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 agents/dauquangthanh/sso-mcp-server/rainbow.taskifygit clone --depth 1 https://github.com/DauQuangThanh/sso-mcp-serverWrote 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/agents/dauquangthanh/sso-mcp-server/rainbow.taskify)<a href="https://agentmods.dev/agents/dauquangthanh/sso-mcp-server/rainbow.taskify"><img src="https://agentmods.dev/badge/agents/dauquangthanh/sso-mcp-server/rainbow.taskify.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 | $0.00018 | $0.01704 |
| Opus 5 | $0.00009 | $0.00852 |
| Sonnet 5 | $0.00004 | $0.00341 |
| Haiku 4.5 | $0.00002 | $0.00170 |
Grade A, and why
rainbow.taskify 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.
This is a copy
75% identical to speckit.tasks — 106 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 151 lines — stays where its author put it; the contents beside it link to each section on GitHub.
User Input
$ARGUMENTS
You MUST consider the user input before proceeding (if not empty).
Outline
IMPORTANT: Automatically generate a 'docs:' prefixed git commit message (e.g., 'docs: add implementation tasks for feature-name') and commit tasks.md upon completion.
-
Setup: Run
.rainbow/scripts/bash/check-prerequisites.sh --jsonfrom repo root and parse FEATURE_DIR and AVAILABLE_DOCS list. All paths must be absolute. For single quotes in args like "I'm Groot", use escape syntax: e.g 'I'''m Groot' (or double-quote if possible: "I'm Groot"). -
Load design documents: Read from FEATURE_DIR:
- Required: design.md (tech stack, libraries, structure), spec.md (user stories with priorities)
- Optional: data-model.md (entities), contracts/ (API endpoints), research.md (decisions), quickstart.md (test scenarios)
- Product-level:
docs/architecture.md(if exists - architectural patterns, ADRs, deployment architecture) - Note: Not all projects have all documents. Generate tasks based on what's available.
-
Execute task generation workflow:
- Load design.md and extract tech stack, libraries, project structure
- Load spec.md and extract user stories with their priorities (P1, P2, P3, etc.)
- If
docs/architecture.mdexists: Extract architectural patterns, deployment requirements, and ADRs relevant to implementation - If data-model.md exists: Extract entities and map to user stories
- If contracts/ exists: Map endpoints to user stories
- If research.md exists: Extract decisions for setup tasks
- Generate tasks organized by user story (see Task Generation Rules below)
- Generate dependency graph showing user story completion order
- Create parallel execution examples per user story
- Validate task completeness (each user story has all needed tasks, independently testable)
- Ensure tasks align with architectural decisions and patterns from architecture.md (if available)
-
Generate tasks.md: Use
.rainbow.rainbow/templates/tasks-template.mdas structure, fill with:- Correct feature name from design.md
- Phase 1: Setup tasks (project initialization, aligned with deployment architecture if defined)
- Phase 2: Foundational tasks (blocking prerequisites for all user stories, following architectural patterns)
- Phase 3+: One phase per user story (in priority order from spec.md)
- Each phase includes: story goal, independent test criteria, tests (if requested), implementation tasks
- Final Phase: Polish & cross-cutting concerns
- All tasks must follow the strict checklist format (see Task Generation Rules below)
- Clear file paths for each task (following code organization from architecture.md if available)
- Dependencies section showing story completion order
- Parallel execution examples per story
- Implementation strategy section (MVP first, incremental delivery)
- Architecture alignment notes (if architecture.md exists)
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 · 151 lines · 18 tokens per session scan A 52cadcffc988
rainbow.taskify is an agent published in the GitHub repository DauQuangThanh/sso-mcp-server (0 stars, last pushed 8mo ago), licensed MIT. It adds 18 tokens to every session and 1,704 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 75% identical to speckit.tasks, differing in 106 lines, and is treated as a copy.
Other agents, from other repositories
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
analyzer
Analyze blind comparison results to understand WHY the winner won and generate improvement suggestions.
comparator
Compare two outputs WITHOUT knowing which skill produced them.
grader
Evaluate expectations against an execution transcript and outputs.
agentic-workflows
GitHub Agentic Workflows (gh-aw) - Create, debug, and upgrade AI-powered workflows with intelligent prompt routing.