verify

A verification workflow for proving that a software change behaves as required before it is delivered. It combines automated checks with real end-to-end exercises, such as running the app, calling an endpoint, clicking a flow, or rendering an output.

In plain words
What is it for?
Use it after non-trivial work to run the project's checks, type checking, linting, tests, and build, then verify each acceptance criterion with realistic inputs, including empty, invalid, unauthorized, and repeated actions.
Why use it?
A successful build or test suite alone does not prove that users see the intended behavior. This workflow compares predicted results with observed results and checks important edge cases.

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/tradebaas/groundwork/verify
Any agent
npx skills add Tradebaas/Groundwork --skill verify
Clone the repo
git clone --depth 1 https://github.com/Tradebaas/Groundwork

Made for: Claude Code, Codex.

Per session 46 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
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 $0.00046 $0.00737
Opus 5 $0.00023 $0.00368
Sonnet 5 $0.00009 $0.00147
Haiku 4.5 $0.00005 $0.00074

Measured 2d ago against content hash 1ecef9506898, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

verify 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 2d 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.

.agents/skills/verify/SKILL.md · 52 lines

How it starts

The opening of the file, as written. The whole thing — 52 lines — stays where its author put it; the contents beside it link to each section on GitHub.

verify: evidence, not confidence

A change is verified when its observable behavior matches its acceptance criteria, and you have seen that behavior yourself. "Should work", "compiles", "tests pass" are not that.

1. The floor: every gate green

Run the full chain and paste real results, not summaries of results: node checks/check.mjs, then the stack gates (typecheck, lint, tests, build; commands are in docs/standards/<stack>.md). Red gate → stop, fix, rerun. Never weaken a gate to pass it.

2. The point: exercise the change

  • Take the spec's acceptance criteria (tier S: the request itself) and walk them end to end in the closest thing to reality available: run the app, call the endpoint, click the flow, render the document. Per criterion: what you predicted you would see → what you did → what you observed. Write the prediction down before running. A pass you did not predict proves only that something happened; a prediction that turns out wrong is the finding, and the real behavior gets explained and traced to a rule before the criterion counts as met.
  • Probe the edges the criteria imply: empty input, wrong input, unauthorized user, the second run (idempotency), the slow path. The first bug is usually one step off the happy path.
  • User-facing output → also run design-guard on what rendered. A built interface gets re-checked against its direction contract and the finish verdict; a document, e-mail, export or error string gets judged there in full, because the design method does not carry those.
  • Bug fix → reproduce the bug first on the old behavior (or its regression test), then show it gone. A fix you never saw fail is a guess. Cause unknown, or a fix attempt already failed? Run the debug skill's loop before trying again.

3. What you cannot verify

Some things this environment can't exercise (production data, real payment, app-store review). Name them explicitly: "verified: A, B; not verifiable here: C, needs , tracked in STATE.md". Unverifiable-and-silent is how "works on my machine" ships.

Read the full file on GitHub · 52 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. 2d ago First seen · 52 lines · 46 tokens per session scan A 1ecef9506898

Subscribe to this mod's changes

verify is a skill published in the GitHub repository Tradebaas/Groundwork (2 stars, last pushed 7d ago), licensed MIT. It adds 46 tokens to every session and 737 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 2026-08-31.

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