Borrowing it
Nothing to install: this file belongs to HatmanStack/RAGStack-Lambda. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/HatmanStack/RAGStack-Lambda/main/.claude/skills/repo-eval/SKILL.mdgit clone --depth 1 https://github.com/HatmanStack/RAGStack-LambdaWrote 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.
[](https://agentmods.dev/skills/hatmanstack/ragstack-lambda/repo-eval)<a href="https://agentmods.dev/skills/hatmanstack/ragstack-lambda/repo-eval"><img src="https://agentmods.dev/badge/skills/hatmanstack/ragstack-lambda/repo-eval/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.
<a href="https://agentmods.dev/skills/hatmanstack/ragstack-lambda/repo-eval"><img src="https://agentmods.dev/badge/skills/hatmanstack/ragstack-lambda/repo-eval.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00038 | $0.02035 |
| Opus 5.5 | $0.00015 | $0.00814 |
| Sonnet 5.5 | $0.00008 | $0.00407 |
| Haiku 4.5 | $0.00004 | $0.00203 |
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 2mo 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.
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:
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.
- 2mo ago First seen · 243 lines · 38 tokens per session scan A 5264be232c3e
repo-eval is a skill published in the GitHub repository HatmanStack/RAGStack-Lambda (25 stars, last pushed yesterday), licensed Apache-2.0. It adds 38 tokens to every session and 2,035 once invoked, about $0.0002 per session on Opus 5.5. 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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