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 agentmods add commands/thesmokedev/taskchad-os/plan-featuregit clone --depth 1 https://github.com/TheSmokeDev/taskchad-osWrote 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/thesmokedev/taskchad-os/plan-feature)<a href="https://agentmods.dev/commands/thesmokedev/taskchad-os/plan-feature"><img src="https://agentmods.dev/badge/commands/thesmokedev/taskchad-os/plan-feature.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 | $0.00011 | $0.02628 |
| Opus 5 | $0.00005 | $0.01314 |
| Sonnet 5 | $0.00002 | $0.00526 |
| Haiku 4.5 | $0.00001 | $0.00263 |
Grade A, and why
plan-feature 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 5d 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 — 433 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Plan a new task
Feature: $ARGUMENTS
Mission
Transform a feature request into a comprehensive implementation plan through systematic codebase analysis, external research, and strategic planning.
Core Principle: We do NOT write code in this phase. Our goal is to create a context-rich implementation plan that enables one-pass implementation success for ai agents.
Key Philosophy: Context is King. The plan must contain ALL information needed for implementation - patterns, mandatory reading, documentation, validation commands - so the execution agent succeeds on the first attempt.
Planning Process
Phase 1: Feature Understanding
Deep Feature Analysis:
- Extract the core problem being solved
- Identify user value and business impact
- Determine feature type: New Capability/Enhancement/Refactor/Bug Fix
- Assess complexity: Low/Medium/High
- Map affected systems and components
Create User Story Format Or Refine If Story Was Provided By The User:
As a <type of user>
I want to <action/goal>
So that <benefit/value>
Phase 2: Codebase Intelligence Gathering
Use specialized agents and parallel analysis:
1. Project Structure Analysis
- Detect primary language(s), frameworks, and runtime versions
- Map directory structure and architectural patterns
- Identify service/component boundaries and integration points
- Locate configuration files (pyproject.toml, package.json, etc.)
- Find environment setup and build processes
2. Pattern Recognition (Use specialized subagents when beneficial)
- Search for similar implementations in codebase
- Identify coding conventions:
- Naming patterns (CamelCase, snake_case, kebab-case)
- File organization and module structure
- Error handling approaches
- Logging patterns and standards
- Extract common patterns for the feature's domain
- Document anti-patterns to avoid
- Check CLAUDE.md for project-specific rules and conventions
3. Dependency Analysis
- Catalog external libraries relevant to feature
- Understand how libraries are integrated (check imports, configs)
- Find relevant documentation in docs/, ai_docs/, .agents/reference or ai-wiki if available
- Note library versions and compatibility requirements
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.
- 5d ago First seen · 433 lines · 11 tokens per session scan A 202d9894dac1
plan-feature is a command published in the GitHub repository TheSmokeDev/taskchad-os (23 stars, last pushed 12d ago), licensed MIT. It adds 11 tokens to every session and 2,628 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
tokenless-stats
Show Tokenless compression statistics.
migrate_plans
Automatically review all plans in .cursor/plans/, determine relevance, and migrate relevant ones to docs/proposals/ for future consideration.
graphify
Turn your vault into a clustered knowledge graph with HTML and JSON outputs.
music-suno-prompt
Grounded Suno prompt synthesis from local knowledge corpus + persona canon + label canon. No vibes-prompting.
scorecard
Run a repeatable weekly health check. Score Brain OS out of 100. Compare week-over-week.
export-project
Export a single registered Gnosys project to a portable .json.gz bundle (round-trips with gnosys import project).