Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add bdfinst/agentic-dev-team/plugin install 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-derive)<a href="https://agentmods.dev/skills/bdfinst/agentic-dev-team/gherkin-derive"><img src="https://agentmods.dev/badge/skills/bdfinst/agentic-dev-team/gherkin-derive/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-derive"><img src="https://agentmods.dev/badge/skills/bdfinst/agentic-dev-team/gherkin-derive.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 | $0.00134 | $0.08634 |
| Opus 5.5 | $0.00054 | $0.03454 |
| Sonnet 5.5 | $0.00027 | $0.01727 |
| Haiku 4.5 | $0.00013 | $0.00863 |
Grade A, and why
gherkin-derive 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 — 631 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Gherkin Derive
Role: worker. A standalone Gherkin derivation skill. It derives scenarios for
a repo's public surface directly from code — it does not require any prior
/cd-test-architecture analysis, and it does not create tracker Stories
(the calling orchestrator owns triage). /gherkin-public remains a
public-boundary Gherkin authoring worker used standalone.
Usable two ways: on its own to capture intended behavior before any test change,
or as the Phase-3 sub-step of /test-improve.
Parse Arguments
- Positional:
<repo-path>(default: cwd). --mode <none|xunit-with-annotations|bdd-runner>— the binding mode. When omitted, present the BDD value rubric (Step 1) and let the operator choose.--repo-slug <slug>— namespace for the surface inventory under.claude/memory/<workflow>/<slug>/(/test-improvepasses--workflow test-improve).
Step 1 — Choose the binding mode (BDD value rubric)
If --mode was not supplied, present the 5-question rubric from
knowledge/references/bdd-value-guide.md and recommend a mode from the score:
≥ 3 yes→bdd-runner1–2 yes→xunit-with-annotations0 yes→none
Show the recommendation and let the operator override. The three modes:
none— emit no Gherkin. Exit immediately with a one-line recommendation to use plain xUnit (e.g. "0/5 BDD signals — use plain xUnit; no.featurefiles written."). Write no files.xunit-with-annotations— derive scenarios and write.featurefiles, but do NOT wire a BDD runner or install any framework. The files are documentation that/buildcites in test method names and leading comments.bdd-runner— derive scenarios, write.featurefiles, wire the language-appropriate BDD framework (Step 4), and generate pending step definition stubs.
Step 2 — Discover the public surface
No pre-computed component map is required. Discover surfaces in this priority order, most authoritative first:
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 · 631 lines · 134 tokens per session scan A a0abd61d99eb
gherkin-derive is a skill published in the GitHub repository bdfinst/agentic-dev-team (295 stars, last pushed 4d ago), licensed MIT. It adds 134 tokens to every session and 8,634 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.
Other skills, from other repositories
research-engineer
An uncompromising Academic Research Engineer. Operates with absolute scientific rigor, objective criticism, and zero flair. Focuses on theoretical correctness, formal verification, and optimal implementation across any required technology.
eval-dataset-design
A guide to creating reliable test sets for AI agents. Each evaluation task includes the request, the simulated user's behaviour, a reset starting state, and a success check that can be independently verified.
tika-eval-compare
Compare extracts from two Tika builds over a corpus to detect regressions in content, encoding, exceptions, and embedded-document handling. Use for "compare before/after extracts", "eval this change against the corpus".
cua-driver
Use Cua Driver for desktop or browser tasks that are awkward or unavailable through Bash/APIs, or when the user explicitly wants GUI interaction: app testing, visual bug reproduction, form filling, calendar entry, screenshots, and demo recording. Also covers Cua setup; not OpenAI Codex Computer Use or web research.
jetson-validate-image
Use after jetson-flash-image to run static BSP checks, on-target smoke/regression tests on a flashed DUT, or both. Not for build or flash steps. Triggers: validate bsp, on-target validation.
atmos-validation
Validate Atmos projects, components, arbitrary JSON Schema inputs, EditorConfig, and GitHub Actions; use affected-file selection and native CI annotations.