Nw Jtbd Bdd IntegrationSAFE
AI agents that guide you from idea to working code, with you in control at every step.
Overview
AI agents that guide you from idea to working code, with you in control at every step.
1a864ec81cdeOBSERVED · 2026-10-14What it tells the agent
The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.
--- name: nw-jtbd-bdd-integration description: Translating JTBD analysis to BDD scenarios - job story to Given-When-Then patterns, forces-based test discovery, job-map-based test discovery, and property-shaped criteria user-invocable: false disable-model-invocation: true --- # JTBD to BDD Integration Use when translating JTBD discoveries (job stories, forces, job map) into testable BDD scenarios. Job stories map naturally to Given-When-Then because both emphasize context (situation/given), action (motivation/when), and outcome (result/then). ## Job Story to Given-When-Then Translation ### The Pattern ``` Job Story: When [situation], I want to [motivation], so I can [outcome]. BDD Scenario: Given [the situation from the job story] When [user takes action aligned with the motivation] Then [the outcome is achieved and measurable] ``` ### Worked Example **Job Story**: "When I receive an alert that a production service is degraded, I want to see the most likely root cause with supporting evidence, so I can begin remediation within minutes instead of hours." ```gherkin Scenario: Root cause surfaced from degradation alert Given the monitoring system has detected degraded response times on the payment service And the degradation started 5 minutes ago When the operator opens the incident dashboard Then the system displays the top 3 probable root causes ranked by likelihood And each root cause includes supporting metrics and log evidence And the most likely root cause is displayed within 10 seconds Scenario: Remediation guidance from identified root cause Given the operator has identified the root cause as database connection pool exhaustion When the operator selects the recommended remediation action Then the system presents a runbook with step-by-step instructions And the estimated time to remediate is displayed And the operator can execute the first step directly from the dashboard ``` ### Translation Rules 1. **Given** = situation from job story, made concrete with specific data 2. **When** = user action fulfilling the motivation 3. **Then** = measurable outcome, including quality attributes (time, confidence, completeness) ### Multi-Scenario Expansion One job story typically produces 3-5 BDD scenarios: - **Happy path**: Outcome achieved as described - **Anxiety path**: User's fear about new solution addressed - **Habit path**: User transitioning from old workflow supported - **Edge cases**: Derived from job map steps (see below) ## Forces-Based Test Discovery Use Four Forces to discover test scenarios that feature-driven thinking misses. ### Force-to-Scenario Mapping | Force | Test Question | Scenario Pattern | |-------|--------------|-----------------| | **Push** | What frustration drove the user here? | `Given [user has experienced the frustration]...` | | **Pull** | What outcome is the user hoping for? | `Then [the promised improvement is measurable]` | | **Anxiety** | What could go wrong with the new solution? | `Given [user has specific fear] When [fear-triggering event] Then [safety net activates]` | | **Habit** | What existing workflow might resist adoption? | `Given [user accustomed to old way] When [new approach encountered] Then [familiar patterns preserved]` | ### Worked Example: Automated Deployment Tool **Forces analysis**: - Push: Manual deployments cause errors and take hours - Pull: One-command deployment with automatic rollback - Anxiety: "What if the automated rollback breaks worse than the original deploy?" - Habit: Team SSH-es into servers and runs scripts manually **Derived scenarios**: ```gherkin # Push scenario: validates the frustration is resolved Scenario: Deployment completes without manual intervention Given the operator has configured deployment for the payment service And the previous manual deployment took 3 hours with 2 errors When the operator runs the automated deployment command Then deployment completes within 15 minutes And zero manual steps are required # Pull scenario: validates the promised outcome Scenario: Automatic rollback on health check failure Given the deployment has completed and health checks are running When the payment endpoint returns 500 errors for 30 seconds Then the system automatically rolls back to the previous version And the operator receives a notification with rollback details # Anxiety scenario: addresses the specific fear Scenario: Rollback preserves database state Given the deployment included a database migration And the health check has triggered an automatic rollback When the rollback completes Then the database schema matches the previous stable version And no data is lost during the rollback process And the operator can verify data integrity from the dashboard # Habit scenario: eases the transition Scenario: Manual override available during automated deployment Given the operator is accustomed to SSH-based manual deployments When the automated deployment is in progress Then the operator can view real-time logs (familiar output format) And the operator can pause or cancel the deployment at any stage And manual SSH access remains available as a fallback ``` ## Job-Map-Based Test Discovery Walk each of the 8 job map steps and generate at least one test scenario per step. Surfaces edge cases that happy-path thinking misses. | Job Map Step | Missing Scenario Pattern | Example | |-------------|------------------------|---------| | **Define** | User lacks information to start | `Given the user has not reviewed the change log When they attempt to deploy Then the system warns about unreviewed changes` | | **Locate** | Required inputs unavailable or stale | `Given the build artifact is 30 days old When the user selects it for deployment Then the system flags the artifact as potentially stale` | | **Prepare** | Setup fails or environment misconfigured | `Given the target environment has a port conflict When preparation runs Then the s
Trust audit
SAFEgrade B · trust 89/100 Nothing in the source contradicts what it says it does. Grade A is reserved for packages that have also passed the behavioural sandbox.
| Layer | What it checks | Result |
|---|---|---|
| L0 | Provenance & inventory | PASS |
| L1 | Static analysis of the code | NA |
| L2 | Instruction surface (what it tells the agent) | PASS |
| L3 | Class-specific surface | PASS |
| L4 | Behavioural (sandbox) | SKIPPED |
What the source does
- Filesystem
- none-observed
- Network
- none-observed
- Shell
- none-observed
- Dependencies
- pinned
- Secrets in source
- none-found
Findings (0)
No findings outside the package's declared scope.
Gates applied: no_behavioural_pass.
1a864ec81cdefull audit observations/trust-audit/skill/nwave-ai__nw-jtbd-bdd-integration.json · Report an issue / request a re-scanAudit history
Every audit this skill has had.
| Date | Source | Verdict | Grade | Score | Change |
|---|---|---|---|---|---|
| 2026-10-14 | 1a864ec81cde | SAFE | B | 89 | first audit |
Questions
What does the Nw Jtbd Bdd Integration skill do?
AI agents that guide you from idea to working code, with you in control at every step.
Is Nw Jtbd Bdd Integration safe to install?
The audit found nothing in the source that contradicts what it says it does, and graded it B (89/100). Grade A is held back for packages that have also passed a sandboxed behavioural run, which is why a clean skill reads B.
What can Nw Jtbd Bdd Integration access on my machine?
The audit observed no filesystem, network or shell use at all in its source.
How current is this page?
The grade is for one exact copy of the source (1a864ec81cde), read on 2026-10-14. The repository is watched, and a new audit runs when it changes — this is the first audit.