pre-implementation-review

pre-implementation-review is a skill for Claude Code from supportersimulator/3-surgeons. It costs 24 tokens per session (750 once invoked), scanned A, original, MIT.

A mandatory adversarial review of an implementation plan by three specialist reviewers before coding begins.

In plain words
What is it for?
Use it after creating an implementation plan and before starting multi-file work; it checks complexity, cross-examines the plan, argues against its likely success, and verifies required infrastructure gates.
Why use it?
It looks for ways the plan could fail while there is still time to change it, reducing implementation mistakes across multiple files.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter. Also seen: names the TodoWrite tool.

Part of the 3-surgeons plugin — 23 skills, 7 commands, 1 agent, 3 hooks, 1 MCP server shipped together

Good fit Use it after creating an implementation plan and before starting multi-file work; it checks complexity, cross-examines the plan, argues against its likely success, and verifies required infrastructure gates.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/supportersimulator/3-surgeons/pre-implementation-review
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add supportersimulator/3-surgeons --skill pre-implementation-review
Clone the repo
git clone --depth 1 https://github.com/supportersimulator/3-surgeons

Made for: Claude Code.

Or install 3-surgeons, the plugin that ships this one along with the rest of its 23 skills, 7 commands, 1 agent, 3 hooks, 1 MCP server.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for pre-implementation-review

README.md
[![agentmods](https://agentmods.dev/badge/skills/supportersimulator/3-surgeons/pre-implementation-review/github.svg)](https://agentmods.dev/skills/supportersimulator/3-surgeons/pre-implementation-review)
Your own site
<a href="https://agentmods.dev/skills/supportersimulator/3-surgeons/pre-implementation-review"><img src="https://agentmods.dev/badge/skills/supportersimulator/3-surgeons/pre-implementation-review/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for pre-implementation-review

Your own site · 80×15
<a href="https://agentmods.dev/skills/supportersimulator/3-surgeons/pre-implementation-review"><img src="https://agentmods.dev/badge/skills/supportersimulator/3-surgeons/pre-implementation-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 24 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 750 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00024 $0.00750
Opus 5 $0.00012 $0.00375
Sonnet 5 $0.00005 $0.00150
Haiku 4.5 $0.00002 $0.00075

Measured 8d ago against content hash e1c2664ba7a5, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

pre-implementation-review scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/pre-implementation-review/SKILL.md · 88 lines

How it starts

The opening of the file, as written. The whole thing — 88 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Pre-Implementation Review

Purpose

The gap between "good plan" and "good implementation" is where most bugs are born. This gate ensures the plan survives adversarial scrutiny BEFORE any code is written.

Auto-Invocation Trigger

Invoke when transitioning from planning to implementation — specifically:

  • After writing-plans skill produces an implementation plan
  • Before executing-plans skill begins work
  • Before any multi-file implementation begins
  • When a design doc exists and coding is about to start

Checklist

You MUST create a TodoWrite task for each step:

  1. Sentinel scan on the implementation plan — run sentinel with the plan description as content. This catches complexity risks the planning phase may have introduced.

  2. Cross-exam the plan with all 3 surgeons — focus question: "What will break when we implement this?" Not "is this a good idea?" (that was architectural-gate's job) but "what will go wrong during execution?"

  3. Counter-position: argue why this plan will FAIL — invoke the counter-position skill. The claim is "this implementation plan will succeed." Steelman why it will not.

  4. Verify gains-gate passes — infrastructure must be healthy before starting. Do not build on broken foundations.

  5. Create explicit rollback strategy — before writing code, document how to undo the changes if they go wrong. This forces thinking about reversibility.

  6. Present review results — summarize findings from steps 1-5. Proceed only with approval. If surgeons raised critical concerns, those must be addressed in the plan before implementation begins.

What the Cross-Exam Should Focus On

The cross-exam question for pre-implementation is specifically:

We are about to implement the following plan:
[plan summary]

Focus your analysis on:
1. What will break during implementation?
2. What dependencies or ordering constraints are missing?
3. What edge cases does the plan not address?
4. Are there simpler approaches the plan overlooked?
5. What tests should exist BEFORE implementation starts?

Read the full file on GitHub · 88 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 8d ago First seen · 88 lines · 24 tokens per session scan A e1c2664ba7a5

Subscribe to this mod's changes

pre-implementation-review is a skill published in the GitHub repository supportersimulator/3-surgeons (2 stars, last pushed 3mo ago), licensed MIT. It adds 24 tokens to every session and 750 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

Related

Other skills, from other repositories

gh-pr-review

Automated Cherry Studio review for local branches, PRs, commits, files, architecture docs, and repository skills. Use for code or documentation reviews that need project-specific naming, main/renderer/shared placement and dependency rules, IpcApi and DataApi boundaries, lifecycle/service ownership, renderer hooks…

CherryHQ/cherry-studio · 147 tokens

review-spd

Findings-first code review workflow for AI coding agents. Use when the user asks to review uncommitted changes, commits in a date range, or a branch compared to the main branch / PR-style diff. Focuses on bugs, regressions, correctness risks, missing tests, security/data-safety issues, and other behavior-changing…

zhu1090093659/deepseek-pp · 72 tokens

karpathy-guidelines

A set of coding guidelines based on observations about common mistakes made by language models. It emphasizes simple solutions, small targeted edits, clear assumptions, and checkable results.

itmisx/deepx-code · 55 tokens

gh-cli

Patterns for invoking the GitHub CLI (gh v2.100.0+) from agents. Use when the task mentions GitHub, gh, pull requests/PRs, issues, releases, gists, Actions/workflow runs, forks, repo cloning, reviews, or you need exact gh commands. Covers pagination, repo targeting, search vs list, discussions, projects, rulesets…

znlgis/my-opencode-deepseek-config · 88 tokens

code-review

A single-pass review method for examining a code change, branch, or pull request. A pull request is a proposed change for review before it is merged into a shared codebase.

znlgis/my-opencode-deepseek-config · 75 tokens

simplify

Behavior-preserving code simplification — reduce complexity without changing what the code does. Use when the task mentions "simplify", "reduce complexity", "too clever", "hard to read", "reduce nesting", or after a feature lands and the code needs polishing. Oracle analyzes (read-only), light-orchestrator applies the…

znlgis/my-opencode-deepseek-config · 70 tokens