improve-review-bot

name	improve-review-bot
description	Analyze feedback on bk-docsbot suggestions to identify rejection patterns and draft prompt improvements that increase accuracy. Run this periodically to close the feedback loop.

Improve the documentation review bot

You are analyzing feedback on the bk-docsbot — an AI-powered documentation style reviewer — to improve its accuracy.

Locate project files

This skill lives inside the docs repo. Derive all paths from the skill directory:

REPO_ROOT="${CLAUDE_SKILL_DIR}/../../.."

Key files relative to ${REPO_ROOT}:

• Feedback script: scripts/measure-bot-accuracy.sh
• Review agent: tools/pr-review-agent/main.go
• Style guide: AGENTS.md
• Prompt location: buildPrompt() function in main.go (search for func buildPrompt)

---

Context

The bk-docsbot is a Go program (tools/pr-review-agent/main.go) that uses Claude to review documentation PRs against the style guide in AGENTS.md. When it finds violations, it posts GitHub suggestion comments on the PR.

The bot's prompt has two parts:

1. Style guide: Loaded from AGENTS.md at the repo root
2. Review instructions: Hardcoded in the buildPrompt() function in main.go — this includes the "Critical Rules" that control behavior

Step 1: Gather feedback

Run the measurement script with the --feedback flag to get detailed data:

cd "${REPO_ROOT}"
bash scripts/measure-bot-accuracy.sh --feedback 2>&1 | tee /tmp/bot-feedback.txt

This outputs every bot suggestion along with:

• The bot's reasoning for the suggestion
• The suggested code change
• The classification (COMMITTED, ACCEPTED, REJECTED, NOT APPLIED, and so on)
• Any human reply comments from the docs team
• Reaction data (👍/👎)

Step 2: Analyze rejection patterns

Focus on items classified as REJECTED or NOT APPLIED. Look for patterns:

1. Rule misapplication: The bot cited a rule that doesn't apply to the context (for example, applying YAML rules to HTML, or prose rules to code)
2. False positives: The bot flagged something that wasn't actually a violation
3. Context blindness: The bot didn't understand the surrounding context (for example, ERB files, partial templates, intentional terminology)
4. Overreach: The bot suggested changes beyond what the style guide requires
5. Domain errors: The bot got Buildkite-specific terminology wrong

Group rejections by category. For each category, determine:

• How many rejections fall into this category?
• Is there a common trigger?
• Can this be fixed by adding a rule to the "Critical Rules" section, or does it need a style guide clarification?

Step 3: Analyze accepted suggestions

Also review highly-accepted patterns (COMMITTED suggestions) to understand what the bot does well. This helps avoid accidentally weakening good behavior when fixing bad behavior.

Step 4: Draft improvements

Based on the analysis, draft specific changes. There are two places changes can go:

Changes to buildPrompt() in main.go

The "Critical Rules" section controls the bot's behavior. Add rules like:

• "Do not flag X in Y context"
• "When reviewing ERB files (.md.erb), be aware that..."
• "The term 'agent token' is correct when referring to..."

These are behavioral guardrails. Keep them concise and specific.

Changes to AGENTS.md

If the style guide itself is ambiguous or missing a rule that caused confusion, propose a clarification there instead.

Step 5: Draft a PR

Create a branch and commit the changes:

cd "${REPO_ROOT}"
git checkout main && git pull
git checkout -b improve-review-bot-prompt
# ... make edits ...
git add -A && git commit -m "Improve review bot prompt based on feedback analysis"
git push -u origin improve-review-bot-prompt

Then provide a PR description that includes:

• The current accuracy rate
• A summary of the rejection patterns found
• What changes were made and why
• Expected impact on accuracy

Guidelines

• Be conservative: Only add rules you're confident about based on multiple data points
• Be specific: "Do not flag missing </tr> tags in HTML tables" is better than "Be more careful with HTML"
• Don't weaken good behavior: If the bot is 100% accepted on product naming suggestions, don't add caveats that might reduce that
• Preserve the prompt's tone: The existing Critical Rules section is terse and imperative — match that style
• Show your work: Include the data that supports each change in the PR description