gru-ai: Skill for Claude Code

.claude/skills/scout/SKILL.md

scout is a skill for Claude Code from andrew-yangy/gru-ai. It costs 50 tokens per session (5,094 once invoked), scanned B, original, MIT.

A recurring process in which company leadership roles research competitors, market trends, working methods, and user opinions outside the company, then propose initiatives for the CEO to review.

In plain words
What is it for?
Use it for outward research across technology, product and users, marketing, and operations, followed by consolidation and CEO review of proposed initiatives.
Why use it?
It helps a company keep track of changes beyond its own codebase and avoid making plans based only on internal assumptions. Approved proposals become instructions for the company to act on.

Skill for Claude Code

Written for Claude Code: installed under .claude/. Also seen: reads .claude/ paths; names the AskUserQuestion tool; mentions Claude Code.

This is andrew-yangy/gru-ai's own configuration. It tells Claude Code how to work on gru-ai itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything gru-ai configures →

Reuse

Borrowing it

Nothing to install: this file belongs to andrew-yangy/gru-ai. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/andrew-yangy/gru-ai/main/.claude/skills/scout/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/andrew-yangy/gru-ai

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.

agentmods badge for scout

README.md
[![agentmods](https://agentmods.dev/badge/skills/andrew-yangy/gru-ai/scout/github.svg)](https://agentmods.dev/skills/andrew-yangy/gru-ai/scout)
Your own site
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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.

agentmods 80×15 button for scout

Your own site · 80×15
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Per session 50 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 5,094 The whole file, excluding the scripts and references it only reads on demand.
Security scan B 1 finding. A grade says what 26 rules found in the file — not that it is safe.
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 5.1 $0.00050 $0.05094
Opus 5 $0.00025 $0.02547
Sonnet 5 $0.00010 $0.01019
Haiku 4.5 $0.00005 $0.00509

Measured 11d ago against content hash 11cdf5f47abb, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade B, and why

scout scanned grade B with 1 finding 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 11d 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.

Asks the agent to reveal its instructionsmediumSystem prompt leakage

Directions to print, repeat or translate the system prompt extract configuration the operator did not intend to expose.

{JSON output instructions below}
.claude/skills/scout/SKILL.md · 453 lines

How it starts

The opening of the file, as written. The whole thing — 453 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Scout — External Intelligence Gathering

Role Resolution

Read .claude/agent-registry.json to map roles to agent names. Use each agent's id as the subagent_type when spawning. The CTO = technology scout, the CPO = product/user scout, the CMO = market/growth scout, the COO = process/ecosystem scout + consolidation.


Run a scout: each C-suite member researches their external domain, brings back intelligence, and proposes initiatives. The COO consolidates, CEO reviews, approved proposals become directives.

Key principle: Agents look OUTWARD. They use WebSearch and WebFetch to research the world — competitors, market trends, frameworks, user sentiment. They do NOT scan the codebase. That's /healthcheck.

Step 1: Read Context

Read ALL of these before spawning agents:

  • .context/vision.md — north star + guardrails (agents need to know what's relevant)
  • .context/preferences.md — CEO standing orders
  • .context/directives/*/directive.json — current directives and priorities (so agents focus research on what matters)
  • .context/backlog.json — so agents don't propose what's already queued, AND so the COO can check trigger conditions during consolidation
  • .context/lessons/orchestration.md
  • Recent scout archive in .context/intel/archive/ — so agents don't re-report known intelligence (just filenames + dates, not full content)
  • .context/reports/ (proposals tracked in reports) — so agents know what's been proposed and approved/rejected before

Step 2: Spawn Scout Agents (Parallel)

Spawn all 4 C-suite agents in parallel. Each researches their external domain.

Each agent receives:

  • Their full personality from .claude/agents/{name}.md
  • .context/vision.md (full file — guardrails help agents assess relevance)
  • .context/preferences.md
  • .context/directives/*/directive.json
  • Current backlogs summary (what's already planned)
  • List of recent intelligence reports (filenames only — so they skip known topics)

All agents: subagent_type: "general-purpose", model: "opus"

Read the full file on GitHub · 453 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. 11d ago First seen · 453 lines · 50 tokens per session scan B 11cdf5f47abb

Subscribe to this mod's changes

scout is a skill published in the GitHub repository andrew-yangy/gru-ai (153 stars, last pushed 6mo ago), licensed MIT. It adds 50 tokens to every session and 5,094 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it B with 1 finding (asks the agent to reveal its instructions). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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