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 bdfinst/agentic-dev-team --skill gherkin-publicgit clone --depth 1 https://github.com/bdfinst/agentic-dev-teamWrote 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/bdfinst/agentic-dev-team/gherkin-public)<a href="https://agentmods.dev/skills/bdfinst/agentic-dev-team/gherkin-public"><img src="https://agentmods.dev/badge/skills/bdfinst/agentic-dev-team/gherkin-public/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/bdfinst/agentic-dev-team/gherkin-public"><img src="https://agentmods.dev/badge/skills/bdfinst/agentic-dev-team/gherkin-public.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 | $0.00124 | $0.03462 |
| Opus 5.5 | $0.00050 | $0.01385 |
| Sonnet 5.5 | $0.00025 | $0.00692 |
| Haiku 4.5 | $0.00012 | $0.00346 |
Grade A, and why
gherkin-public 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 1mo 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 — 267 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Gherkin Public
Role: worker. Standalone Gherkin authoring skill. Reads the component map produced by /cd-test-architecture and writes .feature files at the public boundary of each component — the surface an external caller actually depends on. Internal steps are out of scope here; scenarios describe observable outputs.
You have been invoked with the /gherkin-public command.
Parse Arguments
Arguments: $ARGUMENTS
- Positional:
<repo-path>— the repo under modernization. --repo-slug <slug>— namespace under.claude/memory/<workflow>/. Defaults to the last path segment of<repo-path>.
If <repo-path> is absent, ask the operator.
Steps
1. Load the component map
Read .claude/memory/<workflow>/<slug>/phase-1.md for the components & patterns table. If it's missing, tell the operator Phase 1 has not run and stop.
2. Pick the output directory
- Prefer
features/<workflow>/if<repo>/features/already exists (matches the repo's existing Gherkin layout). - Otherwise write to
<repo>/specs/<workflow>/. - Create the directory if missing.
3. Author scenarios per public surface
For each component in the map, generate one .feature file per public surface using the pattern's template. Every scenario MUST cover at least one success and one failure path. Every scenario MUST be observable at the boundary — no scenario describes an internal call.
Grounding scenarios in real behavior. The component map names each surface but not its actual branches or error-handling depth. Prefer CodeGraph/Repowise over raw Grep to inspect a surface's real failure conditions before writing its failure scenario, rather than inferring one from the surface name alone — see knowledge/codegraph-vs-graphify.md for tool selection and the fallback contract.
Titles must be specific enough to identify the surface without relying on the Feature: header for context (issue #1526). A Scenario: title read in isolation — in a CI report, a BDD runner's scenario list, or a coverage dashboard — must be recognizable as belonging to its specific surface, not a generic category label that could describe any endpoint (e.g. returns error for invalid input, handles success case). Prefer wording that names the concrete condition or resource involved (e.g. rejects the request when the id path parameter is non-numeric, returns the created order with a 201 and Location header) over a bare category name. A title that is merely a paraphrase of the surface name (the same words as the Feature: line, reworded) fails this rule.
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.
- 1mo ago First seen · 267 lines · 124 tokens per session scan A d2e4c564641f
gherkin-public is a skill published in the GitHub repository bdfinst/agentic-dev-team (295 stars, last pushed 4d ago), licensed MIT. It adds 124 tokens to every session and 3,462 once invoked, about $0.0005 per session on Opus 5.5. 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 2026-09-03.
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