mcpField
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scout

scout

github.com/venhdevDevelopment1131 installsUpdated today
Repository

About scout

scout is an agent skill for researching technical questions against standards, production engineering reports, and postmortems. It guides bounded research and produces a source-backed report on production patterns and failure modes.

Key features

  1. 01Ranks sources by authority
  2. 02Limits search and page counts
  3. 03Triangulates canonical and production evidence
  4. 04Produces a structured scout report
  5. 05Routes follow-up to clarify or changeset

Use cases

  1. 01Assess an architecture against production precedents
  2. 02Identify operational trade-offs and failure modes

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/venhdev/skills --skill scout

After installing, type/scoutin Claude Code to confirm it loaded.

Details

From the project README

scout — Real-System Best Practices & Production Archetype Engine

Domain Rubric

1. Source Authority Hierarchy

Triangulate technical inquiries against authoritative production evidence:

  • Tier 1 (Canonical Standards & Official Specs): RFCs, language specifications, cloud vendor reference architectures, and formal technical papers.
  • Tier 2 (High-Scale Production Engineering): Tech blogs and architecture disclosures from proven high-scale engineering organizations (e.g., Stripe, Netflix, Cloudflare, Discord, Uber, Meta).
  • Tier 3 (Battle Scars & Postmortems): Documented production outages, postmortems, and root-cause analyses revealing edge cases, operational bottlenecks, and failure modes.
  • Exclusion Filter: Exclude SEO-optimized content farms, unverified personal blog posts, vendor marketing collateral, and unvetted AI summaries.
2. Search & Inclusion Budget
  • Exploration Ceiling: Maximum 8 search queries and 10 fetched pages per scouting run.
  • Inclusion Budget: Synthesize from 3 to 5 premier, high-signal sources.
  • Short-Circuit Stop: Terminate search as soon as the canonical pattern and known failure modes are triangulated with primary evidence.

Canonical Output Contract

# Scout Report: <Topic / Inquiry>

## 1. Executive Summary
- **Canonical Production Archetype**: <Boring, battle-tested standard approach adopted by industry leaders>
- **Operational Reality**: <Key complexity, maintenance overhead, or scaling bottleneck in practice>

## 2. Industry Precedents & Battle Scars
| Source / Organization | Pattern Deployed | Production Scale / Context | Known Failure Mode / Trade-off |
| :--- | :--- | :--- | :--- |
| <Org / Paper> | <Architecture / Tool> | <Traffic, Data Volume, SLA> | <Failure point, latency spike, cost trap> |

## 3. Recommended Baseline vs. Traps
- **Recommended Baseline**: <Simplest robust architecture meeting requirements without premature optimization>
- **Premature Traps to Avoid**: <Over-engineered patterns or hype-driven tools that introduce unnecessary failure modes>

## 4. Primary Citations
- [<Source Title>](<Verified URL>) — <Core insight or concrete data point extracted>
Routing
  • Deliberate trade-offs or evaluate architectural fit for the project → (via /clarify).
  • Baseline is accepted, adequately supported, and the caller requests change planning → (via /changeset).

Execution Protocol

  1. Query Triangulation & Dispatch:

    • Formulate 2 to 3 targeted search queries crossing: canonical standards/RFCs, high-scale engineering cases, and failure postmortems.
    • Launch a research subagent (Role: Real-System Research Scout) supplying:
      • The triangulated queries and Search Budget (max 8 queries, 10 fetched pages).
      • The Canonical Output Contract above as the required output contract.
    • Mandate the subagent to inspect full page bodies via URL tools before synthesizing; cite strictly verified URLs and never synthesize from search snippets alone.

  2. Synthesis & Halt:

    • Emit the completed report into the conversation stream and halt turn immediately.

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