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docs: move auto-categorizing to step 1 and update prompt to set type field

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Coco Sheng
2026-05-06 16:25:15 -04:00
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scripts/backlog-analysis/README.md
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@@ -36,7 +36,22 @@ python3 fetch_from_url.py "https://github.com/google-gemini/gemini-cli/issues/?q
## 🚀 Workflows ## 🚀 Workflows
### 1. Initial Triage (Static) ### 1. Auto-Categorizing Issues with Gemini CLI
If you have a list of uncategorized issues fetched from GitHub, your first step
should be to classify them. You can use the Gemini CLI directly in your terminal
to label them.
**Example command:**
```bash
gemini "Read data/uncategorized.json. For each issue, determine if it is a bug or a feature request. Then, use the gh CLI tool to add either the 'type/bug' or 'type/feature' label to the issue on GitHub, AND update the JSON object in the file to include a 'type' field with the chosen value."
```
_Note: Make sure your `gemini-cli` has permission to execute shell commands if
you want it to apply the labels automatically via `gh`._
### 2. Initial Triage (Static)
Use this for a quick, first-pass estimation. Use this for a quick, first-pass estimation.
@@ -44,7 +59,7 @@ Use this for a quick, first-pass estimation.
python3 analyze_bugs.py --api-key "YOUR_KEY" python3 analyze_bugs.py --api-key "YOUR_KEY"
``` ```
### 2. Deep Agentic Analysis ### 3. Deep Agentic Analysis
Uses Gemini as an agent with access to the codebase. Uses Gemini as an agent with access to the codebase.
@@ -52,7 +67,7 @@ Uses Gemini as an agent with access to the codebase.
python3 bug_analyzer_final.py --api-key "YOUR_KEY" python3 bug_analyzer_final.py --api-key "YOUR_KEY"
``` ```
### 3. Iterative Analysis ### 4. Iterative Analysis
Runs the single-turn analyzer in a loop until all issues have a valid analysis. Runs the single-turn analyzer in a loop until all issues have a valid analysis.
@@ -60,7 +75,7 @@ Runs the single-turn analyzer in a loop until all issues have a valid analysis.
GEMINI_API_KEY="YOUR_KEY" ./loop_analyzer.sh GEMINI_API_KEY="YOUR_KEY" ./loop_analyzer.sh
``` ```
### 4. Validation & Export ### 5. Validation & Export
Run validation from the utils folder to ensure consistency, then generate a Run validation from the utils folder to ensure consistency, then generate a
readable report. readable report.
@@ -70,7 +85,7 @@ python3 utils/validate_effort.py
python3 generate_bugs_csv.py python3 generate_bugs_csv.py
``` ```
### 5. Generic Issue Processing ### 6. Generic Issue Processing
For any other backlog task (e.g., categorizing features, updating labels, or For any other backlog task (e.g., categorizing features, updating labels, or
custom analysis), use the `generic_processor.py`. This script allows you to custom analysis), use the `generic_processor.py`. This script allows you to
@@ -85,21 +100,6 @@ python3 generic_processor.py \
--prompt "Analyze these features and suggest which package they belong in. Output JSON: {\"package\": \"name\"}" --prompt "Analyze these features and suggest which package they belong in. Output JSON: {\"package\": \"name\"}"
``` ```
### 6. Auto-Categorizing Issues with Gemini CLI
If you have a list of uncategorized issues (e.g., lacking `type/bug` or
`type/feature`), you can use the Gemini CLI itself directly in your terminal to
classify and label them.
**Example command:**
```bash
gemini "Read data/uncategorized.json. For each issue, determine if it is a bug or a feature request. Then, use the gh CLI tool to add either the 'type/bug' or 'type/feature' label to the issue on GitHub."
```
_Note: Make sure your `gemini-cli` has permission to execute shell commands if
you want it to apply the labels automatically via `gh`._
## 🧠 Effort Level Criteria ## 🧠 Effort Level Criteria
Ratings are based on technical complexity and reproduction difficulty: Ratings are based on technical complexity and reproduction difficulty: