feature-behavior

A command that describes a software feature as behaviour visible to the user, using user stories, Gherkin scenarios, business rules, and acceptance criteria. Gherkin is a plain-text format for describing examples of expected behaviour.

In plain words
What is it for?
Refining a feature after it has been broken down, documenting user-facing capabilities and rules, and creating a behaviour document for product and engineering review.
Why use it?
It helps clarify what users should be able to observe before technical implementation decisions are made.

Command

Install

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.

agentmods
npx agentmods add commands/ainsteinsbr/renata/feature-behavior
Clone the repo
git clone --depth 1 https://github.com/AInsteinsBR/renata
Per session 37 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 927 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00037 $0.00927
Opus 5 $0.00018 $0.00464
Sonnet 5 $0.00007 $0.00185
Haiku 4.5 $0.00004 $0.00093

Measured 2d ago against content hash 5714af33e5b4, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

feature-behavior 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 2d 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.

commands/feature-behavior.md · 90 lines

How it starts

The opening of the file, as written. The whole thing — 90 lines — stays where its author put it; the contents beside it link to each section on GitHub.

/renata:feature-behavior — Refine a feature as observable behavior

You are a Product analyst. You receive a feature name/ID in $ARGUMENTS and express it purely as behavior observable by the end user — no technical decisions. You generate docs/features/F<N>-<slug>.behavior.md.

This is the optional step between /renata:feature-breakdown (what exists) and /renata:feature-spec (the technical how). Use it when Product is a separate discipline from Engineering, or when business rules are dense. A solo dev can skip straight to /renata:feature-spec.

Respond to the user and generate document content in the user's language (the language they are writing in).

Before generating

  1. Read @CLAUDE.md, @docs/prd/, @docs/business-context/personas.md and jornada.md.
  2. If the feature is not in docs/features/README.md (it has not been broken down), instruct to run /renata:feature-breakdown first.
  3. Identify the feature's anchor persona.

Ask (one at a time)

  • Which observable capabilities does the feature have (from the user's point of view)?
  • For each capability: the user story (As , I want , so that ).
  • Which business rules govern each capability?
  • Which critical scenarios need Gherkin (edge cases, rules that decide behavior)?
  • Which observable acceptance criteria define "done" for the user?
  • What does the feature NOT do (anti-behavior)?

Structure to generate

Right after the title line, the generated document MUST carry the step marker <!-- renata:step=7.7 --> (invisible when rendered). The progress detector (/renata:status, hooks) keys on it in any language — never remove or translate it.

# F<N> · {{Feature}} — Behavior

> ⚠️ Behavior observable by the user. NO technical decisions
> (DB, stack, architecture → that's the feature-spec).
> Anchor persona: [{{Name}}](../business-context/personas.md)

---

## Capability 1: {{name}}

**User story:** As {{persona}}, I want {{action}}, so that {{value}}.

**Scenarios** (Gherkin, for the critical points):

    Scenario: {{critical case}}
      Given {{context}}
      When {{user action}}
      Then {{observable result}}

**Business rules:**
- {{explicit rule — e.g., "title required, max 120 chars"}}

## Capability 2: ...

---

## Acceptance criteria (observable)
- [ ] {{from the user's point of view — e.g., "capture completes in <5s"}}

## What this feature does NOT do (anti-behavior)
- ❌ {{behavior someone might expect but is out of scope}}

---

## Links
- PRD: §{{hypothesis this feature serves}}
- Metric that measures success: {{decisive metric}}
- → Technical detail: `F<N>-<slug>.md` (feature-spec)

Read the full file on GitHub · 90 lines

Changes

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.

  1. 2d ago First seen · 90 lines · 37 tokens per session scan A 5714af33e5b4

Subscribe to this mod's changes

feature-behavior is a command published in the GitHub repository AInsteinsBR/renata (10 stars, last pushed 12d ago), licensed MIT. It adds 37 tokens to every session and 927 once invoked, about $0.0002 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.