repo-eval

A codebase assessment carried out by three separate evaluator agents across 12 areas, covering hiring decisions and the practical work of maintaining the code after release.

In plain words
What is it for?
Use it to assess a repository for a chosen developer level, investigate concerns, and produce an evaluation document for a later remediation process.
Why use it?
It gives a structured view of a repository’s strengths, risks, and known problem areas instead of relying on one informal review.

Skill for Claude CodeCodex

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 skills/hatmanstack/ragstack-lambda/repo-eval
Any agent
npx skills add HatmanStack/RAGStack-Lambda --skill repo-eval
Clone the repo
git clone --depth 1 https://github.com/HatmanStack/RAGStack-Lambda

Made for: Claude Code, Codex.

Per session 38 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,035 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 $0.00038 $0.02035
Opus 5 $0.00019 $0.01018
Sonnet 5 $0.00008 $0.00407
Haiku 4.5 $0.00004 $0.00203

Measured yesterday against content hash 5264be232c3e, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

repo-eval 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 yesterday.

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.

.claude/skills/repo-eval/SKILL.md · 243 lines

How it starts

The opening of the file, as written. The whole thing — 243 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Repo Evaluation

You coordinate a 3-evaluator hiring panel assessment of a codebase. Each evaluator runs as a separate agent with its own context window.

Input

$ARGUMENTS is optional context — the repo path, role level being evaluated, or specific concerns. If empty, evaluate the current working directory.

Process

Step 1: Scope the Evaluation

Ask scoping questions one at a time, preferring multiple choice. Wait for each answer before asking the next.

The code evaluation runs 3 evaluator agents in parallel, each scoring 4 pillars (12 total). These questions calibrate the evaluation.

Question 1 — Known pain points give the evaluators a starting hypothesis instead of scanning cold:

Are there parts of the codebase you already know are problematic?
Things that keep breaking, areas you dread touching, modules that slow down every PR.

A) Yes (tell me which areas and what's wrong)
B) No — scan everything with fresh eyes

Question 2 — Role level sets the scoring bar:

What role level should I evaluate this codebase against?

A) Junior Developer — fundamentals: readability, basic error handling, test presence
B) Mid-Level Developer — patterns: separation of concerns, consistent conventions, test coverage
C) Senior Developer — production: defensive coding, observability, performance awareness, type rigor
D) Staff+ / Principal — systems: architectural coherence, scalability, operational excellence

Question 3 — Focus areas weight what evaluators pay extra attention to (they still score all 12 pillars):

Any specific concerns the evaluators should weight more heavily?

A) Performance — hot paths, algorithmic complexity, resource management
B) Security — input validation, auth patterns, secrets handling
C) Testing — coverage quality, test architecture, edge cases
D) Architecture — separation of concerns, modularity, coupling
E) Multiple (tell me which)
F) None — balanced evaluation across all pillars

Question 4 — Scope and exclusions:

Read the full file on GitHub · 243 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. yesterday First seen · 243 lines · 38 tokens per session scan A 5264be232c3e

Subscribe to this mod's changes

repo-eval is a skill published in the GitHub repository HatmanStack/RAGStack-Lambda (25 stars, last pushed 4d ago), licensed Apache-2.0. It adds 38 tokens to every session and 2,035 once invoked, about $0.0002 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 2026-08-30.

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