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Agent skill

Verify Skill

by NeuroAIHub in NeuroAIHub/BrainPilot

Interactive skill verification — assess accuracy of parameters, citations, and methodology through structured expert review

AGPL-3.0Auto-check passedResearch & Science

Install Verify Skill

skills CLI
$ npx skills add NeuroAIHub/BrainPilot --skill verify-skill -a claude-code

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

GitHub CLI
$ gh skill install NeuroAIHub/BrainPilot verify-skill --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/NeuroAIHub/BrainPilot.git skills-src && mkdir -p .claude/skills && cp -r skills-src/packages/skills/skills/01_Meta-Skills/verify-skill .claude/skills/verify-skill && 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
verify-skill
GitHub stars
1.1k
Token cost
~3.2k tokens
SKILL.md length
1,454 words
Files
1
Skills in repo
59
Repo updated
First seen
Licence
AGPL-3.0

At a glance

Interactive skill verification — assess accuracy of parameters, citations, and methodology through structured expert review

  • Works in 6 steps: Cognitive Alignment → Experience Collection → Test Scenario Construction → …
  • Tasks that involve Citation management
  • SKILL.md covers Purpose, When to Use This Skill, Research Planning Protocol and ⚠️ Verification Notice, plus 5 more sections
  • Calls gh

What it does

Verify Skill is an agent skill from NeuroAIHub/BrainPilot. Interactive skill verification — assess accuracy of parameters, citations, and methodology through structured expert review

Its SKILL.md is about 3.2k 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 Research & Science, covering Citation management. The repository describes itself as: BrainPilot: Automating Brain Discovery with Agentic Research. The licence is AGPL-3.0.

When your agent uses it

  • Tasks that involve Citation management

Example prompts

  • “/verify-skill”

Workflow steps

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

  1. Cognitive Alignment
  2. Experience Collection
  3. Test Scenario Construction
  4. Item-by-Item Assessment
  5. Apply Corrections
  6. Report Generation and Submission

What it can do on your machine

Read from SKILL.md and the folder at commit 93f6855. 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

    Shell commands in SKILL.md call:

    • gh

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

  • Network

    Links to these hosts (documentation or services it may open):

    • github.com
    • cli.github.com

    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

Verify Skill loads about 3.2k tokens when it runs. Until then it costs about 34 tokens; SKILL.md has 1,454 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~34
When it runs · the whole SKILL.md, loaded when a task matches
~3.2k

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 NeuroAIHub/BrainPilot at commit 93f6855, republished under its AGPL-3.0 licence (© NeuroAIHub). 1,454 words, ~3,199 tokens.

Download SKILL.mdSave it as .claude/skills/verify-skill/SKILL.md (or your agent's skills folder).
name
verify-skill
description
Interactive skill verification — assess accuracy of parameters, citations, and methodology through structured expert review
domain
meta-skill
version
1.0.0
review_status
ai-generated
dependencies.required
research-literacy

Verify Skill

Purpose

This meta-skill guides domain experts through a structured verification of any skill in this repository. It produces a detailed verification report that records the expert's assessment of parameters, citations, and methodology — then submits the report to GitHub Discussions for community knowledge building.

Verification is the highest-impact contribution a domain expert can make. Every skill starts as ai-generated and needs human verification to progress to community-reviewed or expert-verified.

When to Use This Skill

Activate when the user:

  • Says "verify a skill", "验证这个 skill", "review this skill's accuracy"
  • Wants to check whether a skill's parameters and citations are correct
  • Has domain expertise and wants to contribute a verification
  • Is reviewing a skill before using it in their research

Research Planning Protocol

Before starting the verification process, you MUST:

  1. Identify the target skill — Which skill will be verified? Read its SKILL.md.
  2. Assess the reviewer's qualifications — What is their domain expertise and experience level?
  3. Define verification scope — Will this be a full verification or focused on specific sections?
  4. Note inherent limitations — Verification without lab replication cannot confirm all claims; flag what can and cannot be verified from literature alone.
  5. Present the verification plan to the user and WAIT for confirmation before proceeding.

For detailed methodology guidance, see skills/research-literacy/SKILL.md.

⚠️ Verification Notice

This skill was generated by AI from academic literature. All parameters, thresholds, and citations require independent verification before use in research. If you find errors, please open an issue.

Prerequisites

Before running this skill, verify:

  1. gh CLI is installed and authenticated

    Run: gh auth status

    If not authenticated, tell the user:

    To submit verification reports, you need the GitHub CLI. Install it from https://cli.github.com/ and run gh auth login. Alternatively, I can save the report as a local markdown file.

  2. The target skill exists — The skill directory must exist under skills/ in the repository.

Interactive Flow (6 Phases)

Progress Tracking (Required)

At the start of the verification, you MUST create a task list (using TodoWrite or equivalent) with these items:

  • Phase 1: Cognitive Alignment
  • Phase 2: Experience Collection
  • Phase 3: Test Scenario Construction
  • Phase 4: Item-by-Item Assessment
  • Phase 5: Apply Corrections to Skill
  • Phase 6: Generate Report and Submit to GitHub Discussions ← DO NOT SKIP

Mark each item as you complete it. Do NOT consider the verification complete until ALL items are checked, especially Phase 6 (GitHub submission).

Phase 1 — Cognitive Alignment

Goal: Ensure the reviewer understands what the skill does before assessing it.

  1. Read the target skill's SKILL.md in full.

  2. Present a summary to the reviewer:

    • What this skill does (purpose and domain)
    • Typical use scenarios (when it triggers)
    • Key parameters and claims it makes (list the specific numbers and citations)
    • What verification means in this context

  3. Ask the reviewer: "Does this summary match your understanding of the skill? Anything to clarify before we proceed?"

  4. Wait for confirmation before moving to Phase 2.

Next → Phase 2: Experience Collection (4 remaining phases until GitHub submission)

Phase 2 — Experience Collection

Goal: Understand the reviewer's domain knowledge to calibrate the verification.

Present these questions:

Q1: How familiar are you with this domain? (1-5)

  • 1 = Heard of it
  • 2 = Read about it
  • 3 = Studied it formally
  • 4 = Use it in my research
  • 5 = I am a specialist in this area

Q2: Have you used these methods in your research?

  • Yes, I use them regularly (describe briefly)
  • Yes, I have used them before (describe context)
  • No, but I know the literature well
  • No, I am learning about this area

Q3: For the key parameters listed in Phase 1, what values do you typically use?

  • Present each key parameter from the skill and ask the reviewer what value they use or expect
  • This is a per-parameter question — iterate through the most important parameters
  • Accept "I don't know" as a valid answer

Q4: What pitfalls have you encountered that this skill should mention?

  • Free text — any practical warnings from experience

Next → Phase 3: Test Scenario Construction (3 remaining phases until GitHub submission)

Phase 3 — Test Scenario Construction

Goal: Test the skill against a realistic scenario to evaluate its practical advice.

  1. Ask the reviewer to describe a scenario:

    "Describe a real or realistic dataset and research question where you would use the methods covered by this skill. Include: modality, sample size, conditions, and what you are trying to find."

  2. Run the target skill against this scenario (simulate how the skill would respond to the described research question).

  3. Present the skill's recommendations for this scenario.

  4. Ask the reviewer to evaluate:

    • Did the skill give appropriate recommendations for this scenario?
    • Were there any incorrect suggestions?
    • What important advice was missing?

Next → Phase 4: Item-by-Item Assessment (2 remaining phases until GitHub submission)

Show full SKILL.md (668 more words)Show less
Phase 4 — Item-by-Item Assessment

Goal: Systematic parameter-by-parameter verification with structured scoring.

For each key claim in the skill (parameters, thresholds, citations, methodological recommendations), present a table row and ask the reviewer to assess:

Assessment format:

#ParameterSkill SaysCitationYour VerdictNotes
1[param name][value from skill][cited source]✅ / ⚠️ / ❌ / ❓[reviewer's explanation]
2...............

Verdict options:

  • ✅ Confirmed — The value and citation are correct
  • ⚠️ Context-dependent — Correct in some contexts but not universally; needs qualification
  • ❌ Incorrect — The value or citation is wrong (reviewer provides the correct information)
  • ❓ Cannot verify — The reviewer does not have enough expertise or resources to confirm

Process each parameter interactively — present 3-5 parameters at a time, get the reviewer's verdicts, then continue with the next batch.

After all parameters are assessed, collect overall ratings (1-5 stars each):

  1. Parameter accuracy — Are the numerical values and thresholds correct?
  2. Completeness — Does the skill cover all important aspects of this methodology?
  3. Practical usefulness — Would this skill actually help a researcher do better work?
  4. Pitfall awareness — Does the skill warn about common mistakes and edge cases?

⚠️ CRITICAL: Do NOT stop here. Next → Phase 5: Apply Corrections, then Phase 6: Submit to GitHub Discussions. The verification is NOT complete without submission.

Phase 5 — Apply Corrections

Goal: Update the skill based on verification findings.

If Phase 4 produced any ❌ (Incorrect) or ⚠️ (Context-dependent) verdicts:

  1. List all corrections needed — Summarize what parameters, citations, or methodology need updating based on the reviewer's verdicts.

  2. Apply corrections to the skill's SKILL.md:

    • Fix incorrect parameter values (replace with reviewer-provided values and citations)
    • Add missing caveats or context qualifications for ⚠️ items
    • Update citations where the reviewer identified errors
    • Add pitfalls and warnings from the reviewer's experience (Phase 2 Q4)

  3. Update the skill's review_status in the YAML frontmatter:

    • If the reviewer's familiarity was 4-5 → set to "expert-verified"
    • If the reviewer's familiarity was 2-3 → set to "community-reviewed"
    • If the reviewer's familiarity was 1 → keep as "ai-generated"

  4. Commit the changes with a descriptive message, e.g.: fix: update [skill-name] parameters per expert verification

  5. Present the diff to the reviewer for confirmation.

If Phase 4 produced NO corrections needed (all ✅), skip to Phase 6 but still update review_status if appropriate.

⚠️ CRITICAL: You are NOT done. You MUST proceed to Phase 6 to submit the verification report to GitHub Discussions.

Phase 6 — Report Generation and Submission

Goal: Generate a structured verification report and submit it to GitHub Discussions.

  1. Generate the verification report using the format below.

  2. Present the complete report to the reviewer. Present options:

    • Approve all — Submit as shown
    • Delete sections — Remove specific sections
    • Anonymize — Replace identifying information (name, institution) with generic descriptions
    • Save locally only — Save without submitting to GitHub
    • Abort — Cancel without saving

  3. Wait for explicit confirmation before submitting.

  4. Submit to GitHub Discussions in the "Verification" category.

Submission command:

bash
gh api graphql -f query='
mutation {
  createDiscussion(input: {
    repositoryId: "REPO_ID",
    categoryId: "VERIFICATION_CATEGORY_ID",
    title: "Verification Report: SKILL_NAME",
    body: "REPORT_BODY_HERE"
  }) {
    discussion {
      url
    }
  }
}'

To get the required IDs:

bash
gh api graphql -f query='
{
  repository(owner: "HaoxuanLiTHUAI", name: "awesome_cognitive_and_neuroscience_skills") {
    id
    discussionCategories(first: 10) {
      nodes {
        id
        name
      }
    }
  }
}'

After successful submission, display the Discussion URL to the reviewer.

If submission fails: Save the report to ~/.cache/awesome-neuro-skills/pending-verifications/YYYY-MM-DD-skill-name.md and provide manual submission instructions.

Verification Report Format

markdown
## Verification Report: [skill-name]

### Reviewer Profile
- **Domain**: [e.g., "cognitive neuroscience"]
- **Experience**: [e.g., "5 years EEG research"]
- **Familiarity with this topic**: [1-5 from Q1]
- **Context**: [e.g., "currently running oddball paradigm study"]

### Verification Scenario
> [Description of the test scenario used in Phase 3]

### Skill Evaluation Against Scenario
> [Summary of how well the skill performed on the test scenario]

### Parameter Review
| # | Parameter | Skill Says | Citation | Verdict | Notes |
|---|-----------|-----------|----------|---------|-------|
| 1 | [param] | [value] | [citation] | ✅/⚠️/❌/❓ | [explanation] |

### Expert Insights
> [Reviewer's professional knowledge that supplements or corrects the skill — from Q3, Q4, and Phase 3 feedback]

### Overall Scores
| Dimension | Score |
|-----------|-------|
| Parameter accuracy | [1-5 stars] |
| Completeness | [1-5 stars] |
| Practical usefulness | [1-5 stars] |
| Pitfall awareness | [1-5 stars] |

### Suggested Improvements
- [Concrete suggestions for updates to the skill]

---
*Submitted via the `verify-skill` meta-skill.*

Verification Depth Guidance

What CAN be verified from literature
  • Whether a cited paper exists and the citation is correct
  • Whether the cited paper actually recommends the stated parameter value
  • Whether the methodology matches current field consensus
  • Whether important caveats or alternatives are mentioned
What CANNOT be verified without lab work
  • Whether specific parameter values are optimal for all datasets
  • Whether the pipeline actually produces valid results on real data
  • Whether edge cases and failure modes are exhaustively covered

Flag this distinction clearly in the report.

Completion Checklist

Before considering this verification COMPLETE, you MUST confirm ALL of the following:

  • Skill SKILL.md has been updated with corrections (if any ❌/⚠️ verdicts in Phase 4)
  • review_status has been updated in the skill's YAML frontmatter
  • Changes have been committed to git
  • Verification report has been submitted to GitHub Discussions (or saved locally if gh is unavailable)
  • Discussion URL (or local file path) has been shown to the reviewer

If any item above is unchecked, GO BACK and complete it now. Do NOT end the conversation.

© NeuroAIHub, AGPL-3.0. 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 packages/skills/skills/01_Meta-Skills/verify-skill of NeuroAIHub/BrainPilot.

Open the folder on GitHubat commit 93f6855

Compare with similar skills

Verify Skill 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.

Verify Skill compared with similar skills
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Systematic Review ScreenerImbad0202/academic-research-skills51k—~8.4kAutomated safety check: PassCustom licence
NetworkxzLanqing/codex-claude-academic-skills4.7k15 repos~3.2kAutomated safety check: PassBSD-3-Clause
Literature Reviewneflibata-feng/MyArxiv-Agent12620 repos~5.9kAutomated safety check: NotesMIT
Openalex Databaseneflibata-feng/MyArxiv-Agent12612 repos~3kAutomated safety check: PassCustom licence

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Categories

Questions about Verify Skill

What does Verify Skill do?

Interactive skill verification — assess accuracy of parameters, citations, and methodology through structured expert review. Verify Skill is an agent skill from NeuroAIHub/BrainPilot.

When should I use Verify Skill?

Verify Skill fits situations like: tasks that involve Citation management.

How do I install Verify Skill in Claude Code?

Run `npx skills add NeuroAIHub/BrainPilot --skill verify-skill -a claude-code`. Or copy the skill folder (packages/skills/skills/01_Meta-Skills/verify-skill in NeuroAIHub/BrainPilot) into .claude/skills/verify-skill in your project. Claude Code loads it when a task matches its description.

How do I install Verify Skill in Codex?

Run `npx skills add NeuroAIHub/BrainPilot --skill verify-skill -a codex`. Or copy the skill folder (packages/skills/skills/01_Meta-Skills/verify-skill in NeuroAIHub/BrainPilot) into .agents/skills/verify-skill in your project. Codex loads it when a task matches its description.

Can I use Verify Skill 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 NeuroAIHub/BrainPilot --skill verify-skill -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/verify-skill, .gemini/skills/verify-skill, .github/skills/verify-skill and .opencode/skills/verify-skill in your project.

What does Verify Skill need to run?

Going by SKILL.md and its folder, Verify Skill needs the command-line tools its instructions call (gh).

Does Verify Skill access the network?

SKILL.md names 2 domains. As links in the text: github.com and cli.github.com. This is read from the text; nothing was executed.

Is Verify Skill 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 Verify Skill use?

Verify Skill is published under the AGPL-3.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Verify Skill use?

About 3.2k tokens (SKILL.md is roughly 13k 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 Verify Skill?

Skills that share tags, products or a category with Verify Skill: Content Research Writer (weapp-tailwindcss/weapp-tailwindcss, 1.9k stars), Systematic Review Screener (Imbad0202/academic-research-skills, 51k stars), Networkx (zLanqing/codex-claude-academic-skills, 4.7k stars) and Literature Review (neflibata-feng/MyArxiv-Agent, 126 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Verify Skill?

NeuroAIHub (a GitHub organization) maintains it in NeuroAIHub/BrainPilot, which has 1,062 GitHub stars. The repository holds 59 skills in this directory. The repository was last updated on October 2, 2026.

Source: NeuroAIHub/BrainPilot on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.