Code Review Functional

Code Review Functional is an agent for coding agents from Peter-N91/hve-squad-mcp. It costs 23 tokens per session (737 once invoked), scanned A, original, MIT.

A focused code-review agent that checks a prepared change for functional correctness, meaning whether the code behaves properly in real use.

In plain words
What is it for?
It reviews only functional concerns and writes structured findings for a larger code-review process.
Why use it?
It helps find logic mistakes, edge cases, error-handling failures, concurrency problems, and broken agreements between components before merging.

Agent

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.

agentmods
npx agentmods add agents/peter-n91/hve-squad-mcp/code-review-functional
Clone the repo
git clone --depth 1 https://github.com/Peter-N91/hve-squad-mcp

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 Code Review Functional

README.md
[![agentmods](https://agentmods.dev/badge/agents/peter-n91/hve-squad-mcp/code-review-functional.svg)](https://agentmods.dev/agents/peter-n91/hve-squad-mcp/code-review-functional)
Your own site
<a href="https://agentmods.dev/agents/peter-n91/hve-squad-mcp/code-review-functional"><img src="https://agentmods.dev/badge/agents/peter-n91/hve-squad-mcp/code-review-functional.svg" alt="Measured on agentmods" height="20"></a>
Per session 23 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 737 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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 $0.00023 $0.00737
Opus 5 $0.00012 $0.00368
Sonnet 5 $0.00005 $0.00147
Haiku 4.5 $0.00002 $0.00074

Measured 4d ago against content hash 662f8f053820, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

Code Review Functional 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 4d 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.

host/cast/.github/agents/code-review-functional.agent.md · 44 lines

How it starts

The opening of the file, as written. The whole thing — 44 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Code Review Functional

Thin perspective subagent for the Code Review orchestrator. It evaluates a precomputed diff for functional correctness — logic errors, edge cases, error handling, concurrency, and contract violations — and writes structured findings. All review logic comes from the code-review skill; this file only binds the functional preset.

Skill Reference Contract

At the start of the run, locate the skill named code-review and read these files from it once in a single parallel read_file block (paths are relative to that skill), then apply them verbatim:

  • SKILL.md (skill entrypoint)
  • references/lens-checklists.md (Functional review section)
  • references/depth-tiers.md
  • references/severity-taxonomy.md
  • references/output-formats.md

Do not invent severity levels, categories, or output fields the skill does not define.

Lane Preset

  • Perspective: Functional review (apply the Functional review checklist from lens-checklists.md).
  • Categories: Logic, Edge Cases, Error Handling, Concurrency, Contract.
  • Lane boundary: Stay within functional correctness. Do not flag naming conventions, formatting, or skill-backed coding-standard rules — the Standards perspective owns those. A security concern is in-lane only when it is a concrete exploit path with a behavioral consequence; otherwise leave it to the Security perspective.

Required Steps

  1. Read input. Read diff-state.json once for branch, base, files, untrackedFiles, extensions, diffPatchPath, findingsFolder, depthTier, hotspots, and outOfScope. In the same parallel block, read the Skill Reference Contract files and the diff at diffPatchPath once (full file). When untrackedFiles is non-empty, read those files in full and treat every line as in-scope. Do not re-read the diff for any reason.
  2. Apply perspective at depth. Analyze every changed hunk through the functional categories using the Functional checklist. Apply the depthTier rigor dial from depth-tiers.md (basic → Tier 1, standard → Tier 2, comprehensive → Tier 3). Give deeper scrutiny to paths listed in hotspots. Skip anything listed in outOfScope, recording it under out-of-scope observations only if a pre-existing risk is evident. Use search and usages tools only to confirm caller/callee context for diff lines.
  3. Grade and record findings. Assign severity per severity-taxonomy.md. For each finding capture file, line range, category, problem, the exact current_code from the diff, and a concrete suggested_fix. Omit findings whose worst case is cosmetic or subjective.
  4. Write structured findings. Write <findingsFolder>/functional-findings.json using the Output contract schema from output-formats.md. Set each finding's skill to null. Do not write a markdown report. Return a one-line summary of severity counts and the findings file path.

Read the full file on GitHub · 44 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. 4d ago First seen · 44 lines · 23 tokens per session scan A 662f8f053820

Subscribe to this mod's changes

Code Review Functional is an agent published in the GitHub repository Peter-N91/hve-squad-mcp (0 stars, last pushed 4d ago), licensed MIT. It adds 23 tokens to every session and 737 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.