Borrowing it
Nothing to install: this file belongs to Parslee-ai/neo. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/Parslee-ai/neo/main/.claude/commands/feature-autonomous.mdgit clone --depth 1 https://github.com/Parslee-ai/neoWrote 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/commands/parslee-ai/neo/feature-autonomous)<a href="https://agentmods.dev/commands/parslee-ai/neo/feature-autonomous"><img src="https://agentmods.dev/badge/commands/parslee-ai/neo/feature-autonomous.svg" alt="Measured on agentmods" 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 | $0.00022 | $0.01949 |
| Opus 5 | $0.00011 | $0.00975 |
| Sonnet 5 | $0.00004 | $0.00390 |
| Haiku 4.5 | $0.00002 | $0.00195 |
Grade A, and why
feature-autonomous 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 8d 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 — 234 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Autonomous Feature Planning & Implementation
Generate a feature implementation plan using multi-agent research (Phase 0 from quick-ship-neo), then implement with full validation loop.
Instructions
- You're autonomously planning and implementing a feature using Phase 0 research
- Phase 0: Use 3 agents to research and generate O1-O4 implementation options
- CRITICAL: Phase 0 MUST output explicit Test Specification
- After planning, automatically proceed to /implement with the generated plan
Feature
$ARGUMENTS
Phase 0: PreflightExplain
Step 0.1: Parse Feature
- Extract feature description from $ARGUMENTS
- Classify type (new functionality, enhancement, integration, infrastructure)
- Define acceptance criteria (feature is complete when...)
- Identify user story components (who, what, why)
Step 0.2: Codebase Research
- INVOKE Task tool:
- subagent_type: "codebase-researcher"
- description: "Research codebase for feature context"
- prompt: "Research the codebase for feature: {feature_description}. Identify: (1) Existing patterns and conventions to follow, (2) Files that will need modification, (3) Similar features already implemented, (4) Architecture constraints and opportunities, (5) Dependencies and integration points. Provide structured report with file paths, line numbers, and code patterns."
Step 0.3: Feature Analysis
- INVOKE Task tool:
- subagent_type: "debug-detective"
- description: "Deep feature analysis"
- prompt: "Analyze feature requirements: {feature_description}. Investigate: (1) Core functionality needed, (2) Edge cases and error scenarios, (3) User experience considerations, (4) Performance implications, (5) Security considerations, (6) Complexity assessment. Provide comprehensive analysis."
Step 0.4: Generate Implementation Options
- INVOKE Task tool:
- subagent_type: "linus-kernel-planner"
- description: "Generate O1-O4 implementation options"
- prompt: "Create feature implementation plan for: {feature_description}. Generate exactly 4 options (O1-O4), each with: approach overview, files to create/modify, complexity assessment (LOC, risk level), architectural implications, risks and tradeoffs. Include: (1) Feature Assessment, (2) Rejected Approaches (over-engineered solutions), (3) Four Implementation Options with tradeoffs, (4) Recommended Solution (simplest that meets requirements), (5) What NOT to Do (avoid complexity). Follow existing patterns. Prefer simple, extensible solutions."
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.
- 8d ago First seen · 234 lines · 22 tokens per session scan A 09cc4cc1a694
feature-autonomous is a command published in the GitHub repository Parslee-ai/neo (16 stars, last pushed today), licensed Apache-2.0. It adds 22 tokens to every session and 1,949 once invoked, about $0.0001 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.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.