# Verify Skill

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

- Skill: `neuroaihub/verify-skill` (Agent Skill)
- Install (CLI): `npx skillmds@latest add neuroaihub/verify-skill`
- Raw SKILL.md: https://api.skillmd.com/api/skills/neuroaihub/verify-skill/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: neuroaihub (https://skillmd.com/u/neuroaihub)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/neuroaihub/verify-skill

---


# 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](https://github.com/HaoxuanLiTHUAI/awesome_cognitive_and_neuroscience_skills/issues).

---

## 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)

### 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:**

| # | Parameter | Skill Says | Citation | Your Verdict | Notes |
|---|-----------|-----------|----------|-------------|-------|
| 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.**