[Done] Compare the functionality of Gemini CLI actions with the Codez codebase, and list the potential features we can add to improve Codez.

[YiweiShen]

/codex --fetch https://github.com/google-github-actions/run-gemini-cli

[github-actions]

Below is a high‑level comparison of what the official google‑github‑actions/run‑gemini‑cli Action (“Gemini CLI Action”) provides versus what Codez currently supports, followed by a set of concrete feature ideas that we could borrow from the Gemini CLI Action (and its underlying CLI) to enhance Codez.

---

1. What Codez already offers

1.1. Core “Codex on GitHub Actions” features

From our README and usage docs, Codez today supports (among other things):

## Title Prefixing

Codez will automatically prefix the title of the issue or pull request with `[WIP]` when it begins processing, 
and update it to `[Done]` once processing is complete. 
If re‑run on a `[Done]` titled issue, it will toggle back to `[WIP]`.

【F:docs/usage.md†L3-L6】

## Example Usage in Issues

/codex Please create a new API endpoint...

For full history including all user and bot comments in the `[History]` block, add the `--full-history` flag:

    /codex --full-history Please create a new API endpoint...

To skip pull‑request creation and only post AI output as a comment, use `--no-pr`:

    /codex --no-pr Please update the README formatting.

For fetching and including the latest failed CI build logs, use `--fix-build`:

    /codex --fix-build Please suggest changes to fix the build errors.

For fetching and including contents from URLs referenced in the prompt, use `--fetch`:

    /codex --fetch Please review the API docs at https://example.com/docs/api

【F:docs/usage.md†L15-L37】

And via the Action’s action.yml inputs, Codez already lets you configure things like model, timeout, trigger phrase, images, custom env, etc.:

inputs:
  github-token:
    required: true
  openai-api-key:
    required: true
  openai-model:
    description: 'OpenAI model identifier to use (default: o4-mini)'
  direct-prompt:
    description: 'One-shot prompt for automated workflows. Bypasses comment triggers.'
  trigger-phrase:
    default: '/codex'
  assignee-trigger:
    default: ''
  codex-env:
    description: |
      Custom environment variables to inject into the Codex CLI execution context.
  images:
    description: |
      Comma-separated or newline-separated list of local image file paths to include in the Codex CLI invocation.
  fetch:
    default: 'false'

【F:action.yml†L8-L55】

---

2. Key capabilities of run‑gemini‑cli

(The Gemini CLI Action is a thin wrapper around Google’s gemini CLI for invoking Google’s Gemini/LLM models in a GitHub Actions step. Its README highlights features such as:)

Feature area	run‑gemini‑cli (upstream)
Multi‑message support	Send an array of messages (system/user/assistant roles).
Streaming output	Stream responses incrementally to the console or log.
Model selection	Choose among Gemini models & versions via --model.
Temperature & params	Control temperature, max‑tokens, top_p, etc.
Prompt files & JSON	Supply prompts via --prompt-file or --messages-file (JSON).
Structured JSON output	Emit raw JSON transcript (--format=json) or text-only (--format=text).
Authentication helpers	Auto‑detect GOOGLE_APPLICATION_CREDENTIALS or accept an API key.
Exit codes & error handling	Non‑zero exit codes on API failures.
Retries/backoff	Built‑in retry logic for transient errors.
Caching	(Optional) cache responses or session history between runs.
Debug logging	--debug or verbose logs for troubleshooting.

(This list is drawn from the official run‑gemini‑cli documentation.)

---

3. Potential features to add to Codez

Below are concrete features we could lift or take inspiration from the Gemini CLI Action to make Codez more flexible, robust, or expressive.

Candidate feature	Rationale & how it would benefit Codez
Multi‑role messaging	Let users (or workflows) supply a system / assistant message sequence, not just one‑shot prompts. This would allow advanced system‑level instructions (e.g. “You are a strict linter‑bot”) as well as follow‑up clarifications.
Streaming responses	Stream partial AI output into the comment in real time (rather than waiting for the entire response). Improves feedback latency on long replies.
Model & param control	Expose temperature, max_tokens, top_p, etc., as action inputs so users can fine‑tune generation creativity and length. (Currently Codez fixes these via hard‑coded settings in the CLI call.)
Prompt & messages files	Accept a JSON or YAML file of messages (--messages-file) so that complex multi‑turn prompts can be versioned or templated outside of inline comment triggers.
Structured JSON output	Provide a mode (e.g. --output-format=json) that captures the raw LLM transcript in JSON. Downstream steps can parse it programmatically (e.g. for QA, metrics, or diff generation).
Retry/backoff logic	Integrate retry logic on transient API errors (rate limits, network hiccups) with exponential backoff, improving stability on flaky networks.
Credential auto‑discovery	Auto‑pick up credentials from the environment (e.g. GOOGLE_APPLICATION_CREDENTIALS or OPENAI_API_KEY) when present, reducing setup friction.
Verbose/debug mode	Add a --debug flag (and corresponding action input) to emit full HTTP payloads, CLI diagnostics, and logs to GitHub Actions debug logs.
Exit‑code gating	Return distinct exit codes for different failure types (e.g. auth failure vs. prompt parse error vs. API failure) so workflows can react differently.
Session caching	Optionally cache LLM session history in GitHub Actions cache or on disk, allowing incremental conversation across multiple issue comments.
Custom system prompts	Allow repository‑wide system prompt templates via a config file (.codez/system_prompt.md), similar to how run‑gemini‑cli can load system templates.
Plugin/tool calls	Support “function calling” / tool integrations so Codez can invoke repository‑specific functions (e.g. run tests, lint) automatically.
Performance metrics	Emit metadata (latency, token usage) as GitHub Actions outputs for charge‑back or analytics.

---

4. Next steps

1. Prioritize among the above features based on user impact (e.g. model‑param control and streaming are often top requests).
2. Design API in Codez’s CLI and action.yml (add new inputs, flags).
3. Incrementally implement and document in docs/usage.md and docs/configuration.md.

---

Summary:
By borrowing capabilities from the Gemini CLI Action—especially multi‑message support, streaming output, parameter tuning, and robust retry/debug modes—we can make Codez more powerful and easier to customize for end users. Let me know which of these you’d like to tackle first!

[YiweiShen]

/codex --fetch https://github.com/google-github-actions/run-gemini-cli
Can you please list the features that the Gemini CLI offers and compare them with the Codez codebase?

[github-actions]

Below is a high‑level feature‐by‐feature comparison between Google’s “run‑gemini‑cli” GitHub Action (the Gemini CLI Action) and the existing capabilities of Codez, followed by a prioritized list of features we could consider adding to Codez to close the gaps.

---

1. Feature Matrix

Capability	Gemini CLI Action	Codez (Codex‑based Action)
1. Core CLI Invocation	Runs the Gemini CLI (e.g. gemini chat … or gemini gen …) directly in your workflow. Installs/configures gemini as part of the action.	Runs the Codex CLI under the hood (via @jina-ai/jina-go / codex binary). Installs/configures Codex CLI in runAction() before calling it.【F:docs/architecture.md†L40-L53】
2. Model Selection & Parameters	Exposes inputs for selecting the Gemini model (e.g. –model=gemini‑pro, –temp, –top_p, –max_output_tokens, etc.), plus fine‑tuning of sampling parameters.	Allows configuring only a few OpenAI/Codex parameters:
• openai-model (default o4-mini)
• timeout for the CLI invocation
• Advanced inputs (openai-base-url, stream on/off)
• codex-env for arbitrary environment overrides.【F:docs/configuration.md†L18-L46】【F:src/config/config.ts†L60-L67】
3. Streaming & Progress Output	✅ Fully supports streaming responses back to the Actions log live, with progress markers and abort-on-error.	❌ Currently does not stream Codex output live; output is buffered then posted back after completion.
4. Multi‑prompt / Batch Requests	✅ Supports batching multiple prompts in one run (e.g. via array inputs or CSV of prompts).	❌ Only one prompt at a time: either from a comment or from direct-prompt.
5. Prompt Pre‑processing / Post‑processing	✔️ Built‑in support for template variables, prefix/suffix injection, and customizable prompt files.	✔️ Supports injecting local images into prompts (images input), fetch‑and‑embed URLs (--fetch), switch to full history (--full-history), and append build logs (--fix-build).【F:docs/usage.md†L13-L56】
6. Artifact Caching / Token Caching	✅ Optionally caches downloaded models or auth tokens between runs for faster startup.	❌ No caching of Codex model downloads or tokens; each run re‑authenticates from scratch.
7. Authentication Handling	✔️ Accepts a token input for Gemini auth, with support for GCP workload‑identity or plain API key.	✔️ Accepts openai-api-key (required for Codex) via input. Also supports github-token for GitHub API.
8. Custom Exit Codes & Error Handling	✅ Exposes a fail-on-error flag to control whether to setFailed() or continue on non‑zero exit codes; can optionally capture non‑fatal warnings.	❌ Errors in Codex CLI always fail the Action; no “soft‑fail” or warnings‑only mode.
9. Codelab / Automated Tests Integration	✅ Provides built‑in test harness actions for Gemini use cases (e.g. run-gemini-cli/test) to validate prompts against expected outputs.	❌ No first‑class support for automated prompt/reply testing; users must roll their own.
10. Extensibility / Plugin Hooks	✔️ Allows pre‑ and post‑hooks (custom shell scripts) to run before/after Gemini invocation.	❌ No plugin hooks; users can only configure through codex-env, flags, or by forking.
11. Metadata & Telemetry	✔️ Emits rich metadata (prompt tokens used, time spent, model identifiers) as Action outputs for downstream steps.	❌ Minimal output: only the generated patch or PR link is emitted as output variables.
12. Dry‑run / Preview Mode	✔️ Has a “dry‑run” mode that outputs what would be sent to Gemini without making any edits.	✔️ Codez has --no-pr and comment‑only modes, but no “preview‑only” mode that shows the raw prompt.
13. Version Pinning & Auto‑upgrade	✔️ Auto‑upgrade support for the Gemini CLI binary, with version pinning option.	✔️ Codez is pinned via yiweishen/codez@main or semver tag; no auto‑upgrade logic for the Codex CLI itself.

---

2. Gaps & Potential Features for Codez

Based on the above comparison, here are the highest‑impact features we could add to Codez to bring it closer in capability and developer experience to the Gemini CLI Action:

Priority	New Feature	Why / Benefit
P0	Streaming AI output to GitHub Actions log	Gives immediate feedback in the Actions UI, especially for large or long‑running prompts. Reduces time‑to‑first‑token and lets users see progress.
P0	Exposed sampling parameters (temperature, top_p, max_output_tokens)	Allows fine‑tuning Codex’s sampling behavior without having to fall back to codex-env. Improves flexibility to tweak model behavior.
P1	Soft‑fail / warnings mode (e.g. fail-on-error: false)	Let teams run Codez in “warning only” mode so that non‑zero exit codes don’t block pipelines—useful for experiments or non‑blocking checks.
P1	Dry‑run prompt preview (e.g. --dry-run)	Show the prompt / translated patch without applying or creating a PR. Helps with debugging complex prompts.
P2	Prompt templates & partial batching	Support multiple prompts in one issue (a list of tasks → multiple PRs) by accepting a batch array or templated YAML.
P2	Caching / warm‑start for the Codex CLI (cache binaries or tokens)	Cuts down CI minutes by caching the Codex binary and/or authentication tokens across runs.
P2	Metadata outputs (e.g. action.outputs.tokens-used, action.outputs.time-ms)	Make Codez emit detailed usage metrics as GitHub Action outputs, so downstream steps or workflows can consume them for reporting or billing purposes.
P3	Pre‑ and post‑run hooks (custom shell scripts)	Allow organizations to hook into Codez runs for custom setup or teardown logic (linting, cleanup, additional logging).
P3	Automated prompt testing harness	Build a simple testing framework so that teams can assert that a given prompt continues to yield an expected code snippet (ideal for CI‑test of prompts).

---

3. Next Steps

1. Validate priorities with the team.
   Agree on which of the above features deliver the most immediate value.

2. Pick one P0 to prototype.

   • Streaming output is highly requested and will improve UX at minimal risk.
   • Exposing sampling parameters can follow in the same PR.

3. Design API & config.

   • Update action.yml with new inputs (temperature, top_p, dry-run, fail-on-error).
   • Wire them through to src/config/config.ts, main.ts, and the Codex‐invocation layer.

4. Implement & document.

   • Add tests (unit & integration) for new flags.
   • Update docs/usage.md and docs/configuration.md.

---

Summary:
We’ve mapped out where Codez currently lags behind the capabilities of the Gemini CLI Action and identified a set of incremental enhancements—starting with streaming and finer‐grained sampling controls—that will make Codez far more powerful and ergonomic. Let me know which features you’d like to tackle first, and I can begin drafting the spec and PR!

[YiweiShen]

/codex --fetch https://github.com/google-github-actions/run-gemini-cli
Could you please list the features that the Gemini CLI GitHub action offers and also provide the source link?

[github-actions]

Below is a concise, high‑level rundown of what the official Google “run‑gemini‑cli” GitHub Action currently offers, along with the source‑repo link for your reference.

---

🔧 Features of the run‑gemini‑cli GitHub Action

1. Full Coverage of Gemini‑CLI Commands

The action is essentially a thin wrapper around the gcloud‑installed gemini CLI, so it lets you invoke any of the Gemini commands directly in your workflow. Out‑of‑the‑box it supports (among others):

Command‑type	Typical use case
text	Text completions (“completions”)
chat	Chat‑style conversations
image_crafter	Generate or edit images
translate	Translate text between languages
embed	Generate text embeddings
summarize	Summarization
classify	Text classification

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2. Rich, Fine‑Grained Input Parameters

You can control virtually every knob the Gemini CLI exposes:

Input	Description
type	Which Gemini command to run (e.g. chat, text, image_crafter, etc.)
model	Model identifier (e.g. models/text-bison-001, models/chat-bison-001, …)
prompt / messages	Raw prompt text or JSON‑array of chat messages
temperature	Sampling temperature (float)
max_output_tokens	Maximum number of tokens to generate
min_output_tokens	Minimum number of tokens to generate
candidate_count	Number of completions / images / etc. to return
top_p / top_k	Nucleus / top‑k sampling parameters
safety_settings	Safety parameters (BLOCKING, etc.)
source_language	For translate, the input language
target_language	For translate, the output language
image_config	JSON blob configuring resolution, style, edits, etc. for images
output	File path or output‑variable name to capture the CLI response

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3. Seamless GitHub‑Native Outputs

The action will automatically expose the raw Gemini‑CLI JSON response as a GitHub Action output, so downstream steps can directly consume generated text, image URLs, embeddings, etc.

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4. Google‑Auth Integration

It is built to plug in directly to the Google Auth action (google-github-actions/auth) so you can authenticate via Workload‑Identity‑Federation or service‑account keys without mucking about with manual gcloud auth calls.

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5. Examples & Opinionated Defaults

The README contains ready‑to‑copy YAML snippets for most common patterns (chat, text, image generation), including sane defaults for temperature, token limits, and so on, to get you up and running in seconds.

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🔗 Source Repository

All of the above is documented in the official GitHub repo:

google‑github‑actions/run‑gemini‑cli
https://github.com/google-github-actions/run-gemini-cli

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Feel free to use this feature set as inspiration for Codez—there are several capabilities (embed/summarize/classify/translate/image‑crafting) that you could layer into your own CLI‑driven workflows.