OpenClaw Skill Guide for AI Agent QA Review Workflows

AI agents are useful because they can take work across steps. They can read files, call tools, write drafts, run checks, and send updates. That same strength creates a problem: the output can look finished before it has actually been reviewed.

For a person using one chatbot, quality control is mostly manual. For an operator using recurring agents, scheduled automations, browser tools, file tools, and message tools, manual inspection alone does not scale.

That is what an OpenClaw skill is good for.

An AI agent QA review skill gives the system a standard way to inspect its own work before publication, deployment, handoff, or external messaging. It makes obvious failures harder to ship and review evidence easier to find later.

What the QA skill should review

The skill should focus on things that can be checked repeatedly.

Useful QA categories include:

• Task completion against the original request
• Missing required fields
• Factual claims that need verification
• Unsupported claims
• Broken links
• Formatting errors
• Tone mismatch
• Forbidden actions
• Approval-gated changes
• Tracking or analytics changes
• Secret leakage
• Source attribution
• Word count or length limits
• Final proof

This is not only for blog posts. A QA skill can review status updates, code changes, SEO briefs, customer support drafts, research summaries, compliance evidence, and deployment reports.

The important part is that the skill produces a decision, not only comments. The output should say whether the work is ready, blocked, or needs edits.

The core output contract

Every QA skill should have a simple output contract. If the output varies every time, downstream automation cannot use it.

A practical format:

Status: PASS | NEEDS_EDIT | BLOCKED

Critical issues:
- ...

Required edits:
- ...

Optional improvements:
- ...

Proof checked:
- ...

Approval needed:
- YES | NO

Final recommendation:
- ...

This lets the next step make a clear decision. If Status: PASS and Approval needed: NO, the workflow can continue. If Status: NEEDS_EDIT, the writer agent can revise. If Status: BLOCKED, the workflow stops and asks for human input.

Machines can route it. Humans can scan it.

Start with task fit

The first QA check is whether the output actually answers the request.

Agents often fail softly. A content task asked for two posts and the agent wrote one. A report asked for bullets only and the answer includes long paragraphs. A code task asked for no tracking changes and the diff touched analytics. A deployment brief asked for filenames and slugs, but the message only says "new content is ready."

The QA skill should compare:

• Original request
• Required deliverables
• Constraints
• Output files or message
• Evidence produced

Then it should list missing requirements plainly.

Example:

Status: NEEDS_EDIT
Required edits:
- Article 2 is 1,180 words; request required 1,500-2,000 words
- Header is missing meta description
- Deploy brief does not include file paths

This is not glamorous. It prevents avoidable failure.

Add claim verification

A QA skill should separate claims into tiers.

Tier 1: Claims that must be verified

These include prices, dates, laws, current model names, platform features, product availability, security statements, rankings, and anything that could change. If the output says "latest," "best," "current," or "as of 2026," it probably needs verification.

Tier 2: Claims that can be checked against local context

These include filenames, commit hashes, deployed URLs, local config values, tool outputs, and prior status records.

Tier 3: General guidance

These are conceptual explanations that do not require live verification unless the user asked for citations or the topic is high-risk.

The QA skill should not demand citations for every sentence. That makes review slow and noisy. It should identify claims where being wrong would matter.

Example:

Verification needed:
- "OpenClaw supports X connector" needs either local tool proof or docs proof
- "This URL is live" needs HTTP check
- "No tracking changed" needs diff or hash proof

When the claim cannot be verified, say so. Unknown beats invented proof.

Review formatting against channel

Good output in one channel can fail in another.

A Telegram SEO update should be short bullets. A GitHub PR review should lead with findings and file lines. A blog post should have a clean title, meta fields, headings, and internal consistency. A customer email should not look like a changelog.

The QA skill should know the destination:

• Telegram: short bullets, no tables, no long sections
• Blog: meta title, description, slug, H1, scannable H2s
• GitHub review: issues first, severity order, file references
• Internal report: status, blockers, proof, next actions
• Customer response: concise answer, no internal notes, no secrets

This prevents the right information shipping in the wrong shape.

Check approval gates

Approval gates are where many workflows fail. The agent does a task correctly, then takes one step too many.

The QA skill should identify approval-gated actions:

• Sending external email
• Posting publicly
• Deploying production changes
• Changing DNS
• Editing tracking code
• Publishing on a client site
• Changing billing or subscriptions
• Sharing private files
• Running destructive commands

In OpenClaw, this matters because agents may access files, channels, browser sessions, and deployment tools. Capability does not equal permission.

A useful QA line:

Approval needed: YES
Reason: final message would send unpublished financial detail to an external recipient

The skill should be conservative here. A paused workflow is cheaper than a bad external action.

Review proof before done claims

A good QA skill rejects unproven completion claims.

"Deployed" requires deployment proof.

"Live" requires a live check.

"No tracking changed" requires a diff, hash, or explicit file inspection.

"Article written" requires the file path and basic content check.

"Sent to Vic" requires message ID or tool confirmation.

For recurring operations, proof is the difference between a useful agent and a confident narrator. The QA skill should not accept vibes as evidence. Vibes are not a build artifact.

Proof can be lightweight:

• File exists
• Word count in range
• HTTP 200
• Sitemap contains URL
• Tool returned message ID
• Git status shows expected files
• Diff excludes protected paths
• Log entry written

The point is not bureaucracy. The point is to avoid work that only exists in the agent's final answer.

Make the skill reusable

A reusable OpenClaw QA skill should avoid being tied to one content type.

Use inputs like:

Task:
Destination:
Required deliverables:
Constraints:
Files changed:
Proof available:
Risk flags:

Then let the skill apply the right review lens.

For example, a blog content batch might pass:

Task: Write 2 blog posts
Destination: openclawdashboard.com blog
Required deliverables: 2 markdown files, each 1,500-2,000 words, meta title, description, URL slug
Constraints: save in content/openclaw, brief deploy owner
Files changed: 56..., 57...
Proof available: word counts, filenames, deploy brief sent
Risk flags: external deployment handled by Ebbot

A support workflow might pass:

Task: Draft customer reply
Destination: email
Required deliverables: concise answer, no internal notes
Constraints: do not send without approval
Files changed: none
Proof available: source ticket, product doc
Risk flags: customer-facing

Same skill, different review path.

Add severity levels

Not every issue should block delivery.

Use three severity levels:

• Critical: must stop
• Required: must edit before shipping
• Optional: improves quality but does not block

Critical issues include secret leakage, missing approval, unsupported high-risk claims, broken production claims, or doing the wrong task.

Required issues include missing metadata, wrong length, unclear proof, broken formatting, or weak source support.

Optional issues include stronger examples, tighter title, better internal links, or clearer wording.

This keeps the QA output actionable. A reviewer should not have to guess whether a note matters.

Example QA skill instructions

A compact skill can look like this:

# OpenClaw QA Review Skill

## Trigger
Use when reviewing AI agent output before publishing, deployment, external send, or completion report.

## Inputs
- Original task
- Output or changed files
- Destination
- Constraints
- Available proof

## Process
1. Compare output against task requirements.
2. Identify destination-specific formatting problems.
3. Flag unsupported or high-risk claims.
4. Check approval-gated actions.
5. Verify proof for completion claims.
6. Return PASS, NEEDS_EDIT, or BLOCKED.

## Output
Use the fixed QA contract:
- Status
- Critical issues
- Required edits
- Optional improvements
- Proof checked
- Approval needed
- Final recommendation

That is enough for a first version. Do not overbuild the skill before it has real examples.

Run QA before and after revision

For higher-value workflows, run QA twice.

The first pass catches obvious failures. The writer or executor fixes them. The second pass confirms the fixed output meets the contract.

This is useful for:

• Blog content batches
• SEO deployment briefs
• Customer-facing support replies
• Public changelogs
• Security questionnaire answers
• Legal-adjacent summaries
• Investor updates

Do not run expensive QA on every tiny internal note. Use judgment. A two-line internal reminder does not need a full inspection. A production deploy summary does.

Human review still matters

The QA skill is not a substitute for ownership. It is a filter. It catches missing fields, weak proof, obvious risk, and formatting errors. It cannot know every business preference, legal nuance, or relationship context.

Use human review when:

• The message is external and sensitive
• The task involves money, legal, medical, or security risk
• The agent is making a strategic recommendation
• The output changes public positioning
• The source material is incomplete
• The cost of being wrong is high

The right workflow is not agent or human. It is agent does repeatable checks, human handles judgment.

Common anti-patterns

Anti-pattern 1: The QA skill rewrites everything

QA should identify issues. It can suggest edits, but it should not silently replace the original work unless asked.

Anti-pattern 2: The skill treats all claims equally

Focus verification on claims that are current, risky, specific, or user-facing.

Anti-pattern 3: The skill ignores the destination

A good answer in a blog post can be a bad Telegram update. Channel fit matters.

Anti-pattern 4: The skill accepts unproven completion

If there is no proof, say there is no proof. This one rule improves agent reliability immediately.

Anti-pattern 5: The skill has no final status

Comments without a decision create another review task. Always return PASS, NEEDS_EDIT, or BLOCKED.

A launch checklist

Before using the QA skill in production, confirm:

• The fixed output contract is defined
• Critical, required, and optional issue levels are clear
• Approval-gated actions are listed
• Destination-specific formatting rules are included
• Proof standards are defined
• The skill can review files and messages
• A human can override the recommendation
• QA logs are stored with the task

Start with one workflow. A content pipeline is usually a good first target because the requirements are easy to state: title, description, slug, word count, topic match, links, and deploy brief.

Once that works, add QA to more sensitive workflows such as outbound email, customer support, compliance evidence, and security review.

Final recommendation

Every serious AI agent stack needs a review layer. The more tools an agent can use, the more important the review layer becomes.

OpenClaw skills make that review layer practical because rules can live beside the workflow. The agent can load the QA contract, inspect the output, check proof, and decide whether the task is ready to move forward.

That is how private agent automation becomes operational. Not by trusting the first draft. By making the second look boring, complete, and evidenced.