Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 80.74%. Comparing base (a847cfa) to head (076df9c).
⚠️ Report is 3 commits behind head on main.

@@            Coverage Diff             @@
##             main    #1947      +/-   ##
==========================================
- Coverage   80.82%   80.74%   -0.08%     
==========================================
  Files         117      127      +10     
  Lines       19095    19176      +81     
  Branches        0       12      +12     
==========================================
+ Hits        15433    15484      +51     
- Misses       3662     3689      +27     
- Partials        0        3       +3     

I love how this pr-walkthrough agent focuses on building the reviewer’s mental model. That output feels genuinely valuable.

One suggestion: thoughts on consider modeling it as a subagent of pr-review rather than as a new top-level peer agent. To me, the strongest value here is orientation before inspection: helping the reviewer understand the shape of the diff, triage important files, and surface design forks before pr-review produces findings.

Keeping it under pr-review would also make the product boundary clearer: pr-review remains the user-facing coordinator for review findings and merge readiness, while pr-walkthrough provides the narrative orientation artifact. It could also reuse the existing diff/CI/tracking pipeline instead of duplicating that setup.

If we go this route, I’d suggest marking the subagent with maturity: experimental in the collection manifest since this capability is still new and being validated.

With this said, I would like to invite @agreaves-ms and @WilliamBerryiii for other perspectives regarding this thought! Thank you!

Thanks for the feedback @jkim323. I tested this and want to share some context on this exact design tension I've been wrestling with.

I ran the walkthrough against 9 merged PRs and fed the output to a model acting as pr-review for focus-zone extraction. 9/9 high-confidence extractions, so the subagent model works mechanically. But there's a philosophical problem. This agent exists in its current form (i.e. 385 lines of attitude - professional, focused attitude, but still designed as a strong personality) because without the opinionated voice and strong point of view, the model immediately regresses to gluing English prose between diff hunks, using the hunks themselves as narrative scaffolding. IMO that kind of output isn't worth very much because it doesn't capture human attention, doesn't abstract by ideas, and doesn't structure around decisions. That style makes more sense if the agent is passing judgement (which pr review already does), less so if it needs to act as a lens for human attention. The personality itself seems to be what forces architectural thinking rather than line-by-line summarization.

Which means this thing embodies a fundamentally different philosophy than classical AI code review: PR Review scans the diff and formulates its own judgments, while this agent explicitly bans itself from judgment. Its job is to focus the human on what needs their judgment, not to replace it. It's for the person staring down a 45-file diff who needs orientation before they can effectively form their own opinions.

The problem with combining them sequentially is that the walkthrough says "here are the design forks, you decide" and then PR Review immediately says "this is wrong, fix it." In about 4 of 9 test cases the neutrality reads as theater once the verdict follows. They serve different audiences at different moments in the review lifecycle.

I considered three options: (1) standalone peer, different audiences, already interoperable via diff-state.json; (2) subagent of code-review-full, technically works but creates voice-whiplash in security/governance PRs; (3) dual registration, subagent AND standalone, users choose invocation path.

Based on @katriendg's review feedback, the branch now reflects option 1: standalone peer. The subagent registration and coding-standards collection entry have been removed. The agent stays in the hve-core collection only, marked maturity: experimental, with documentation at docs/agents/pr-walkthrough/. Pipeline interop via diff-state.json and the shared pr-reference skill remains wired for future orchestration if the team decides to revisit.

Please ensure you ran these checks:

AI Artifact Contributions

• Used /prompt-analyze to review contribution
• Addressed all feedback from prompt-builder review
• Verified contribution follows common standards and type-specific requirements

AI Artifact Contribution Checks

Re: @jkim323's checklist:

• ✅ Used /prompt-analyze\ to review contribution — Ran a full prompt-builder quality audit. Found 4 critical + 2 major issues.
• ✅ Addressed all feedback from \prompt-builder\ review — Fixed across 4 commits, each A/B tested against 5 PRs with independent scoring subagents:
  • Fix Add development tools configuration files #3: Coherence tension (opinions vs editorializing clarification) — avg delta -0.10
  • Fix Add repository foundation and documentation files #2: Bolded-prefix bullet removal — avg delta +0.37
  • Fix Repository Foundation & Documentation #1: ALL CAPS removal (replaced with bold/italic) — avg delta +0.20
  • Fix Development tools configuration files missing #4: Token budget (extracted voice to \walkthrough-voice.instructions.md) — validated by 10-PR A/B experiment (8.44 vs 8.43, tied)
• ✅ Verified contribution follows common standards — Agent description under 120 chars, asterisk bullets, standard section naming (## Required Steps), no run-together paragraphs.

Remaining ALL CAPS in the file are legitimate: \BAD:/\GOOD:\ (example labels), \WEAKEN/\KILL/\COUNTER\ (enum action labels), and git/code placeholders (\MERGE_BASE, \HEAD, \AUTHOR).

AI Artifact Contribution Checks

Re: @jkim323's checklist:

• ✅ Used /prompt-analyze\ to review contribution — Ran a full prompt-builder quality audit. Found 4 critical + 2 major issues.

• ✅ Addressed all feedback from \prompt-builder\ review — Fixed across 4 commits, each A/B tested against 5 PRs with independent scoring subagents:

  • Fix Add development tools configuration files #3: Coherence tension (opinions vs editorializing clarification) — avg delta -0.10
  • Fix Add repository foundation and documentation files #2: Bolded-prefix bullet removal — avg delta +0.37
  • Fix Repository Foundation & Documentation #1: ALL CAPS removal (replaced with bold/italic) — avg delta +0.20
  • Fix Development tools configuration files missing #4: Token budget (extracted voice to \walkthrough-voice.instructions.md) — validated by 10-PR A/B experiment (8.44 vs 8.43, tied)

• ✅ Verified contribution follows common standards — Agent description under 120 chars, asterisk bullets, standard section naming (## Required Steps), no run-together paragraphs.

Remaining ALL CAPS in the file are legitimate: \BAD:/\GOOD:\ (example labels), \WEAKEN/\KILL/\COUNTER\ (enum action labels), and git/code placeholders (\MERGE_BASE, \HEAD, \AUTHOR).

awesome thank you for doing that!!

Thanks for the feedback @jkim323. I tested this and want to share some context on this exact design tension I've been wrestling with.

I ran the walkthrough against 9 merged PRs and fed the output to a model acting as pr-review for focus-zone extraction. 9/9 high-confidence extractions, so the subagent model works mechanically. But there's a philosophical problem. This agent exists in its current form (i.e. 385 lines of attitude - professional, focused attitude, but still designed as a strong personality) because without the opinionated voice and strong point of view, the model immediately regresses to gluing English prose between diff hunks, using the hunks themselves as narrative scaffolding. IMO that kind of output isn't worth very much because it doesn't capture human attention, doesn't abstract by ideas, and doesn't structure around decisions. That style makes more sense if the agent is passing judgement (which pr review already does), less so if it needs to act as a lens for human attention. The personality itself seems to be what forces architectural thinking rather than line-by-line summarization.

Which means this thing embodies a fundamentally different philosophy than classical AI code review: PR Review scans the diff and formulates its own judgments, while this agent explicitly bans itself from judgment. Its job is to focus the human on what needs their judgment, not to replace it. It's for the person staring down a 45-file diff who needs orientation before they can effectively form their own opinions.

The problem with combining them sequentially is that the walkthrough says "here are the design forks, you decide" and then PR Review immediately says "this is wrong, fix it." In about 4 of 9 test cases the neutrality reads as theater once the verdict follows. They serve different audiences at different moments in the review lifecycle.

I considered three options: (1) standalone peer, different audiences, already interoperable via diff-state.json; (2) subagent of code-review-full, technically works but creates voice-whiplash in security/governance PRs; (3) dual registration, subagent AND standalone, users choose invocation path.

Based on @katriendg's review feedback, the branch now reflects option 1: standalone peer. The subagent registration and coding-standards collection entry have been removed. The agent stays in the hve-core collection only, marked maturity: experimental, with documentation at docs/agents/pr-walkthrough/. Pipeline interop via diff-state.json and the shared pr-reference skill remains wired for future orchestration if the team decides to revisit.

Thanks a lot for the reflections and move to standalone. I think it's the best choice.

There are a few remaining changes to be done, the main thing to call out here is we cannot leave the pipeline interop (other than pr-reference skill (which is part of the same collection). The interactions on diff-state.json are for Code Reviews, so that would be another collection and not bundled together. Also in that case we would want to update the Code Review agent itself to gain knowledge of this new agent.

My vote is keeping it separate and standalone for an experimental testing phase. So let's not have references to the pipeline which is not fully implemented anyways.

Agreed and done. Removed all diff-state.json references, orchestrated mode plumbing, and pipeline integration language. The agent is now fully standalone: computes its own diff via pr-reference skill, no cross-collection dependencies.

Also moved voice guidance back into the agent body per your inline comment about the applyTo pattern not working in plugin/extension contexts. Agent is self-contained and portable now.

All 8 inline comments addressed and resolved. CI is green.