Report Feedback Agent Skill

Report Feedback

When to Use

• Reviewing negative feedback (downvotes, deletions) on AI-generated comments
• Investigating comment quality issues for a specific language
• Understanding why AI comments were marked bad or deleted during a period

Understanding Feedback

Feedback describes why a bad AI comment is bad. Each feedback entry includes a reason (e.g., FactuallyIncorrect, AcceptedSDKPattern, RenderingBug) and an optional free-text comment. There are no upvotes in the feedback data — upvotes on comments exist in APIView but are not surfaced through this report. All feedback returned by this command is negative.

Do NOT state that feedback is "100% bad" or highlight that all feedback is negative in your summary. This is always the case by design — the report only surfaces negative feedback. Treat it as a given and focus the summary on the count, reasons, themes, and actionable insights.

Defaults

Unless the user says otherwise, always apply these defaults:

• Environment: production
• Language: All languages (do not pass --language unless user specifies one)
• Exclude: Do not pass --exclude unless user asks to filter out certain feedback types
• Include implicit: Always pass --include-implicit by default. Only omit it if the user explicitly asks to exclude implicit bad comments.
• Format: JSON (do not pass --format)

Implicit Bad Comments

The --include-implicit flag also returns implicit bad comments: AI comments on approved revisions that were never upvoted, downvoted, resolved, and have no Feedback entries. The inference is that the reviewer ignored them and approved anyway, suggesting they were unhelpful.

> Date semantics differ: Explicit feedback is filtered by feedback submission time (Feedback[].SubmittedOn / ChangeHistory[].ChangedOn), but implicit bad is filtered by comment creation time (CreatedOn). A comment created in January with no interaction will appear in January's implicit bad results, not March's.

This skill always passes --include-implicit (the CLI flag defaults to off, but the skill includes it for completeness). It has a weaker signal than explicit feedback because there is no reason or confirmation — just silence. Only omit --include-implicit if the user explicitly asks to exclude them (e.g., "only explicit feedback", "exclude implicit bad").

The output will contain items with "FeedbackTypes": ["implicit_bad"].

Summarizing Implicit Bad

When presenting results, break out implicit bad themes separately from explicit feedback:

• Explicit feedback — Summarize count, breakdown by reason, and themes for items that have explicit feedback types (e.g., bad, delete).
• Implicit bad — Summarize separately: count, common comment topics/patterns, and any notable themes. Note that these lack a reason — group them by the comment content or guideline referenced instead.

This separation helps the user understand the strength of signal behind each theme.

Date Resolution

The user will typically specify a calendar month by name (e.g. "March", "January 2025"). Resolve to the full month date range:

| User says | start_date | end_date |

|-----------|-----------|----------|

| "March" (current year) | YYYY-03-01 | YYYY-03-31 |

| "January 2025" | 2025-01-01 | 2025-01-31 |

| "March 1 to March 15" | YYYY-03-01 | YYYY-03-15 |

When only a month name is given without a year, use the current year. Be careful with month lengths (28/29/30/31 days).

Running the Command

Step 1: Run the Command

Show the resolved command and run it immediately in a foreground terminal with a 120-second timeout (timeout: 120000). Redirect to a file since feedback output can be very large.

Full terminal command (cleanup + run):

New-Item -ItemType Directory -Path output -Force | Out-Null; if (Test-Path output/feedback_output.json) { Remove-Item output/feedback_output.json }; python cli.py report feedback -s <start_date> -e <end_date> --include-implicit | Out-File -Encoding UTF8 output/feedback_output.json

After the command completes, read the output file with read_file to get the JSON results. Summarize the findings for the user (total count, breakdown by feedback reason, common themes, etc.).

Step 2: Answer Follow-up Questions

For follow-up questions about the same data (filtering, counting, searching), read the output file with read_file instead of re-running the command. The file is at output/feedback_output.json.

Examples

# All feedback for March 2025 (implicit bad included by default)
python cli.py report feedback -s 2025-03-01 -e 2025-03-31 --include-implicit

# Python feedback only
python cli.py report feedback -s 2025-03-01 -e 2025-03-31 -l python --include-implicit

# Exclude implicit bad (only explicit feedback)
python cli.py report feedback -s 2025-03-01 -e 2025-03-31

# Exclude good feedback (show only bad and deleted)
python cli.py report feedback -s 2025-03-01 -e 2025-03-31 --include-implicit --exclude good

# YAML output
python cli.py report feedback -s 2025-03-01 -e 2025-03-31 --include-implicit --format yaml

# Staging environment
python cli.py report feedback -s 2025-03-01 -e 2025-03-31 --include-implicit --environment staging

Available Flags

| Flag | Type | Default | Description |

|------|------|---------|-------------|

| --start-date / -s | string | required | Start date (YYYY-MM-DD) |

| --end-date / -e | string | required | End date (YYYY-MM-DD) |

| --language / -l | string | all | Language to filter by (e.g., python, Go, C#) |

| --environment | string | production | production or staging |

| --exclude | list | none | Feedback types to exclude: good, bad, delete, implicit_bad |

| --include-implicit | flag | off | Include implicit bad comments (unresolved, unvoted on approved revisions) |

| --format / -f | string | json | Output format: json or yaml |

Gotchas

• Output can be large: Redirect to file and use read_file rather than relying on terminal output.
• Date range semantics are mixed: Explicit feedback filters by feedback submission time (a comment created in January but downvoted in March appears in March). Implicit bad filters by comment creation time (a comment created in January with no interaction appears in January).
• Use python cli.py not .\avc: The avc.bat script may resolve to system Python.
• Do NOT use 2>&1: Merges stderr into stdout, corrupting JSON. Only redirect stdout.
• Do NOT use >: Produces UTF-16 in PowerShell 5.1. Use | Out-File -Encoding UTF8.
• Month end dates: February has 28/29 days, April/June/Sept/Nov have 30 days.