New Module: ai-lifecycle — AI/ML project lifecycle agentic team - #17
New Module: ai-lifecycle — AI/ML project lifecycle agentic team#17avielbl wants to merge 1 commit into
Conversation
WalkthroughA new community module registry entry for ChangesCommunity Module Registry Entry
Estimated code review effort🎯 1 (Trivial) | ⏱️ ~2 minutes Poem
🚥 Pre-merge checks | ✅ 5✅ Passed checks (5 passed)
✏️ Tip: You can configure your own custom pre-merge checks in the settings. ✨ Finishing Touches🧪 Generate unit tests (beta)
Tip 💬 Introducing Slack Agent: The best way for teams to turn conversations into code.Slack Agent is built on CodeRabbit's deep understanding of your code, so your team can collaborate across the entire SDLC without losing context.
Built for teams:
One agent for your entire SDLC. Right inside Slack. Thanks for using CodeRabbit! It's free for OSS, and your support helps us grow. If you like it, consider giving us a shout-out. Review rate limit: 0/1 reviews remaining, refill in 60 minutes.Comment |
![coderabbitai[bot]](https://avatars.githubusercontent.com/in/347564?s=60&v=4){kind=small}
There was a problem hiding this comment.
Actionable comments posted: 1
🤖 Prompt for all review comments with AI agents
Verify each finding against the current code and only fix it if needed.
Inline comments:
In `@registry/community/ai-lifecycle.yaml`:
- Around line 22-27: This entry mixes unverified status with filled approval
metadata: update the fields so they are consistent—either set trust_tier to a
reviewed value (e.g., community-reviewed or bmad-certified) and replace reviewer
with the reviewer’s GitHub handle while keeping approved_tag, approved_sha, and
approved_date, or clear approved_tag, approved_sha, approved_date and set
reviewer to an empty string (or remove it) while leaving trust_tier: unverified;
adjust the trust_tier, reviewer, and approved_* fields accordingly to remove the
semantic contradiction.
🪄 Autofix (Beta)
Fix all unresolved CodeRabbit comments on this PR:
- Push a commit to this branch (recommended)
- Create a new PR with the fixes
ℹ️ Review info
⚙️ Run configuration
Configuration used: Organization UI
Review profile: CHILL
Plan: Pro
Run ID: 8a592410-0fe6-4884-866b-56d2bb63ad82
📒 Files selected for processing (1)
registry/community/ai-lifecycle.yaml
| version: "4.0.0" | ||
| approved_tag: v4.0.0 | ||
| approved_sha: 6c992499636c0a53b4f4ae6f30ab8cfbd17ea9cc | ||
| trust_tier: unverified | ||
| approved_date: "2026-05-04" | ||
| reviewer: pending |
There was a problem hiding this comment.
🧩 Analysis chain
🏁 Script executed:
#!/bin/bash
set -euo pipefail
echo "== reviewer schema =="
rg -n "reviewer:" registry/registry-schema.yaml
echo "== trust_tier schema =="
rg -n "trust_tier:" registry/registry-schema.yaml
echo "== community module approval fields section =="
rg -n "approved_tag|approved_sha|approved_date" registry/registry-schema.yaml
echo "== dump this registry entry =="
python - <<'PY'
import sys, yaml, pathlib
p = pathlib.Path("registry/community/ai-lifecycle.yaml")
print(p)
print(p.read_text())
PY
echo "== optional: run any registry validation script if present =="
# Best-effort: detect a likely validator command
ls -la .
ls -la registry || true
ls -la .github || true
# If you have a script/Make target, run it here (adjust name if needed).Repository: bmad-code-org/bmad-plugins-marketplace
Length of output: 2932
🏁 Script executed:
cat -n registry/registry-schema.yamlRepository: bmad-code-org/bmad-plugins-marketplace
Length of output: 3512
🏁 Script executed:
ls -la .github/scripts/
fd -t f validator registryRepository: bmad-code-org/bmad-plugins-marketplace
Length of output: 260
Semantic contradiction: trust_tier: unverified with populated approved_* fields and reviewer: pending.
The registry schema comments (lines 80-84 of registry-schema.yaml) explicitly document that reviewer should be a "GitHub handle of reviewer" and that approved_tag, approved_sha, approved_date are required for community modules. However, this entry uses reviewer: pending (not a GitHub handle) and populates all approved_* fields while simultaneously setting trust_tier: unverified. This contradicts the documented intent that approval fields should only be populated for verified/reviewed entries.
Either set trust_tier to an appropriate value (e.g., community-reviewed or bmad-certified) if the module is approved, or leave the approved_* fields and reviewer empty/blanked and set trust_tier: unverified to indicate pending review. Do not mix unverified status with populated approval metadata.
🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed.
In `@registry/community/ai-lifecycle.yaml` around lines 22 - 27, This entry mixes
unverified status with filled approval metadata: update the fields so they are
consistent—either set trust_tier to a reviewed value (e.g., community-reviewed
or bmad-certified) and replace reviewer with the reviewer’s GitHub handle while
keeping approved_tag, approved_sha, and approved_date, or clear approved_tag,
approved_sha, approved_date and set reviewer to an empty string (or remove it)
while leaving trust_tier: unverified; adjust the trust_tier, reviewer, and
approved_* fields accordingly to remove the semantic contradiction.
|
The CodeRabbit finding is a false positive. The CONTRIBUTING.md submission template explicitly shows this exact pattern — |
|
@avielbl thank you - team is reviewing! |
|
@avielbl I spot checked a few things and there are missing file references that various skills are told to run - you will want to probably scan with claude or similar for references across skills that are referencing something that does not existing within the skill |
|
Good catch — thanks. Scanned every
Pushed as avielbl/ai-lifecycle@1425015. Test suite (20 tests) still passes. Please re-pull when you get a chance and let me know if anything else looks off. |
|
@bmadcode, have you had the chance to review my latest corrections per your comments? |
Hi, I've addressed the raised issues regarding file references. Thanks, |
|
Hi @bmadcode , any updates on this PR? |
2 participants
New Module Submission
Module: ai-lifecycle
Repository: https://github.com/avielbl/ai-lifecycle
Version: v4.0.0
Author: Aviel
License: MIT
What it does
A structured agentic team of 5 specialist agents that guide an AI/ML project from raw domain research through production inference:
Works across deep learning, gradient boosting (XGBoost/LightGBM), transformers, fine-tuning, and classical ML.
Checklist
bmad-manifest.jsonper agent,SKILL.mdfrontmatter,module.yaml)validate_skills.ymlon PRs).claude-plugin/marketplace.jsonv4.0.0(6c992499636c0a53b4f4ae6f30ab8cfbd17ea9cc)ai-and-machine-learning/mlopsSummary by CodeRabbit