.claude/skills/review-pipeline/SKILL.md
Use when you want review AND automated fixes in one pass, when /review-dispatch alone would leave findings unaddressed, or before merging a feature branch that needs thorough diagnosis and remediation. Two-phase diagnose-then-fix pipeline.
npx skillsauth add ahrav/gossip-rs review-pipelineInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Two-phase review team: diagnose issues from multiple perspectives, then systematically fix approved findings.
/review-dispatch or /ce:review alone would leave findings unaddressed/review-pipeline [<target>]
git diff)--staged: review staged changes (git diff --cached)--branch: review all commits on the current branch vs mainLaunch two diagnostic agents in parallel using the Agent tool. Each agent gets a different review methodology to maximize coverage through perspective diversity.
Prompt this agent with the review-dispatch methodology: six focused review dimensions applied independently, then merged into a ranked report.
Dimensions to cover:
with_capacity, lock contention, async blocking, cache-hostile patternsAgent prompt template:
You are a code reviewer analyzing changes for quality issues. Review the
following code changes across six dimensions: correctness, design, performance,
safety, documentation, and complexity.
For each finding, report:
- Severity: Critical | High | Medium | Low
- Dimension: which of the six
- Location: file:line
- Evidence: what you see in the code
- Impact: what could go wrong
- Recommended fix: actionable remediation
Target: {target_description}
{diff or file contents}
Prompt this agent with the ce:review methodology: tiered persona agents with confidence scoring, where only findings above a confidence threshold are reported.
Agent prompt template:
You are a senior code reviewer performing a confidence-gated review. For each
potential issue you identify, assign a confidence score (0-100) representing
how certain you are this is a real problem, not a false positive.
Only report findings with confidence >= 60.
For each finding, report:
- Confidence: 0-100
- Severity: Critical | High | Medium | Low
- Location: file:line
- Issue: what is wrong
- Why you are confident: evidence from the code
- Recommended fix: actionable remediation
Target: {target_description}
{diff or file contents}
After both agents complete, merge their findings:
[CONVERGED] tag — these are highest confidencePresent findings to the user in this format:
## Review Pipeline — Phase 1 Complete
Found {N} issues across {M} files.
### Findings (ranked by severity + convergence)
| # | Sev | Location | Issue | Source |
|----|----------|-----------------------|--------------------------------|-------------|
| 1 | Critical | src/engine/core.rs:42 | Unchecked overflow in merge() | CONVERGED |
| 2 | High | src/engine/core.rs:87 | Missing error propagation | Specialist |
| 3 | High | src/shard/split.rs:15 | Clone in hot loop | Confidence |
| 4 | Medium | src/engine/core.rs:55 | Dead code in fallback path | CONVERGED |
Approve all? Or enter finding numbers to address (e.g., "1,2,3"):
Wait for user response. Accept:
For each approved finding, dispatch execution agents to apply fixes.
Group approved findings by file. Each execution agent owns a non-overlapping set of files. If multiple findings target the same file, they go to the same agent.
For each file group, launch an Agent with this prompt:
You are fixing code review findings. Apply the minimum change needed to
address each finding correctly. Do not refactor beyond what the finding
requires.
After fixing, run:
cargo fmt --all && cargo check && cargo clippy --all-targets --all-features -- -D warnings
Findings to address:
{list of findings with file:line, issue description, and recommended fix}
Files you own (only modify these):
{list of files in this group}
After all execution agents finish, present a summary:
## Review Pipeline — Complete
### Changes Applied
| Finding | Status | Files Modified |
|---------|---------|-----------------------------|
| #1 | Fixed | src/engine/core.rs |
| #2 | Fixed | src/engine/core.rs |
| #3 | Fixed | src/shard/split.rs |
### Verification
Run to confirm:
cargo fmt --all && cargo check && cargo clippy --all-targets --all-features -- -D warnings
cargo test --all-features
SKIPPED with reason/review-dispatch — Phase 1 methodology (multi-specialist)/ce:review — Phase 1 methodology (confidence-gated)/execute-review-findings — Phase 2 methodology/perf-pipeline — Performance-focused team pipeline/test-pipeline — Testing-focused team pipelinedevelopment
Deep first-principles code explanation that builds real understanding through phased walkthroughs with diagrams. Covers algorithms, data structures, memory layout, concurrency patterns, and performance tricks — especially for systems code in Rust. Use whenever the user asks to explain, walk through, break down, deep dive into, or understand code. Trigger on "how does this work", "what's happening here", "teach me about this", "why is it done this way", or when the user references a file with @ and wants to understand it. Proactively use when examining code involving lock-free algorithms, atomics/CAS, memory ordering,
development
Use when creating implementation-ready beads tasks that need testing strategy, optimal implementation approach, and documentation requirements baked in — composes /create-task with parallel enrichment agents that analyze the codebase and produce concrete test specifications, algorithm/data-structure guidance, and doc quality standards so implementing agents don't need to re-research
development
--- name: autoresearch description: Autonomous Goal-directed Iteration. Apply Karpathy's autoresearch principles to ANY task. Loops autonomously — modify, verify, keep/discard, repeat. Supports bounded iteration via Iterations: N inline config. version: 1.9.11 --- # Claude Autoresearch — Autonomous Goal-directed Iteration Inspired by [Karpathy's autoresearch](https://github.com/karpathy/autoresearch). Applies constraint-driven autonomous iteration to ANY work — not just ML research. **Core id
development
Use when implementing a new feature and assessing coverage gaps, during periodic test hygiene, when test suites feel bloated, or before merging code that changes coordination or hot paths. Two-phase assess-then-improve testing pipeline.