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/DauQuangThanh/hanoi-rainbowWrote 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/dauquangthanh/hanoi-rainbow/assess-context)<a href="https://agentmods.dev/commands/dauquangthanh/hanoi-rainbow/assess-context"><img src="https://agentmods.dev/badge/commands/dauquangthanh/hanoi-rainbow/assess-context/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/dauquangthanh/hanoi-rainbow/assess-context"><img src="https://agentmods.dev/badge/commands/dauquangthanh/hanoi-rainbow/assess-context.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 | $0.00017 | $0.02911 |
| Opus 5.5 | $0.00007 | $0.01164 |
| Sonnet 5.5 | $0.00003 | $0.00582 |
| Haiku 4.5 | $0.00002 | $0.00291 |
Grade A, and why
assess-context 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 2mo 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 — 418 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 codebase context assessment') and commit context-assessment.md upon completion.
-
Setup: Run
{SCRIPT}from repo root and parse JSON for CONTEXT_ASSESSMENT, DOCS_DIR, REPO_ROOT, HAS_GIT. -
Load context: Read
memory/ground-rules.md(if exists),docs/architecture.md(if exists),docs/standards.md(if exists). Load CONTEXT_ASSESSMENT template (already copied). Adhere to the principles for maximizing system clarity, structural simplicity, and long-term maintainability. -
Execute context assessment workflow: Follow the structure in CONTEXT_ASSESSMENT template to:
- Identify technology stack and frameworks
- Analyze project structure and organization
- Document architectural patterns and design decisions
- Extract coding conventions and standards
- Map key components and their relationships
- Identify data models and storage patterns
- Document API contracts and integration patterns
- Assess testing approach and coverage
- Review build and deployment processes
- Calculate technical health score
-
Update agent context: Run
{AGENT_SCRIPT}to update agent-specific context with assessment findings. -
Stop and report: Command ends after assessment completion. Report CONTEXT_ASSESSMENT path and technical health score.
Phases
Phase 0: Technology Stack Discovery
-
Identify core technologies:
- Programming languages and versions (package.json, requirements.txt, pom.xml, etc.)
- Frameworks and major libraries
- Runtime environment (Node.js, Python, Java, .NET, etc.)
- Database systems (PostgreSQL, MongoDB, Redis, etc.)
- Build tools (npm, pip, Maven, Gradle, etc.)
-
Document development environment:
- Required tools and versions
- Environment setup requirements
- Local development scripts
- Configuration management approach
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.
- 2mo ago First seen · 418 lines · 17 tokens per session scan A 17d377ed3954
assess-context is a command published in the GitHub repository DauQuangThanh/hanoi-rainbow (17 stars, last pushed 9mo ago), licensed MIT. It adds 17 tokens to every session and 2,911 once invoked, about $0.0001 per session on Opus 5.5. 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.
Other commands, from other repositories
sddp-qc
Command description: Run quality control against the implemented feature. Argument hint: [optional: testing focus such as unit tests, security audit, requirements sync] Command category: feature-delivery Prerequisites: spec, plan, tasks, implementation:complete.
refactor-clean
Refactor provided code for cleanliness, maintainability, and alignment with SOLID principles and modern best practices — no over-engineering.
ingest-cwe-taxonomies
Ingest CWE XML from MITRE and generate per-domain security audit taxonomies for PromptKit.
review
A command for reviewing code with security checks and a check for compliance with MX tags. The available description does not explain what MX tags are.
post
Post the current OCR review to a GitHub PR.
default-ai-review
Extract findings from the provided diff. Rank by severity. Produce a structured review. Emit one recommendation.