research

A multi-stage research workflow that investigates a topic and records claims with supporting evidence. Evidence can include web sources, documentation, tests, or production results.

In plain words
What is it for?
Use it to study a topic through broad, technical, risk, and synthesis passes, adding sourced claims to the active sprint.
Why use it?
It turns open-ended research into a traceable set of findings and highlights risks, trade-offs, and recommendations.

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/grainulation/grainulator/research
Any agent
npx skills add grainulation/grainulator --skill research
Clone the repo
git clone --depth 1 https://github.com/grainulation/grainulator

Made for: Claude Code, Codex.

Per session 14 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 614 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 $0.00014 $0.00614
Opus 5 $0.00007 $0.00307
Sonnet 5 $0.00003 $0.00123
Haiku 4.5 $0.00001 $0.00061

Measured 2d ago against content hash ff60479f6fb3, 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 2d 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 · 65 lines

What it actually says

/research -- Investigate a topic

The user wants to research a topic within the current sprint.

Arguments

$ARGUMENTS

Instructions

  1. Identify the active sprint by checking for claims.json in the project root, or ask which sprint to target if none exists.

  2. Plan research passes (2-4 passes depending on topic breadth):

    • Pass 1: Broad landscape -- what exists, key concepts, major players
    • Pass 2: Deep dive -- technical details, implementation specifics
    • Pass 3: Risk and trade-offs -- failure modes, limitations, alternatives
    • Pass 4 (if needed): Synthesis -- cross-cutting insights, recommendations
  3. For each pass:

    • Use WebSearch and WebFetch for general research
    • When the user references a GitHub repo or the current repo is on GitHub, use DeepWiki tools to gather codebase context
    • For private repos, use Read/Grep/Glob to analyze code directly
    • Add 3-5 claims per pass via wheat_add-claim:
      • Use r### ID prefix
      • Set appropriate evidence tier (web, documented, tested)
      • Include source URLs in source.artifact when available
      • Mix claim types: factual, estimate, risk, recommendation
    • Run wheat_compile after each pass
    • Announce progress: "Pass 2/3 complete: 11 claims across 4 topics."
  4. Completion check: After all passes, run wheat_status.

    • If total_claims >= 8 and the user's original message included words like "write up", "summarize", "brief", or "report", immediately run the /brief workflow.
    • Otherwise, suggest next steps.
  5. Print summary:

    Research complete: <pass_count> passes, <claim_count> claims across <topic_count> topics.
    
    Next steps:
      /brief              -- generate a compiled brief
      /challenge r003     -- stress-test a specific finding
      /witness r005 <url> -- corroborate with external source
    
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. 2d ago First seen · 65 lines · 14 tokens per session scan A ff60479f6fb3

Subscribe to this mod's changes

research is a skill published in the GitHub repository grainulation/grainulator (86 stars, last pushed 4mo ago), licensed MIT. It adds 14 tokens to every session and 614 once invoked, about $0.0001 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.