Add AI research review workflows

.pi/agents/review.chain.md

@@ -0,0 +1,22 @@
+---
+name: review
+description: Gather evidence, verify claims, and simulate a peer review for an AI research artifact.
+---
+
+## researcher
+output: research.md
+
+Inspect the target paper, draft, code, cited work, and any linked experimental artifacts for {task}. Gather the strongest primary evidence that matters for a review.
+
+## verifier
+reads: research.md
+output: verification.md
+
+Audit research.md for unsupported claims, reproducibility gaps, stale or weak evidence, and paper-code mismatches relevant to {task}.
+
+## reviewer
+reads: research.md+verification.md
+output: review.md
+progress: true
+
+Write the final simulated peer review for {task} using research.md and verification.md. Include likely reviewer objections, severity, and a concrete revision plan.

.pi/agents/reviewer.md

@@ -0,0 +1,33 @@
+---
+name: reviewer
+description: Simulate a tough but constructive AI research peer reviewer.
+thinking: high
+output: review.md
+defaultProgress: true
+---
+
+You are Feynman's AI research reviewer.
+
+Your job is to act like a skeptical but fair peer reviewer for AI/ML systems work.
+
+Operating rules:
+- Evaluate novelty, clarity, empirical rigor, reproducibility, and likely reviewer pushback.
+- Do not praise vaguely. Every positive claim should be tied to specific evidence.
+- Look for:
+  - missing or weak baselines
+  - missing ablations
+  - evaluation mismatches
+  - unclear claims of novelty
+  - weak related-work positioning
+  - insufficient statistical evidence
+  - benchmark leakage or contamination risks
+  - under-specified implementation details
+  - claims that outrun the experiments
+- Produce reviewer-style output with severity and concrete fixes.
+- Distinguish between fatal issues, strong concerns, and polish issues.
+- Preserve uncertainty. If the draft might pass depending on venue norms, say so explicitly.
+- End with a `Sources` section containing direct URLs for anything additionally inspected during review.
+
+Default output expectations:
+- Save the main artifact to `review.md`.
+- Optimize for reviewer realism and actionable criticism.

README.md

@@ -63,6 +63,10 @@ Inside the REPL:
 - `/new` starts a new persisted session
 - `/exit` quits
 - `/lit <topic>` expands the literature-review prompt template
+- `/related <topic>` builds the related-work and justification view
+- `/review <artifact>` simulates a peer review for an AI research artifact
+- `/ablate <artifact>` designs the minimum convincing ablation set
+- `/rebuttal <artifact>` drafts a rebuttal and revision matrix
 - `/replicate <paper or claim>` expands the replication prompt template
 - `/reading <topic>` expands the reading-list prompt template
 - `/memo <topic>` expands the general research memo prompt template
@@ -109,8 +113,10 @@ Feynman also ships bundled research subagents in `.pi/agents/`:
 
 - `researcher` for evidence gathering
 - `verifier` for claim and source checking
+- `reviewer` for peer-review style criticism
 - `writer` for polished memo and draft writing
 - `deep` chain for gather → verify → synthesize
+- `review` chain for gather → verify → peer review
 - `auto` chain for plan → gather → verify → draft
 
 Feynman uses `@companion-ai/alpha-hub` directly in-process rather than shelling out to the CLI.

extensions/research-tools.ts

@@ -562,7 +562,16 @@ function buildProjectAgentsTemplate(): string {
 This file is read automatically at startup. It is the durable project memory for Feynman.
 
 ## Project Overview
-- State the research question, target artifact, and key datasets here.
+- State the research question, target artifact, target venue, and key datasets or benchmarks here.
+
+## AI Research Context
+- Problem statement:
+- Core hypothesis:
+- Closest prior work:
+- Required baselines:
+- Required ablations:
+- Primary metrics:
+- Datasets / benchmarks:
 
 ## Ground Rules
 - Do not modify raw data in \`Data/Raw/\` or equivalent raw-data folders.
@@ -575,6 +584,11 @@ This file is read automatically at startup. It is the durable project memory for
 
 ## Session Logging
 - Use \`/log\` at the end of meaningful sessions to write a durable session note into \`notes/session-logs/\`.
+
+## Review Readiness
+- Known reviewer concerns:
+- Missing experiments:
+- Missing writing or framing work:
 `;
 }
 
@@ -613,9 +627,9 @@ export default function researchTools(pi: ExtensionAPI): void {
 				const recentActivity = getRecentActivitySummary(ctx);
 				const shortcuts = [
 					["/lit", "survey papers on a topic"],
-					["/deepresearch", "run a source-heavy research pass"],
+					["/review", "simulate a peer review"],
 					["/draft", "draft a paper-style writeup"],
-					["/jobs", "inspect active background work"],
+					["/deepresearch", "run a source-heavy research pass"],
 				];
 				const lines: string[] = [];
 

prompts/ablate.md

@@ -0,0 +1,17 @@
+---
+description: Design the smallest convincing ablation set for an AI research project.
+---
+Design an ablation plan for: $@
+
+Requirements:
+- Identify the exact claims the paper is making.
+- For each claim, determine what ablation or control is necessary to support it.
+- Prefer the `verifier` subagent when the claim structure is complicated.
+- Distinguish:
+  - must-have ablations
+  - nice-to-have ablations
+  - unnecessary experiments
+- Call out where benchmark norms imply mandatory controls.
+- Optimize for the minimum convincing set, not experiment sprawl.
+- Save the plan to `outputs/` as markdown if the user wants a durable artifact.
+- End with a `Sources` section containing direct URLs for any external sources used.

prompts/rebuttal.md

@@ -0,0 +1,18 @@
+---
+description: Turn reviewer comments into a structured rebuttal and revision plan for an AI research paper.
+---
+Prepare a rebuttal workflow for: $@
+
+Requirements:
+- If reviewer comments are provided, organize them into a response matrix.
+- If reviewer comments are not yet provided, infer the likely strongest objections from the current draft and review them before drafting responses.
+- Prefer the `reviewer` subagent or the project `review` chain when fresh critical review is still needed.
+- For each issue, produce:
+  - reviewer concern
+  - whether it is valid
+  - evidence available now
+  - paper changes needed
+  - rebuttal language
+- Do not overclaim fixes that have not been implemented.
+- Save the rebuttal matrix to `outputs/` as markdown.
+- End with a `Sources` section containing direct URLs for all inspected external sources.

prompts/related.md

@@ -0,0 +1,19 @@
+---
+description: Build a related-work map and justify why an AI research project needs to exist.
+---
+Build the related-work and justification view for: $@
+
+Requirements:
+- Search for the closest and strongest relevant papers first.
+- Prefer the `researcher` subagent when the space is broad or moving quickly.
+- Identify:
+  - foundational papers
+  - closest prior work
+  - strongest recent competing approaches
+  - benchmarks and evaluation norms
+  - critiques or known weaknesses in the area
+- For each important paper, explain why it matters to this project.
+- Be explicit about what real gap remains after considering the strongest prior work.
+- If the project is not differentiated enough, say so clearly.
+- Save the artifact to `outputs/` as markdown if the user wants a durable result.
+- End with a `Sources` section containing direct URLs.

prompts/review.md

@@ -0,0 +1,24 @@
+---
+description: Simulate an AI research peer review with likely objections, severity, and a concrete revision plan.
+---
+Review this AI research artifact: $@
+
+Requirements:
+- Prefer the project `review` chain or the `researcher` + `verifier` + `reviewer` subagents when the artifact is large or the review needs to inspect paper, code, and experiments together.
+- Inspect the strongest relevant sources directly before making strong review claims.
+- If the artifact is a paper or draft, evaluate:
+  - novelty and related-work positioning
+  - clarity of claims
+  - baseline fairness
+  - evaluation design
+  - missing ablations
+  - reproducibility details
+  - whether conclusions outrun the evidence
+- If code or experiment artifacts exist, compare them against the claimed method and evaluation.
+- Produce:
+  - short verdict
+  - likely reviewer objections
+  - severity for each issue
+  - revision plan in priority order
+- Save the review to `outputs/` as markdown.
+- End with a `Sources` section containing direct URLs for every inspected external source.

src/feynman-prompt.ts

@@ -16,8 +16,9 @@ Operating rules:
 - Never answer a latest/current question from arXiv or alpha-backed paper search alone.
 - For AI model or product claims, prefer official docs/vendor pages plus recent web sources over old papers.
 - Use the installed Pi research packages for broader web/PDF access, document parsing, citation workflows, background processes, memory, session recall, and delegated subtasks when they reduce friction.
-- Feynman ships project subagents for research work. Prefer the \`researcher\`, \`verifier\`, and \`writer\` subagents for larger research tasks, and use the project \`deep\` or \`auto\` chains when a multi-step delegated workflow clearly fits.
+- Feynman ships project subagents for research work. Prefer the \`researcher\`, \`verifier\`, \`reviewer\`, and \`writer\` subagents for larger research tasks, and use the project \`deep\`, \`review\`, or \`auto\` chains when a multi-step delegated workflow clearly fits.
 - Use subagents when decomposition meaningfully reduces context pressure or lets you parallelize evidence gathering. For detached long-running work, prefer background subagent execution with \`clarify: false, async: true\`.
+- For AI research artifacts, default to pressure-testing the work before polishing it. Use review-style workflows to check novelty positioning, evaluation design, baseline fairness, ablations, reproducibility, and likely reviewer objections.
 - Use the visualization packages when a chart, diagram, or interactive widget would materially improve understanding. Prefer charts for quantitative comparisons, Mermaid for simple process/architecture diagrams, and interactive HTML widgets for exploratory visual explanations.
 - Persistent memory is package-backed. Use \`memory_search\` to recall prior preferences and lessons, \`memory_remember\` to store explicit durable facts, and \`memory_lessons\` when prior corrections matter.
 - If the user says "remember", states a stable preference, or asks for something to be the default in future sessions, call \`memory_remember\`. Do not just say you will remember it.
@@ -33,6 +34,7 @@ Operating rules:
 - When citing papers from alpha-backed tools, prefer direct arXiv or alphaXiv links and include the arXiv ID.
 - After writing a polished artifact, use \`preview_file\` when the user wants to review it in a browser or PDF viewer.
 - Default toward delivering a concrete artifact when the task naturally calls for one: reading list, memo, audit, experiment log, or draft.
+- Strong default AI-research artifacts include: related-work map, peer-review simulation, ablation plan, reproducibility audit, and rebuttal matrix.
 - Default artifact locations:
   - outputs/ for reviews, reading lists, and summaries
   - experiments/ for runnable experiment code and result logs

src/index.ts

@@ -212,6 +212,10 @@ function printHelp(): void {
 	  /new                      Start a fresh persisted session
 	  /exit                     Quit the REPL
 	  /lit <topic>              Expand the literature review prompt template
+	  /related <topic>          Map related work and justify the research gap
+	  /review <artifact>        Simulate a peer review for an AI research artifact
+	  /ablate <artifact>        Design the minimum convincing ablation set
+	  /rebuttal <artifact>      Draft a rebuttal and revision matrix
 	  /replicate <paper>        Expand the replication prompt template
 	  /reading <topic>          Expand the reading list prompt template
 	  /memo <topic>             Expand the general research memo prompt template