AI Issue Assessment Commenter

GitHub Action to automatically assess issues with an AI model, post (or optionally suppress) a structured review comment, and apply standardized AI derived labels based on configurable prompt files.

Table of Contents

• Overview
• How It Works
• Features
• Prompt File Schema
• Inputs
• Label → Prompt Mapping
• Regex Customization
• Suppressing the Comment
• Example Workflow Setup
• Outputs / Labels Added
• Required Permissions
• Troubleshooting
• Development
• Contributing
• Security / Reporting Issues
• FAQ
• License

Overview

This action evaluates newly labeled GitHub Issues using an AI model available through GitHub Models (or a compatible endpoint you provide) and:

1. Selects one or more prompt configuration files based on existing issue labels.
2. Runs inference with the chosen model & system prompt.
3. Extracts an "assessment" value from the AI response (via a configurable regex) and converts it into a standardized label of the form: ai:<prompt-stem>:<assessment> (lowercased, spaces preserved unless you modify regex or post processing).
4. Optionally posts the full AI response as a comment (unless a suppression marker is detected).
5. Removes the trigger label so the process is idempotent and can be retriggered manually by re‑adding it.

How It Works

High level flow:

1. Issue receives a trigger label (e.g. request ai review).
2. Action runs and gathers all issue labels.
3. Each label is checked against your labels_to_prompts_mapping list.
4. For each matched prompt file:
   • System prompt + model + max tokens are resolved (overrides from workflow inputs if provided).
   • Inference is executed with the issue body as user content.
   • Response is scanned:
     • Optional "no comment" directive → skip comment.
     • Assessment header line → derive label.
   • Summary written to the job summary.
5. All derived labels are added; trigger label is removed.

Features

• Multiple prompt files per issue (supports multifaceted assessments).
• Per prompt inference (each prompt gets its own AI run & resulting label).
• Override model / max tokens at workflow level or rely on prompt file.
• Configurable assessment extraction via regex.
• Configurable comment suppression via regex.
• Clear action summary with raw AI output + derived assessment.
• Works with any model accessible via the GitHub Models API endpoint you specify.

Prompt File Schema

Example .prompt.yml file:

messages:
  - role: system
    content: >+
      You are a world-class product manager that will help decide whether a particular bug report is completely filled out and able to start being worked on by a team member.
      1. Given a bug report analyze it for the following key elements: a clear description of the problem, steps to reproduce, expected versus actual behavior, and any relevant visual proof. 
      2. Rate each element provided in the report as `complete`, `incomplete`, or `unable to determine` except for Screenshots if included. Justify the rating by explaining what is missing or unclear in each element.
      3. The title of the response should be based on the overall completeness rating of all the provided elements. For example: "### AI Assessment: Ready for Review" if complete, "### AI Assessment: Missing Details" if incomplete, or "### AI Assessment: Unsure" if unable to determine.
      4. When determining the overall completeness rating do not include the Screenshots or relevant visual proof section. This section is more of a "nice to have" versus "hard requirement" and it should be ignored. 
  - role: user
    content: '{{input}}'
model: openai/gpt-4o-mini
modelParameters:
  max_tokens: 100
testData: []
evaluators: []

Required elements:

• messages: Must include at least one system and one user with {{input}} placeholder.
• model: A model identifier in {publisher}/{model_name} format compatible with GitHub Models.
• modelParameters.max_tokens (optional) used if workflow input max_tokens not provided.

Inputs

Various inputs are defined in action.yml:

Name	Description	Required	Default
token	Token for API calls (usually ${{ secrets.GITHUB_TOKEN }})	true	github.token
ai_review_label	Label that triggers AI processing	true
issue_number	Issue number	true
issue_body	Issue body to feed into AI	true
prompts_directory	Directory containing .prompt.yml files	true
labels_to_prompts_mapping	Mapping string label,prompt.yml|label2,prompt2.yml	true
model	Override model (falls back to prompt file)	false
endpoint	Inference endpoint	false	https://models.github.ai/inference
max_tokens	Override max tokens (else prompt file else 200)	false	200
repo_name	Repository name (auto detected)	false
owner	Repository owner (auto detected)	false
assessment_regex_pattern	Pattern to capture assessment line	false	^###.*[aA]ssessment:\s*(.+)$
assessment_regex_flags	Flags for assessment regex	false
no_comment_regex_pattern	Pattern to detect comment suppression	false
no_comment_regex_flags	Flags for suppress pattern	false

Label → Prompt Mapping

Provide a single string where pairs are separated by | and each pair is label,prompt-file-name. Example:

labels_to_prompts_mapping: 'bug,bug-review.prompt.yml|support request,request-intake.prompt.yml|security,security-assessment.prompt.yml'

Rules:

• Matching is case sensitive to how GitHub returns labels.
• A label may map to only one prompt file (first match used if duplicates are present).
• Multiple labels can each trigger their prompt; all selected prompts are processed.

Regex Customization

Default assessment extraction looks for any markdown header starting with ### and containing Assessment: (case insensitive if you supply i flag) and captures the remainder of the line:

^###.*[aA]ssessment:\s*(.+)$

Example variations:

• Want stricter title: ^### AI Assessment:\s*(.+)$
• Want to allow other synonyms: ^###.*(Assessment|Alignment):\s*(.+)$ (then handle capture group 2 in post processing—current implementation expects one capture group, so keep a single (.+)).

If your header is like:

## Result: Passed

You could set:

assessment_regex_pattern: '^## Result:\s*(.+)$'

Suppressing the Comment

Add an instruction in the system prompt to emit a marker when you only want labeling. Example system instruction snippet:

If the overall assessment is fully ready, append: <!-- no-comment -->

Then configure:

no_comment_regex_pattern: '<!--.*no.*comment.*-->'
no_comment_regex_flags: 'i'

When the pattern is found, the comment step is skipped; label + summary still generated.

Example Workflow Setup

Below is an example workflow file. It triggers whenever a label is added, checks for the trigger label, processes, then removes it.

name: AI Issue Assessment
on:
  issues:
    types: [labeled]
jobs:
  ai-assessment:
    if: github.event.label.name == 'request ai review'
    runs-on: ubuntu-latest
    permissions:
      issues: write
      models: read
      contents: read
    steps:
      - name: Checkout
        uses: actions/checkout@v4

      - name: Setup Node.js
        uses: actions/setup-node@v4

      - name: Run AI assessment
        id: ai-assessment
        uses: github/ai-assessment-comment-labeler@main
        with:
          token: ${{ secrets.GITHUB_TOKEN }}
          issue_number: ${{ github.event.issue.number }}
          issue_body: ${{ github.event.issue.body }}
          repo_name: ${{ github.event.repository.name }}
          owner: ${{ github.repository_owner }}
          ai_review_label: 'request ai review'
          prompts_directory: './Prompts'
          labels_to_prompts_mapping: 'bug,bug-review.prompt.yml|support request,request-intake.prompt.yml'

Multiple prompts example with custom overrides:

with:
  model: openai/gpt-4o-mini
  max_tokens: 300
  labels_to_prompts_mapping: 'bug,bug-review.prompt.yml|performance,perf-triage.prompt.yml|design,ux-assessment.prompt.yml'

Outputs / Labels Added

For each prompt file used (e.g. bug-review.prompt.yml), the assessment line text (after Assessment:) is:

1. Lowercased
2. Prefixed with ai:<prompt-stem>: where <prompt-stem> is the file name without extension and trailing -prompt parts preserved.

Examples:

• ### AI Assessment: Ready for Review → ai:bug-review:ready for review
• ### AI Assessment: Missing Details → ai:bug-review:missing details
• No header found → ai:bug-review:unsure

These labels let you filter, search, or automate additional workflows.

Required Permissions

Recommended minimal permissions block:

permissions:
  issues: write   # create comment & add/remove labels
  models: read    # access GitHub Models inference
  contents: read  # read prompt files

Troubleshooting

Symptom	Likely Cause	Fix
Action exits early: "Required inputs are not set"	Missing mandatory input	Ensure all required with: fields are present
No labels added	Assessment regex failed	Adjust assessment_regex_pattern / flags
Comment missing	Suppression regex matched	Remove or modify no_comment_regex_pattern
Fallback label unsure	No header matched regex	Update system prompt to ensure header form
Model error	Unsupported or misspelled model	Verify model exists in GitHub Models catalog
404 prompt file	Wrong prompts_directory path	Ensure path relative to repo root

Enable debug logs by setting in workflow:

env:
  ACTIONS_STEP_DEBUG: true

(Requires enabling debug logging in repository settings.)

Development

Local development steps:

# Install dependencies
bun install

# Lint
bun run lint

# Auto fix + build dist
bun run build

# Run locally (requires env vars if outside GitHub Actions)
GITHUB_TOKEN=ghp_xxx ISSUE_NUMBER=1 bun run src/index.ts

Key scripts (package.json): lint, lintFix, build. The action bundles to dist/index.js (Node 20 runtime).

Testing

Basic tests live under src/__tests__. Add additional parsing / utility tests as needed.

Releasing

• Update version tag or reference a commit SHA in downstream workflows.
• Optionally create a Git tag & release notes summarizing changes.

Contributing

See CONTRIBUTING.md & follow the code of conduct in CODE_OF_CONDUCT.md.

Security / Reporting Issues

For vulnerability disclosures follow SECURITY.md. Please do not open public issues for sensitive reports.

FAQ

Q: Can I run multiple prompts in one execution? Yes, any label in the mapping that matches the issue produces a separate inference & label.

Q: How do I force a re-run? Re-add the trigger label.

Q: Can I use a completely different header phrase? Yes, adjust assessment_regex_pattern to capture the desired line; the first capture group is used as the assessment value.

Q: Can I trim / normalize spaces? Current implementation lowercases assessment as is. You can post process by adding a follow up workflow reacting to labeled events.

Q: Will it modify existing AI labels? It only adds new labels; it does not remove prior ai: labels. Clean up logic can be added in a future enhancement.

License

See LICENSE.txt.