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.
git clone --depth 1 https://github.com/intellegix/intellegix-code-agent-toolkitWrote 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/intellegix/intellegix-code-agent-toolkit/implement)<a href="https://agentmods.dev/commands/intellegix/intellegix-code-agent-toolkit/implement"><img src="https://agentmods.dev/badge/commands/intellegix/intellegix-code-agent-toolkit/implement/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/commands/intellegix/intellegix-code-agent-toolkit/implement"><img src="https://agentmods.dev/badge/commands/intellegix/intellegix-code-agent-toolkit/implement.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00000 | $0.00995 |
| Opus 5.5 | $0.00000 | $0.00398 |
| Sonnet 5.5 | $0.00000 | $0.00199 |
| Haiku 4.5 | $0.00000 | $0.00100 |
Grade A, and why
implement 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 1mo 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 — 166 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Implement Feature
Implement the feature described: $ARGUMENTS
Implementation Process
Phase 0: Portfolio Gate
- Check project constraints
- Read
~/.claude/portfolio/PORTFOLIO.md - If Maintenance phase: ONLY bug fixes. Reject feature requests.
- If Prototype phase: Skip tests, skip types, skip CI.
- Match complexity to tier. T3/T4 = simplest possible implementation.
- Read
Phase 1: Planning (Types First)
-
Clarify Requirements
- What is the expected behavior?
- What are the acceptance criteria?
- Are there edge cases to consider?
- Who are the users of this feature?
-
Identify Scope
- Which files need to be created/modified?
- What new types/interfaces are needed?
- What's the data flow?
- Are there database changes needed?
-
Create Type Definitions
// TypeScript: src/types/[feature].ts export interface FeatureName { id: string; // ... properties }# Python: src/models/[feature].py from pydantic import BaseModel class FeatureName(BaseModel): id: str # ... fields
Phase 2: Implementation (Backend First)
-
Database Layer (if applicable)
- Create migrations
- Add models/schemas
- Update seed data if needed
-
Service Layer
- Implement business logic
- Use Result pattern for error handling
- Add logging at key points
-
API Layer (if applicable)
- Create endpoints following API patterns
- Add input validation (Zod/Pydantic)
- Implement proper error responses
Phase 3: Implementation (Frontend)
-
Components (if applicable)
- Create React components
- Add custom hooks for data fetching
- Implement state management
-
Integration
- Connect frontend to backend
- Add loading/error states
- Implement optimistic updates if needed
Phase 4: Testing
- Write Tests
# Unit tests for services # Integration tests for API # Component tests for UI
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.
- 1mo ago First seen · 166 lines · 0 tokens per session scan A 000b1544c239
implement is a command published in the GitHub repository intellegix/intellegix-code-agent-toolkit (57 stars, last pushed 4d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 995 tokens. 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
cost-optimize
Reduce cloud costs across AWS, Azure, and GCP through rightsizing, reserved and spot capacity, storage tuning, and cost monitoring.
refactor-clean
Refactor provided code for cleanliness, maintainability, and alignment with SOLID principles and modern best practices — no over-engineering.
deps-audit
Audit project dependencies for vulnerabilities, outdated packages, license conflicts, and supply chain risks — then provide actionable remediation strategies.
export-closedloop-learnings
Exports pending ClosedLoop learnings to global location with deduplication.
research-verify
Verify existing research findings against independent primary sources. Upgrades confidence from 'sources agree' to 'independently verified.'.
custom-brief
Deep-research a specific topic and produce a comprehensive news-focused briefing with optional Notion/Obsidian/Teams/Slack publishing.