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 05-deepak-patidar/claude-skills --skill ai-build-qualitygit clone --depth 1 https://github.com/05-deepak-patidar/claude-skillsWrote 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/05-deepak-patidar/claude-skills/ai-build-quality)<a href="https://agentmods.dev/skills/05-deepak-patidar/claude-skills/ai-build-quality"><img src="https://agentmods.dev/badge/skills/05-deepak-patidar/claude-skills/ai-build-quality/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/05-deepak-patidar/claude-skills/ai-build-quality"><img src="https://agentmods.dev/badge/skills/05-deepak-patidar/claude-skills/ai-build-quality.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00105 | $0.01146 |
| Opus 5 | $0.00053 | $0.00573 |
| Sonnet 5 | $0.00021 | $0.00229 |
| Haiku 4.5 | $0.00011 | $0.00115 |
Grade A, and why
ai-build-quality 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 12d 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 — 45 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Build Quality
An AI model is a brilliant contractor with amnesia: enormous skill, zero memory of your project beyond what you hand it, and a bias toward plausible over correct. Quality is therefore not a property of the model — it's a property of your packaging, constraints, and verification. This works for any model; better models raise the ceiling, this process raises the floor.
Law 1: The spec is the product
Vague in, plausible-looking garbage out. Before delegating any non-trivial task, package it as:
- Goal: the user-visible outcome, one paragraph. Not "add validation" but "reject payments exceeding the invoice balance with error code PAYMENT_EXCEEDS_BALANCE".
- Boundaries: what NOT to touch/change ("don't modify the schema", "keep the public API stable", "no new dependencies"). Models over-help; unfenced tasks sprawl. Explicitly state what's out of scope.
- Ground truth: point at the files/patterns that define "how we do it here" ("follow the pattern in
payments_service.py", "errors usecore/errors.py"). A model imitates what it sees; show it the best neighbor, or it imports its training-data habits. - Acceptance checks: how you'll verify — the command to run, the behavior to demo, the test that must pass. If you can't state the check, you can't detect failure.
- Constraints that survive: invariants worth repeating every time (money is Decimal, all writes tenant-scoped, one transaction per use case). Repetition is cheap; corruption isn't.
Law 2: Persistent project memory beats per-prompt heroics
Maintain a project instructions file (CLAUDE.md / AGENTS.md / equivalent — every serious tool reads one) containing what every task needs: run/test/deploy commands, architecture's non-obvious parts, conventions enforced, gotchas that already burned you. Rules of thumb: if you've corrected the model twice for the same thing, it goes in the file; keep it curated and short (a bloated file gets skimmed by models too); update it in the same commit as the change that made it stale.
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.
- 12d ago First seen · 45 lines · 105 tokens per session scan A 16e2707a0ec4
ai-build-quality is a skill published in the GitHub repository 05-deepak-patidar/claude-skills (4 stars, last pushed 2mo ago), licensed MIT. It adds 105 tokens to every session and 1,146 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.
Other skills, from other repositories
plan-pipeline-execute
Execute a validated plan: worktree isolation, TDD scaffolding, level-based parallel agents, quality gate with smoke test, PR creation and merge. Handles everything through to merged PR.
qa
Systematic QA testing of a web application: diff-aware, tiered, with fix-and-verify loop.
plan-pipeline-eng-review
Engineering architecture gate: lock architecture, diagrams, edge cases, and test matrix before writing implementation code.
ci-all
Full CI pipeline: run local tests, type check, push branch, and return the pipeline URL. The only command you need before opening a PR.
ci-tests
Run the test suite for the current repo, auto-detecting Python (pytest/uv), Node (vitest/pnpm), or Rust (cargo test).
generate-tests
Generate comprehensive tests for specified code.