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Review Implementation

Organization
DasDigitaleMomentum
review-implementation

Independent review of a completed implementation against its plan, acceptance criteria, and code quality standards. Produces a structured review with severity-rated findings. Use after execute-work-package to validate implementation quality.

Overview

PublisherDasDigitaleMomentum
Repositoryopencode-processing-skills
Skill namereview-implementation
Stars
61
Forks
3
Bundled files
2
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by DasDigitaleMomentum on GitHub. Read the source before you install it.

Installation

Install the Review Implementation AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/DasDigitaleMomentum/opencode-processing-skills/tree/main/skills/review-implementation
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/DasDigitaleMomentum/opencode-processing-skills.git /tmp/opencode-processing-skills
mkdir -p .claude/skills
cp -r /tmp/opencode-processing-skills/skills/review-implementation .claude/skills/review-implementation
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Review Implementation in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Review Implementation on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Review Implementation is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Skill: Review Implementation

This skill provides an independent quality gate for completed implementations executed via execute-work-package.

A fresh reviewer (with no implementation context) evaluates the actual code changes against the plan, acceptance criteria, and coding standards. The review is persisted as a plan artifact.


When to Use

Use this skill when:

  • A phase has been implemented via execute-work-package and you want to validate before committing/merging.
  • You want to verify that acceptance criteria are actually met (not just claimed).
  • You want an independent assessment of test quality and coverage.
  • The user explicitly requests an implementation review.

Do not use this skill to:

  • Review the plan itself (use review-plan).
  • Review an implementation plan before execution (use review-implementation-plan).
  • Fix implementation issues during the independent review pass. After review completion, the Primary may accept defects and explicitly instruct remediation through review-fix in the eligible source session.

Review Focus

The primary specifies the review focus when delegating. The default focus is functional and technical findings — correctness, feasibility, completeness of the solution.

Review posture

No Gold-Plating. No Adversarial Reviewing. No Scope Creep. Report only evidence-backed problems that affect correctness, security, acceptance, or the reviewed objective. Do not hunt for gotchas, invent improvements, or keep a review/fix loop alive to create more work. This does not mean overlooking real defects.

Formal criteria (DoD compliance checklists, NFR conformance, reference consistency, documentation cleanup) are secondary. Only include formal findings when they reveal real problems — not as standard checkboxes to fill. A review cluttered with formal nitpicking buries the findings that matter.

The primary passes the focus via {{focus}} in the delegation prompt. If no focus is specified, use the default.


Execution Model

Roles

  • Primary (maintainer)

    • Invokes the review skill after implementation is complete.
    • Delegates to delegate-strong (default) or general (for same-model perspective).
    • Receives review summary and decides on follow-up actions.
    • Retains the reviewer task_id while deciding on remediation under review-fix's session eligibility rules.
  • Subagent (delegate-strong / general)

    • Reads plan, phase, implementation plan, and execution digest with no prior context.
    • Examines the actual code changes (via git diff or file reading).
    • Verifies acceptance criteria against real code and test output.
    • Writes the review artifact to plans/<name>/reviews/impl-review-phase-N.md.

Why delegate-strong (not doc-explorer or implementer)

The reviewer must be independent from both the planner and the implementer. delegate-strong provides the judgment depth to evaluate code quality, test coverage, and acceptance criteria without the bias of having authored or implemented the work.

Routing Matrix (Who does what)

  • Writes: plans/<name>/reviews/impl-review-phase-N.md
  • Does NOT write: code, plans, implementation plans, or any other artifact.
  • Primary: owns the decision of whether to accept, request rework, or reject.
  • delegate-strong/general: performs the review, including code examination and test verification.

The reviewer directly reads scoped source, authoritative plans/docs, symbols, and compact targeted evidence. It keeps uncurated bulk evidence out of its working context: use a reliable focused filter when sufficient, otherwise route the raw artifact, command, or path plus a focused question to retriever. This includes verbose diffs, command/test output, logs, generated dumps, broad searches, and coherent multi-file evidence. Numeric tool truncation is a safety net, not the routing rule.


Workflow

1) Prepare references

Primary gathers:

  • Review focus from the delegation prompt
  • plans/<name>/plan.md
  • plans/<name>/phases/phase-N.md
  • plans/<name>/implementation/phase-N-impl.md
  • Execution digest (from execute-work-package output) or git diff reference
  • docs/ references (if available)

2) Delegate

Primary delegates to delegate-strong (or general) using tpl-review-impl-prompt.md.

Provide:

  • Plan, phase, implementation plan paths
  • Digest reference or git diff base reference
  • Review output path: plans/<name>/reviews/impl-review-phase-N.md
  • Review focus (freetext — what to prioritize)

3) Receive summary

Subagent returns:

  • Overall verdict (Accepted / Needs Rework / Rejected)
  • Finding count by severity
  • Top 3 findings

4) Act on findings

Primary decides:

  • Accepted: Proceed to commit/merge. Update plan via update-plan.
  • Needs Rework: Accept defects and explicitly instruct remediation within scope. Reuse correct and sufficient source-session review/diagnosis context through review-fix; that skill owns eligibility, multi-source assignment, and fallback. Related multi-file fixes do not alone require a new work package.
  • Fresh work: If retained context/output is faulty, insufficient, or unavailable, start a fresh execute-work-package with a finding-source pointer as a hint, not inherited context. A new objective/gated scope also requires a new gated package; resolve new dependency or Primary decisions before dependent work.
  • Rejected: Discuss with user. May require replanning via update-plan.

The review pass ends before remediation begins. A reviewer that applies fixes is no longer independent; an additional review is optional and requires an explicit Primary or user decision. Escalate materially new risk/uncertainty to the Primary for that decision. Do not automatically chain review and remediation loops.


Output Contract

The review artifact plans/<name>/reviews/impl-review-phase-N.md MUST:

  • Follow the canonical template headings and frontmatter keys.
  • Include a clear Overall Assessment with verdict and reasoning.
  • Verify EACH acceptance criterion against actual code with evidence.
  • Assess test quality beyond pass/fail (meaningful? behavioral? regression-catching?).
  • Address Real-World Testing explicitly (performed, not performed, waived).
  • Evaluate code quality against the coding standards from execute-work-package.
  • Rate every finding with a severity (Critical / Major / Minor / Note).

Rules

  • The reviewer must examine actual code, not just the execution digest. The digest is a starting point, not the truth.
  • The reviewer must approach the implementation without prior context. Independent perspective is the value.
  • Findings are advisory. The primary decides whether and how to act.
  • Do not modify code or plan artifacts during review — only produce the review artifact.
  • Do not discard the reviewer task_id until the primary has decided whether remediation is needed.
  • Ensure the reviews/ directory exists before delegating (create if needed).
  • Test quality is a first-class concern. A review that only checks "tests pass" without evaluating test meaningfulness is incomplete.
  • When broad/full verification is supplied for review or remediation, use the smallest targeted tests for iterative diagnosis. Run the approved broad/full command once only when ready as the final gate; if it fails, return to targeted diagnosis and pass targeted tests before running the final gate again. Never weaken or omit it.
  • Owning review and test verification does not imply consuming raw verbose output directly. Potentially verbose output should be spooled under /tmp/opencode/, with only path, command, exit status, and compact metadata/evidence retained; use focused filtering or retriever for analysis.
  • Flag mock-only testing as a limitation unless the user explicitly waived real-world testing.

Templates

  • tpl-impl-review.md — Canonical review output format with embedded review criteria
  • tpl-review-impl-prompt.md — Primary → reviewer delegation prompt

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Review Implementation AI skill do?

Independent review of a completed implementation against its plan, acceptance criteria, and code quality standards. Produces a structured review with severity-rated findings. Use after execute-work-package to validate implementation quality.

Why use Review Implementation on TypingMind?

Because you install it once and use it with any model. Review Implementation is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Review Implementation in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/DasDigitaleMomentum/opencode-processing-skills/tree/main/skills/review-implementation. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Review Implementation?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Review Implementation?

As many as you like. As long as a model supports skills, you can use Review Implementation with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Review Implementation AI skill free?

Yes. It is published on GitHub by DasDigitaleMomentum under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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