Agent skill

Scout

by huytieu in huytieu/COG-second-brain

Evaluate URLs and tools — check vault coverage, assess relevance, recommend save or skip

MITAuto-check passedKnowledge Management

Install Scout

skills CLI
$ npx skills add huytieu/COG-second-brain --skill scout -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install huytieu/COG-second-brain scout --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/huytieu/COG-second-brain.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/scout .claude/skills/scout && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
scout
GitHub stars
1.3k
Token cost
~1.8k tokens
SKILL.md length
797 words
Files
1
Skills in repo
37
Repo updated
First seen
Licence
MIT

At a glance

Evaluate URLs and tools — check vault coverage, assess relevance, recommend save or skip

  • Works in 7 steps: Accept Input → Vault Coverage Check → Content Fetch & Analysis → …
  • Knowledge Management work in your project
  • SKILL.md covers Purpose, When to Invoke, Agent Mode Awareness and Pre-Flight Check, plus 7 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Scout is an agent skill from huytieu/COG-second-brain. Evaluate URLs and tools — check vault coverage, assess relevance, recommend save or skip

Its SKILL.md is about 1.8k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Knowledge Management. The repository describes itself as: Self-evolving second brain with 35 AI skills, 10 agents, and people CRM. Closed-loop harness: a V-model verification lifecycle where the worker never grades its own homework… The licence is MIT.

When your agent uses it

  • Knowledge Management work in your project

Example prompts

  • “/scout”

Workflow steps

7 steps, taken from the step headings in SKILL.md.

  1. Accept Input
  2. Vault Coverage Check
  3. Content Fetch & Analysis
  4. Relevance Assessment
  5. Recommendation
  6. Batch Summary (for multiple items)
  7. Execute Follow-up Actions

What it can do on your machine

Read from SKILL.md and the folder at commit 36ac9d7. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are markdown).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Scout loads about 1.8k tokens when it runs. Until then it costs about 24 tokens; SKILL.md has 797 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~24
When it runs · the whole SKILL.md, loaded when a task matches
~1.8k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from huytieu/COG-second-brain at commit 36ac9d7, republished under its MIT licence (© huytieu). 797 words, ~1,811 tokens.

Download SKILL.mdSave it as .claude/skills/scout/SKILL.md (or your agent's skills folder).
name
scout
description
Evaluate URLs and tools — check vault coverage, assess relevance, recommend save or skip
roles
all
integrations
web-fetch, web-search

COG Scout Skill

Purpose

Lightweight URL/tool triage that sits between "ignore" and /url-dump. Evaluates whether a URL or tool is worth saving or skipping — checking existing vault coverage, assessing relevance to the user's profile and interests, and recommending a clear next action.

When to Invoke

  • User wants to evaluate a URL or tool before committing to a full save
  • User says "scout this", "evaluate this", "should I save this?", "is this relevant?"
  • User shares one or more URLs and wants a quick relevance assessment
  • User mentions a tool/service name and wants to know if it's worth investigating

Agent Mode Awareness

Check agent_mode in 00-inbox/MY-PROFILE.md frontmatter:

  • If agent_mode: team — delegate vault scanning and web fetching to parallel sub-agents (one for vault search, one for content fetch/analysis). Combine results for recommendation.
  • If agent_mode: solo (default) — handle all scanning and analysis directly in the conversation. No delegation.

Pre-Flight Check

Before executing, check for user profile:

  1. Look for 00-inbox/MY-PROFILE.md and 00-inbox/MY-INTERESTS.md in the vault
  2. If NOT found:
    Welcome to COG! Scout works best with a profile for relevance matching.
    
    Would you like to run /onboarding first, or should I evaluate with general criteria?
  3. If found:
    • Read MY-PROFILE.md for active projects and role
    • Read MY-INTERESTS.md for topic areas
    • Read 00-inbox/MY-INTEGRATIONS.md for active integrations (check if web-fetch and web-search are available)

Boundary with /url-dump

Scout evaluates ("should I save this?"). URL-dump saves ("save this now").

  • Scout checks existing coverage, assesses relevance, and recommends an action
  • If the recommendation is Save, scout hands off to /url-dump with pre-filled category
  • Users who already know they want to save should use /url-dump directly

Process Flow

1. Accept Input

Accept one or more of:

  • URL(s): Direct links to evaluate
  • Tool/service name(s): Will search for the tool first
  • Mixed: Combination of URLs and names

Prompt (if no input provided):

What URL(s) or tool(s) would you like me to evaluate?
(You can paste URLs, tool names, or a mix)

Batch mode: Multiple URLs/names in one invocation are processed together with a summary table at the end.

2. Vault Coverage Check

For each URL or tool name, search the entire vault for existing coverage.

Search strategy:

  • Extract domain from URL (e.g., github.com/owner/repo → search for repo name)
  • Search for tool/service name across the whole vault (grep for domain, repo name, tool name)
  • Match against URL strings in frontmatter (url: fields) and inline links

If found:

🔍 Existing coverage found for [name]:
- [file path] — saved [date], category: [category]
- [file path] — mentioned in [context]

Want me to check if an update is needed, or skip this one?
3. Content Fetch & Analysis

If URL provided and web-fetch is active:

  • Fetch the URL content using WebFetch
  • Extract: title, description, content type, author, date

If tool name provided (no URL):

  • Use WebSearch to find the tool's primary page
  • Fetch and analyze the top result

Content type detection:

  • Tool/Service: Software, SaaS, API, library, framework
  • Article/Blog: Long-form content, tutorial, opinion piece
  • Repository: GitHub/GitLab repo (extract stars, last commit, language)
  • Research: Paper, study, academic content
  • News: Industry news, announcement
  • Reference: Documentation, spec, standard
Show full SKILL.md (368 more words)Show less
4. Relevance Assessment

Score relevance against user context:

Profile Match (from MY-PROFILE.md):

  • Does it relate to an active project? Which one?
  • Does it align with the user's role?
  • Does it fit the user's tech stack?

Interest Match (from MY-INTERESTS.md):

  • Does it match any declared interest topics?
  • How directly relevant is it?

Quality Signals:

  • For repos: stars, recent activity, maintainer health
  • For tools: pricing model, maturity, adoption
  • For articles: author credibility, publication quality, recency
  • For all: uniqueness vs. what's already in the vault
5. Recommendation

Based on analysis, recommend one of two actions:

Save — Worth adding to the knowledge base
✅ SAVE — [Title/Name]
Category: [suggested category for url-dump]
Relevance: [High/Medium] — [why it matters]
Projects: [affected project(s) if any]

Shall I hand off to /url-dump to save it?
Skip — Not relevant or not worth the time
⏭️ SKIP — [Title/Name]
Reason: [clear explanation — wrong stack, low quality, already covered, irrelevant to interests]
6. Batch Summary (for multiple items)

When processing multiple URLs/tools, end with a summary table:

markdown
## Scout Summary

| # | Item | Verdict | Reason |
|---|------|---------|--------|
| 1 | [Name 1] | ✅ Save | [brief reason] |
| 2 | [Name 2] | ⏭️ Skip | [brief reason] |

**Actions:**
- [X] items ready to save via /url-dump
7. Execute Follow-up Actions

Based on user confirmation:

  • Save items: Hand off to /url-dump with pre-filled category suggestion
  • Skip items: No action needed

Fallback Behavior

ScenarioBehavior
web-fetch unavailableEvaluate based on URL structure, domain reputation, and vault search only. Note that content wasn't fetched.
web-search unavailableFor tool-name inputs (no URL), ask the user for a direct URL instead. For URL inputs, proceed normally — web-search is not needed.
No user profileEvaluate with general quality/relevance criteria, skip personalized relevance scoring
URL is paywalledNote limitation, evaluate based on available preview and metadata
Tool not found via searchAsk user for more context or a direct URL

Uncertainty Handling

  • High confidence: Clear relevance match or clear irrelevance — give direct recommendation
  • Medium confidence: Partial match — present pros/cons, let user decide
  • Low confidence: Can't determine relevance — explain what's unclear, ask user for context

Integration with Other Skills

Downstream
  • Save → hands off to /url-dump with pre-filled category
Upstream
  • /daily-brief may surface new tools/services → user can run /scout to evaluate
  • /auto-research may discover tools during research → scout can triage them

Success Metrics

  • Quick triage (< 1 minute for single URL in solo mode)
  • Clear, actionable recommendations
  • Accurate vault coverage detection (no duplicate saves)
  • Relevance scoring matches user expectations
  • Smooth handoff to /url-dump when saving

Philosophy

Scout embodies COG's "evaluate before you accumulate" principle:

  • Not everything deserves a bookmark — be selective
  • Existing coverage should be surfaced before creating duplicates
  • Binary save/skip keeps decisions fast and avoids half-measures
  • Clear recommendations reduce decision fatigue

© huytieu, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/scout of huytieu/COG-second-brain.

Open the folder on GitHubat commit 36ac9d7

Compare with similar skills

Scout next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Scout compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Scout this skillhuytieu/COG-second-brain1.3k—~1.8kAutomated safety check: PassMIT
Logseq Review Workflow Evallogseq/logseq45k—~1kAutomated safety check: PassAGPL-3.0
Baoyu URL To Markdownsdyckjq-lab/llm-wiki-skill2.5k2 repos~3.2kAutomated safety check: PassNone
Obsidian CLIAtmosphere/atmosphere3.8k13 repos~795Automated safety check: PassApache-2.0
Esm Cjs Risk Scanlogseq/logseq45k—~3.3kAutomated safety check: PassAGPL-3.0
Capture Conversationoutline/outline41k—~474Automated safety check: PassCustom licence

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Questions about Scout

What does Scout do?

Evaluate URLs and tools — check vault coverage, assess relevance, recommend save or skip. Scout is an agent skill from huytieu/COG-second-brain.

When should I use Scout?

Scout fits situations like: knowledge Management work in your project.

How do I install Scout in Claude Code?

Run `npx skills add huytieu/COG-second-brain --skill scout -a claude-code`. Or copy the skill folder (skills/scout in huytieu/COG-second-brain) into .claude/skills/scout in your project. Claude Code loads it when a task matches its description.

How do I install Scout in Codex?

Run `npx skills add huytieu/COG-second-brain --skill scout -a codex`. Or copy the skill folder (skills/scout in huytieu/COG-second-brain) into .agents/skills/scout in your project. Codex loads it when a task matches its description.

Can I use Scout in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add huytieu/COG-second-brain --skill scout -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/scout, .gemini/skills/scout, .github/skills/scout and .opencode/skills/scout in your project.

What does Scout need to run?

SKILL.md names no scripts, command-line tools or credentials: Scout is instructions for the agent only.

Does Scout access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Scout safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Scout use?

Scout is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Scout use?

About 1.8k tokens (SKILL.md is roughly 7.2k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Scout?

Skills that share tags, products or a category with Scout: Logseq Review Workflow Eval (logseq/logseq, 45k stars), Baoyu URL To Markdown (sdyckjq-lab/llm-wiki-skill, 2.5k stars), Obsidian CLI (Atmosphere/atmosphere, 3.8k stars) and Esm Cjs Risk Scan (logseq/logseq, 45k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Scout?

huytieu (a GitHub user) maintains it in huytieu/COG-second-brain, which has 1,267 GitHub stars. The repository holds 37 skills in this directory. The repository was last updated on October 2, 2026.

Source: huytieu/COG-second-brain on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.