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 pproenca/dot-skills --skill acceptance-pipeline-feature-designgit clone --depth 1 https://github.com/pproenca/dot-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/pproenca/dot-skills/acceptance-pipeline-feature-design)<a href="https://agentmods.dev/skills/pproenca/dot-skills/acceptance-pipeline-feature-design"><img src="https://agentmods.dev/badge/skills/pproenca/dot-skills/acceptance-pipeline-feature-design/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/pproenca/dot-skills/acceptance-pipeline-feature-design"><img src="https://agentmods.dev/badge/skills/pproenca/dot-skills/acceptance-pipeline-feature-design.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 | $0.00111 | $0.01230 |
| Opus 5 | $0.00056 | $0.00615 |
| Sonnet 5 | $0.00022 | $0.00246 |
| Haiku 4.5 | $0.00011 | $0.00123 |
Grade A, and why
acceptance-pipeline-feature-design 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 11d 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 — 95 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Acceptance Pipeline Feature Design
Guides agents through designing new features that extend Uncle Bob's Acceptance Pipeline Specification. Produces spec-quality output — language-neutral, implementation-agnostic, with precise behavioral requirements — that matches the style and rigor of the original spec.
This is a composition skill. It does not catalog what exists (that is acceptance-pipeline-catalog's job). Instead, it provides a structured workflow for designing what comes next.
When to Apply
- Designing a new mutation strategy, value type, or filter mechanism for the mutator
- Adding new Gherkin syntax support (data tables, tags, Rules keyword) to the parser
- Extending the JSON IR with new fields or objects
- Creating new report formats (HTML, JUnit XML) or output channels
- Adding new pipeline stages or operating modes (parallel runs, coverage filtering)
- Any spec-level change that affects multiple pipeline components (cross-cutting)
Prerequisite
Before using this skill, ensure acceptance-pipeline-catalog is available. That skill provides the baseline reference for the 9 required components, JSON IR schema, conformance checklist (21 items), and agent setup checklist (15 steps). This skill builds on top of that foundation.
Workflow Overview
The workflow has five phases, executed in order. Each phase builds on the output of the previous one — skipping phases produces incomplete or inconsistent designs.
Phase 1: Survey Existing Spec → Know what exists before proposing changes
Phase 2: Identify Extension Point → Classify where the feature attaches
Phase 3: Draft Feature Spec → Write the spec section in Uncle Bob's style
Phase 4: Conformance Design → Add testable conformance items
Phase 5: Impact Analysis → Assess backward compatibility and migration
Why this order matters:
- Phase 1 prevents reinventing existing capabilities and ensures the design uses established patterns.
- Phase 2 forces classification before writing — a parser extension has different constraints than a reporter extension.
- Phase 3 produces the actual spec text, informed by the classification from Phase 2.
- Phase 4 ensures the feature is testable from outside the implementation — if you cannot write conformance items, the spec is too vague.
- Phase 5 comes last because you need the complete spec and conformance items to assess impact accurately.
What ships with it
10 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- gotchas.md 36 B
- metadata.json 672 B
- references/extension-catalog.md 8.5 KB
- references/phase-1-survey.md 3.9 KB
- references/phase-2-extension-point.md 4.8 KB
- references/phase-3-draft-spec.md 6.7 KB
- references/phase-4-conformance.md 5.3 KB
- references/phase-5-impact-analysis.md 6.3 KB
- references/style-guide.md 7.3 KB
- scripts/README.md 195 B
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.
- 11d ago First seen · 95 lines · 111 tokens per session scan A b7b7931c5d74
acceptance-pipeline-feature-design is a skill published in the GitHub repository pproenca/dot-skills (206 stars, last pushed 26d ago), licensed MIT. It adds 111 tokens to every session and 1,230 once invoked, about $0.0006 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 2026-08-30.
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