eng-rigorous-validation

eng-rigorous-validation is a skill for Claude Code from tjboudreaux/cc-plugin-engineering-excellence. It costs 25 tokens per session (348 once invoked), scanned A, original, MIT.

A testing and verification workflow that requires evidence before code is merged. It covers automated tests, linters, simulations, screenshots, logs, and other checks relevant to a change.

In plain words
What is it for?
Use it to define tests before coding, run checks across affected platforms or environments, capture test evidence, and record the commands and code version used.
Why use it?
It prevents teams from treating untested behavior as finished and makes review results reproducible.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the engineering-excellence plugin — 13 skills, 4 agents shipped together

Good fit Use it to define tests before coding, run checks across affected platforms or environments, capture test evidence, and record the commands and code version used.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/tjboudreaux/cc-plugin-engineering-excellence/eng-rigorous-validation
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 tjboudreaux/cc-plugin-engineering-excellence --skill eng-rigorous-validation
Clone the repo
git clone --depth 1 https://github.com/tjboudreaux/cc-plugin-engineering-excellence

Made for: Claude Code.

Or install engineering-excellence, the plugin that ships this one along with the rest of its 13 skills, 4 agents.

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 eng-rigorous-validation

README.md
[![agentmods](https://agentmods.dev/badge/skills/tjboudreaux/cc-plugin-engineering-excellence/eng-rigorous-validation/github.svg)](https://agentmods.dev/skills/tjboudreaux/cc-plugin-engineering-excellence/eng-rigorous-validation)
Your own site
<a href="https://agentmods.dev/skills/tjboudreaux/cc-plugin-engineering-excellence/eng-rigorous-validation"><img src="https://agentmods.dev/badge/skills/tjboudreaux/cc-plugin-engineering-excellence/eng-rigorous-validation/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 eng-rigorous-validation

Your own site · 80×15
<a href="https://agentmods.dev/skills/tjboudreaux/cc-plugin-engineering-excellence/eng-rigorous-validation"><img src="https://agentmods.dev/badge/skills/tjboudreaux/cc-plugin-engineering-excellence/eng-rigorous-validation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 25 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 348 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 $0.00025 $0.00348
Opus 5 $0.00013 $0.00174
Sonnet 5 $0.00005 $0.00070
Haiku 4.5 $0.00003 $0.00035

Measured 8d ago against content hash 0efcf0c37850, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

eng-rigorous-validation 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/eng-rigorous-validation/SKILL.md · 36 lines

What it actually says

Rigorous Validation

Intent

  • Ship only when behavior is proven, not assumed.
  • Treat tests, QA scripts, linters, and on-chain/off-chain simulations as first-class deliverables.
  • Capture evidence so reviewers can verify quickly.

Inputs

  1. Canonical test commands (unit, integration, e2e, contract sims, UI snapshots).
  2. Acceptance criteria plus measurable signals (logs, screenshots, transaction hashes).
  3. Migrations/seed data steps required to exercise the change locally.

Workflow

  1. Design tests before coding
    • Specify failing cases and target assertions for each requirement.
    • Align on how to stub external services, wallets, or platform APIs.
  2. Automate and isolate
    • Prefer deterministic, headless test harnesses; avoid manual-only steps.
    • Seed data/fixtures close to tests to prevent global coupling.
  3. Run the full relevant matrix
    • Cover affected platforms (iOS/Android/web), runtimes, or chain environments.
    • Capture artifacts (logs, screenshots, traces) for any non-deterministic checks.
  4. Track outcomes
    • Record exact commands run and their commit hash in the PR or issue.
    • File follow-ups for flaky tests before merging.

Verification

  • All targeted tests green and documented; no TODOs or skipped suites without owner sign-off.
  • Static analysis, type-checking, formatters, and contract analyzers pass.
  • Evidence (artifacts, hashes, screenshots) attached or linked for reviewer inspection.
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 · 36 lines · 25 tokens per session scan A 0efcf0c37850

Subscribe to this mod's changes

eng-rigorous-validation is a skill published in the GitHub repository tjboudreaux/cc-plugin-engineering-excellence (4 stars, last pushed 7mo ago), licensed MIT. It adds 25 tokens to every session and 348 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

research-engineer

An uncompromising Academic Research Engineer. Operates with absolute scientific rigor, objective criticism, and zero flair. Focuses on theoretical correctness, formal verification, and optimal implementation across any required technology.

davila7/claude-code-templates · 43 tokens

tika-eval-compare

Compare extracts from two Tika builds over a corpus to detect regressions in content, encoding, exceptions, and embedded-document handling. Use for "compare before/after extracts", "eval this change against the corpus".

apache/tika · 50 tokens

neuron-evaluation-engineer

Create and run AI evaluations with datasets, assertions, and output drivers in Neuron AI. Use this skill whenever the user mentions evaluation, testing AI systems, creating evaluators, dataset-driven testing, assertion-based validation, or wants to measure AI system performance. Also trigger for tasks involving…

neuron-core/neuron-ai · 77 tokens

jetson-validate-image

Use after jetson-flash-image to run static BSP checks, on-target smoke/regression tests on a flashed DUT, or both. Not for build or flash steps. Triggers: validate bsp, on-target validation.

NVIDIA/skills · 50 tokens

atmos-validation

Validate Atmos projects, components, arbitrary JSON Schema inputs, EditorConfig, and GitHub Actions; use affected-file selection and native CI annotations.

cloudposse/atmos · 31 tokens

skill-benchmark

Benchmark AI skill effectiveness by measuring implementation quality against legacy constraints.

HoangNguyen0403/agent-skills-standard · 16 tokens