Agent skill

Specialist Review

by hanamizuki in hanamizuki/solopreneur

Tech-stack-aware expert code review using specialized subagents.

MITAuto-check passedDevelopment

Install Specialist Review

skills CLI
$ npx skills add hanamizuki/solopreneur --skill specialist-review -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install hanamizuki/solopreneur specialist-review --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/hanamizuki/solopreneur.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/solopreneur/specialist-review .claude/skills/specialist-review && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
specialist-review
GitHub stars
152
Token cost
~3k tokens
SKILL.md length
916 words
Files
1
Skills in repo
31
Repo updated
First seen
Licence
MIT

At a glance

Tech-stack-aware expert code review using specialized subagents.

  • Works in 6 steps: Determine Review Scope → Identify Tech Stacks from Diff → 25: Pick a Reviewer for Each Stack → …
  • The user says specialist review
  • SKILL.md covers Step 1: Determine Review Scope, Step 2: Identify Tech Stacks…, Step 2.25: Pick a Reviewer for… and Step 2.5: Check context7…, plus 3 more sections
  • Calls git and gh

What it does

Specialist Review is an agent skill from hanamizuki/solopreneur. Tech-stack-aware expert code review using specialized subagents. Detects which tech stacks are in the diff, then dispatches the matching specialist agents (ios-dev, android-dev, ai-engineer, neo4j-dev) to review against their skill-index best practices. Use when the user says "specialist review", "expert review", "stack review", or wants a multi-perspective code review with best practice verification. Also use after completing a significant implementation when thorough review is needed.

Its SKILL.md is about 3k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Development, covering Subagents and Code review. It works with Neo4j, Android, iOS and Git. The repository describes itself as: Skills and agents for solopreneurs — ship, review, debug, and think through problems with AI. The licence is MIT.

When your agent uses it

  • The user says specialist review
  • Wants a multi-perspective code review with best practice verification

Example prompts

  • “specialist review”
  • “expert review”
  • “stack review”
  • “/specialist-review”

Requirements

  • Python 3

Workflow steps

6 steps, taken from the step headings in SKILL.md.

  1. Determine Review Scope
  2. Identify Tech Stacks from Diff
  3. 25: Pick a Reviewer for Each Stack
  4. 5: Check context7 Availability
  5. Dispatch Subagents
  6. Aggregate and Report

What it can do on your machine

Read from SKILL.md and the folder at commit f43f001. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • git
    • gh

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use git and gh, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Specialist Review loads about 3k tokens when it runs. Until then it costs about 127 tokens; SKILL.md has 916 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~127
When it runs · the whole SKILL.md, loaded when a task matches
~3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from hanamizuki/solopreneur at commit f43f001, republished under its MIT licence (© hanamizuki). 916 words, ~3,047 tokens.

Download SKILL.mdSave it as .claude/skills/specialist-review/SKILL.md (or your agent's skills folder).
name
specialist-review
description
Tech-stack-aware expert code review using specialized subagents. Detects which tech stacks are in the diff, then dispatches the matching specialist agents (ios-dev, android-dev, ai-engineer, neo4j-dev) to review against their skill-index best practices. Use when the user says "specialist review", "expert review", "stack review", or wants a multi-perspective code review with best practice verification. Also use after completing a significant implementation when thorough review is needed.

Specialist Code Review

Dispatch specialized subagents to review code changes against their domain-specific best practices (skill indices).

Step 1: Determine Review Scope

Detect the review scope automatically, in this priority order:

  1. User specified a PR number or URL → use that PR's diff
  2. Current branch has an open PR → gh pr diff
  3. Current branch is not main/master → git diff main...HEAD
  4. Uncommitted changes exist → git diff HEAD (staged + unstaged; plain git diff misses anything already staged)
  5. None of the above → ask the user what to review

Run these checks:

bash
git branch --show-current
gh pr list --head $(git branch --show-current) --json number,url --jq '.[0]'
git status -s

Once scope is determined, get the full diff and save it to a variable for the subagents.

Step 2: Identify Tech Stacks from Diff

Read the full diff and identify which tech stacks are involved based on file paths and content:

SignalTech StackSubagent
*.swift, *.xib, ios/, SwiftUI/UIKit importsiOSios-dev
*.kt, *.kts, android/, Compose/Room importsAndroidandroid-dev
LangChain/LangGraph/OpenAI/Anthropic importsLLM/AIai-engineer
*.cypher, Neo4j driver imports, graph schemaNeo4jneo4j-dev
*.py, FastAPI/Flask/Django importsPython Backendgeneral-purpose
*.ts, *.tsx, *.jsx, React/Next.js importsWeb Frontendgeneral-purpose
docs/gtm/, BRAND.md, marketing copyMarketing / Brandmarketer
*.css, *.scss, design system filesDesigndesigner

List all detected stacks and which subagents will be dispatched. If only one stack is detected, dispatch one agent. If multiple, dispatch them in parallel.

On Codex the last two rows never resolve to their agent — marketer and designer publish no Codex package — and general-purpose has no Codex equivalent. All three fall through the ladder in Step 2.25 to the built-in explorer.

Also extract the key libraries/frameworks used in the diff (e.g., jetpack compose, swiftui, langgraph, react, room, fastapi). These will be passed to subagents for documentation lookup.

Step 2.25: Pick a Reviewer for Each Stack

Each specialist agent ships as its own sub-plugin (ios-dev, android-dev, ai-engineer, neo4j-dev). Users may have only installed solopreneur (the core plugin), and on Codex none of the four agents exist yet — only their knowledge skills are published.

For each stack detected in Step 2, take the first rung that works. This is the ladder plan-review already uses; don't invent another one.

  1. The matching specialist agent — dispatch it directly.
  2. A generic reviewer subagent — general-purpose on Claude Code, the built-in explorer on Codex. The general-purpose rows of the Step 2 table start here.
  3. Inline — when spawning is unavailable or rejected at the current subagent depth, review that stack yourself in this thread.

Rungs 2 and 3 are degradations, and that stack's section opens with the matching banner, substituting <plugin> with the agent / plugin name for that stack (the agent and plugin share the same name).

Rung 2:

⚠️ Specialist agent <plugin> unavailable. Reviewed by a generic reviewer against the installed <plugin> skills.

Rung 3:

⚠️ Specialist agent <plugin> unavailable and no subagent could be spawned. Reviewed inline with generic expertise.

The rung-2 banner is written by the reviewer itself — it is in the Step 3 output format — so keep it when you paste the report in. The rung-3 banner is yours to write, since nobody else was there to write it.

Report the rung you actually used, and never narrate a dispatch that did not happen. Any other dispatch error (crash, timeout, tool failure) → surface it to the user; do not silently fall back.

Do not pre-check the plugin cache path to decide the rung — the cache layout depends on the local marketplace name the user chose, and the dispatch error is the authoritative signal.

Show full SKILL.md (345 more words)Show less

Step 2.5: Check context7 Availability

Check whether this session exposes the context7 MCP tools — resolve-library-id and query-docs. Enumerate tools however this host does it; each host prefixes MCP tool names its own way (mcp__context7__resolve-library-id on Claude Code). A call that fails because the tool does not exist counts as unavailable.

  • Available: Note this for Step 3. Each subagent will query context7 for the technologies it's reviewing.

  • Not available: Display a one-line notice:

    context7 MCP not installed. With context7, review subagents can automatically query the latest official docs for improved review quality.

    Then proceed normally without context7 steps.

Step 3: Dispatch Subagents

For each detected tech stack, spawn the reviewer Step 2.25 picked, in parallel, with this prompt template.

On Codex that is one spawn_agent call per stack. fork_turns="none" is required — a named agent inheriting full parent history is rejected, and a reviewer that inherits your framing is not an independent read. Set agent_type="explorer" for its analysis-shaped persona, but do not mistake it for a boundary: a child spawned with it records agent_role: explorer and still writes files. The role selects an instruction set, not a permission profile — spawn_agent takes no sandbox argument and the child inherits the parent's tools. So the "do NOT modify any files" line below is an instruction; the only enforcement available is starting the session itself with --sandbox read-only.

If context7 is available (from Step 2.5), include the [CONTEXT7 BLOCK] below. If not available, omit it entirely.

You are an expert reviewer. Do NOT modify any files. Only analyze and report.

## Task

1. Discover the skills for your domain:
   - **If you have a specialist system prompt** (`agents/<platform>-dev.md`): it
     lists curated skills and points to the extended skill index. Follow it.
   - **If you don't** (you are a generic reviewer): resolve the plugin's
     *enabled* install, then pick the 3-5 skills whose names match the diff.
     Ask the host which install is active instead of guessing — on Codex,
     `codex plugin list --json` reports `marketplaceName` and `version` per
     enabled plugin, giving one exact path:
     `"${CODEX_HOME:-$HOME/.codex}"/plugins/cache/<marketplaceName>/<plugin>/<version>/skills/`.
     With no such listing, glob `.../plugins/cache/*/<plugin>/*/skills/*/` and
     take the highest semver: both the marketplace name and the version are
     the user's, several versions of one plugin do coexist in a cache, and
     reviewing against a stale copy is worse than finding nothing. Nothing
     there means the plugin is not installed — say so and review with your
     own expertise.
   Report the absolute path of every SKILL.md you actually read.

2. From the diff below, identify which specific technologies and APIs are used
   (e.g., "Jetpack Compose remember", "LazyColumn key", "SwiftData @Model",
   "React useEffect")

[CONTEXT7 BLOCK — include only when context7 is available]
3. Query official documentation for the key technologies found in step 2:
   - For each major library/framework (e.g., "jetpack compose", "swiftui",
     "langgraph", "react"):
     a. Call `mcp__context7__resolve-library-id` with the library name to get its ID
     b. Call `mcp__context7__query-docs` with the resolved ID and a topic relevant
        to what the diff touches (e.g., if diff uses LazyColumn → query
        "LazyColumn performance best practices")
   - Focus on 2-3 most important libraries, not every dependency
   - Use the retrieved documentation as an additional reference when reviewing
[END CONTEXT7 BLOCK]

4. Scan whatever step 1 gave you — the curated list plus extended index, or the
   plugin cache listing — for TWO categories of relevant skills:
   a. **Technology-specific skills**: skills matching the APIs/frameworks used
   b. **Cross-cutting skills**: performance, architecture, patterns, guidelines
      skills that apply regardless of specific API (e.g., compose performance
      audit, architecture patterns, accessibility, project conventions)

5. For each relevant skill (both categories), read its SKILL.md using the path
   step 1 resolved.

6. Review the diff against each relevant skill's best practices AND context7
   documentation (if queried). For each skill checked, report:
   - Skill name
   - What was checked
   - Conformance: check or warning
   - Specific findings with file:line references

7. Also check for general issues not covered by skills:
   - Security concerns
   - Error handling gaps
   - Performance anti-patterns
   - Naming/style inconsistencies within the diff

## Diff to Review

{paste the full diff here}

## Output Format

### Tech Stack: [platform name]

[If you are not this platform's specialist agent — you had no specialist system
prompt and discovered skills from the plugin cache — open the section with this
line, substituting `<plugin>`. It is the only signal the reader gets that the
named specialist did not run:
> ⚠️ Specialist agent `<plugin>` unavailable. Reviewed by a generic reviewer
> against the installed `<plugin>` skills.

If that discovery turned up nothing — the plugin is not installed — open with
this instead. Do not claim skills you never read:
> ⚠️ Specialist agent `<plugin>` unavailable and no installed `<plugin>` skills
> found. Reviewed with generic expertise.]

#### Skills Checked
| Skill | Aspect | Status | Finding |
|-------|--------|--------|---------|
| skill-name | what was checked | check/warning | details |

Skills read: one absolute SKILL.md path per line, marketplace and version
segments included. A bare skill name does not count — the path is the evidence.

#### context7 Documentation Consulted
| Library | Topic Queried | Key Insight |
|---------|--------------|-------------|
| library-name | what was queried | relevant finding from docs |

(Omit this table if context7 was not used)

#### General Findings
- [any issues not covered by skills]

#### Summary
[1-2 sentence overall assessment with taste rating]

Step 4: Aggregate and Report

Wait for all subagents to complete, then compile a unified report:

markdown
## Specialist Review: [branch name or PR title]

### Scope
[what was reviewed: PR #N / branch diff / uncommitted changes]

### Reviews

[per stack: the Step 2.25 degradation banner whenever rung 2 or 3 ran, then that
stack's report pasted verbatim — keep its Skills Checked table and its Skills
read paths; that list is how the user tells a real skill-backed review from a
plausible one. Saying "a generic reviewer ran" in prose is not the banner]

### Cross-Cutting Concerns
[issues that span multiple platforms, if any]

### Verdict
[overall assessment: ready to merge / needs fixes / needs discussion]
[list any blocking issues vs nice-to-haves]

Notes

  • If a skill index doesn't exist for a detected stack, the subagent should use its built-in expertise instead
  • Each subagent should read at most 3-5 most relevant skills (not the entire index)
  • On Codex, discovery is the installed plugin cache only — the extended skill index (/rebuild-skill-index) is a Claude Code path and is not ported
  • The subagent prompt includes the full diff so it can reference specific lines
  • If the diff is very large (>500 lines), mention this to the user and suggest focusing on specific files

© hanamizuki, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/solopreneur/specialist-review of hanamizuki/solopreneur.

Open the folder on GitHubat commit f43f001

Compare with similar skills

Specialist Review next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Specialist Review compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Specialist Review this skillhanamizuki/solopreneur152—~3kAutomated safety check: PassMIT
Cursor Composer Task DelegateChachamaru127/claude-code-harness3.2k—~4.4kAutomated safety check: NotesMIT
Ad ReviewCorridorTech/PoseCap224—~2.4kAutomated safety check: NotesApache-2.0
Workflow Code ReviewdavidYichengWei/agentic-engineering-framework158—~1.1kAutomated safety check: PassMIT
Subagent ReviewNikiforovAll/claude-code-rules141—~927Automated safety check: PassApache-2.0
O2 Review Loopopenobserve/openobserve22k—~3.7kAutomated safety check: PassAGPL-3.0

Similar skills

  • Cursor Composer Task Delegate

    Chachamaru127/claude-code-harness

    Hands one implementation task to Cursor Composer in an isolated git worktree, then reviews its diff and cherry-picks the result into the main branch.

    3.2k GitHub stars~4.4k tokensUpdated 6 days ago
    DevelopmentAuto-check: notes
  • Ad Review

    CorridorTech/PoseCap

    Two-axis fresh-context code review per WORKFLOW §10. An agent skill from CorridorTech/PoseCap.

    224 GitHub stars~2.4k tokensUpdated 4 days ago
    DevelopmentAuto-check: notes
  • Workflow Code Review

    davidYichengWei/agentic-engineering-framework

    代码评审。协调 5 个专项 reviewer subagent 对代码进行并行多维度审查。可由用户直接触发,也可由主 agent 加载后作为 Judge 执行。

    158 GitHub stars~1.1k tokensUpdated 6 mo ago
    DevelopmentAuto-check passed
  • Subagent Review

    NikiforovAll/claude-code-rules

    Review changed code for reuse, quality, and efficiency using three parallel disposable subagents.

    141 GitHub stars~927 tokensUpdated 9 days ago
    DevelopmentAuto-check passed
  • O2 Review Loop

    openobserve/openobserve

    Splits a change into planner, coder and independent reviewer roles: you confirm a spec, a subagent implements it, and a separate reviewer checks each round's local WIP commit.

    22k GitHub stars~3.7k tokensUpdated yesterday
    Agent WorkflowsAuto-check passed
  • Pre-PR Review

    yuga-hashimoto/and-code

    Runs local checks and a repo-reviewer subagent over the whole branch diff before a pull request is opened, then records the approval in the PR description.

    127 GitHub stars~710 tokensUpdated 3 days ago
    DevelopmentAuto-check passed

More from hanamizuki/solopreneur

All 31 skills in this repo
  • Preview

    hanamizuki/solopreneur

    Create an interactive HTML preview of any proposal, plan, idea, doc, brief, or spec and land it in the browsable local Preview Library by default (opens under file:// with a catalog sidebar and…

    152 GitHub stars~5.2k tokensUpdated 14 days ago
    Auto-check passed
  • iOS Patterns

    hanamizuki/solopreneur

    A skill your agent uses when building iOS/macOS apps with SwiftUI — covers localization (String Catalogs), date/time formatting, JSON date decoding, Previews, state management, sheet/navigation…

    152 GitHub stars~2.3k tokensUpdated 14 days ago
    Auto-check: notes
  • Linkedin Growth

    hanamizuki/solopreneur

    LinkedIn organic growth consultant — diagnoses profiles, discusses goals, and co-creates a personalized 90-day growth plan.

    152 GitHub stars~3.7k tokensUpdated 14 days ago
    Auto-check passed
  • Perspective

    hanamizuki/solopreneur

    Switch perspectives to think through problems using the mental models of ten iconic thinkers: Elon Musk, Richard Feynman, Charlie Munger, Naval Ravikant, Steve Jobs, Nassim Taleb, Ilya Sutskever…

    152 GitHub stars~971 tokensUpdated 14 days ago
    Auto-check passed
  • Slide Design

    hanamizuki/solopreneur

    Create brand-aware presentations using frontend-slides or reveal.js.

    152 GitHub stars~3.9k tokensUpdated 14 days ago
    Auto-check passed
  • X Growth

    hanamizuki/solopreneur

    X/Twitter growth consultant — diagnoses profiles, discusses goals, and co-creates a personalized growth plan.

    152 GitHub stars~3.8k tokensUpdated 14 days ago
    Auto-check passed

Questions about Specialist Review

What does Specialist Review do?

Tech-stack-aware expert code review using specialized subagents. Specialist Review is an agent skill from hanamizuki/solopreneur. Tech-stack-aware expert code review using specialized subagents.

When should I use Specialist Review?

Specialist Review fits situations like: the user says specialist review; wants a multi-perspective code review with best practice verification.

How do I install Specialist Review in Claude Code?

Run `npx skills add hanamizuki/solopreneur --skill specialist-review -a claude-code`. Or copy the skill folder (skills/solopreneur/specialist-review in hanamizuki/solopreneur) into .claude/skills/specialist-review in your project. Claude Code loads it when a task matches its description.

How do I install Specialist Review in Codex?

Run `npx skills add hanamizuki/solopreneur --skill specialist-review -a codex`. Or copy the skill folder (skills/solopreneur/specialist-review in hanamizuki/solopreneur) into .agents/skills/specialist-review in your project. Codex loads it when a task matches its description.

Can I use Specialist Review in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add hanamizuki/solopreneur --skill specialist-review -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/specialist-review, .gemini/skills/specialist-review, .github/skills/specialist-review and .opencode/skills/specialist-review in your project.

What does Specialist Review need to run?

Going by SKILL.md and its folder, Specialist Review needs the command-line tools its instructions call (git and gh). Our summary lists: Python 3.

Does Specialist Review access the network?

SKILL.md contains no URLs. Its commands use git and gh, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Specialist Review safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Specialist Review use?

Specialist Review is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Specialist Review use?

About 3k tokens (SKILL.md is roughly 12k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Specialist Review?

Skills that share tags, products or a category with Specialist Review: Cursor Composer Task Delegate (Chachamaru127/claude-code-harness, 3.2k stars), Ad Review (CorridorTech/PoseCap, 224 stars), Workflow Code Review (davidYichengWei/agentic-engineering-framework, 158 stars) and Subagent Review (NikiforovAll/claude-code-rules, 141 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Specialist Review?

hanamizuki (a GitHub user) maintains it in hanamizuki/solopreneur, which has 152 GitHub stars. The repository holds 31 skills in this directory. The repository was last updated on September 26, 2026.

Source: hanamizuki/solopreneur on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.