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.
npx skills add sumithr/sumo-qa --skill sumo-qa-implementing-with-tddgit clone --depth 1 https://github.com/sumithr/sumo-qaWrote 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/sumithr/sumo-qa/sumo-qa-implementing-with-tdd)<a href="https://agentmods.dev/skills/sumithr/sumo-qa/sumo-qa-implementing-with-tdd"><img src="https://agentmods.dev/badge/skills/sumithr/sumo-qa/sumo-qa-implementing-with-tdd.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 3 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Tool Misuse · line 72 Tool parameters are crafted to achieve unintended or unsafe behavior. Parameter abuse can bypass intended safety checks (e.g. shell=True, --force, dangerous glob patterns).Fix: Validate all tool parameters against an allowlist. Reject dangerous parameter values (shell=True, --force, -rf /) and use safe defaults.
- medium Excessive Agency · line 49 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
- medium Excessive Agency · line 53 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00103 | $0.04207 |
| Opus 5 | $0.00051 | $0.02103 |
| Sonnet 5 | $0.00021 | $0.00841 |
| Haiku 4.5 | $0.00010 | $0.00421 |
Grade A, and why
sumo-qa-implementing-with-tdd 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 7d 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 — 148 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Implementing with TDD
Drive a change through TDD discipline: walk the cycle one step at a time, confirm the test idea before writing it, prove the red phase before handing back the green-making step. The user has product context (what "wrong" looks like, the API shape) the AI can't infer from code — surface it through questions, don't assume.
Announce at start: "Walking the red→green cycle."
Output discipline (mandatory)
Inherits the global discipline from using-sumo-qa: output discipline (never surface internal taxonomy labels — say "behaviour change in pricing", not "Classification: business_logic_change"), output economy (spend output on findings not framing; no preamble or self-narration; one question per turn; no closing pleasantries), knowledge authority hierarchy, internal scaffolding stays internal, and specialty-tool fit.
The Iron Law
RED PHASE FIRST. NO PRODUCTION CODE BEFORE A FAILING TEST. A test that has never failed has never tested anything — the red phase is the proof.
Stub allowance — narrow. A production-side stub is permitted in the red phase ONLY when the test can't otherwise be collected (e.g. the function under test doesn't exist yet, so the test file fails at import). It must be signature-only: def apply_discounts(order): raise NotImplementedError or : pass. Any behaviour in the stub — a partial implementation, a heuristic return, a branch that happens to satisfy the assertion — is an Iron Law violation: the red phase would then prove the stub matches the assertion, not that the test catches the bug. Writing if/else or computing a value in the stub → stop; that's green-phase work for the user.
When to Use
sumo-qa-deciding-approach routes here for: tdd-scaffold (greenfield-ish new behaviour), regression-first (bug fix — reproduce as a failing test first), or coverage-first-then-refactor (behaviour-preserving refactor — characterization tests pin behaviour BEFORE the refactor).
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.
- 7d ago First seen · 148 lines · 103 tokens per session scan A fb482ea71aea
sumo-qa-implementing-with-tdd is a skill published in the GitHub repository sumithr/sumo-qa (6 stars, last pushed 3d ago), licensed Apache-2.0. It adds 103 tokens to every session and 4,207 once invoked, about $0.0005 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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