Mirror process-today's digest.

Read the full file on GitHub · 96 lines

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.

  1. 4d ago First seen · 96 lines · 102 tokens per session scan A 50edcb79210d

Subscribe to this mod's changes

research is a skill published in the GitHub repository agno-agi/context (11 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 102 tokens to every session and 1,425 once invoked, about $0.0005 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.

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