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 skills/act-sdk/act-sdk-js/colorizenpx skills add act-sdk/act-sdk-js --skill colorizegit clone --depth 1 https://github.com/act-sdk/act-sdk-jsWrote 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/skills/act-sdk/act-sdk-js/colorize)<a href="https://agentmods.dev/skills/act-sdk/act-sdk-js/colorize"><img src="https://agentmods.dev/badge/skills/act-sdk/act-sdk-js/colorize.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 | $0.00025 | $0.01671 |
| Opus 5 | $0.00013 | $0.00835 |
| Sonnet 5 | $0.00005 | $0.00334 |
| Haiku 4.5 | $0.00003 | $0.00167 |
Grade A, and why
colorize 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
88% identical to colorize — 27 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 — 158 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Strategically introduce color to designs that are too monochromatic, gray, or lacking in visual warmth and personality.
MANDATORY PREPARATION
Context Gathering (Do This First)
You cannot do a great job without having necessary context, such as target audience (critical), desired use-cases (critical), brand personality/tone, and especially existing brand colors.
Attempt to gather these from the current thread or codebase.
- If you don't find exact information and have to infer from existing design and functionality, you MUST STOP and STOP and call the AskUserQuestionTool to clarify. whether you got it right.
- Otherwise, if you can't fully infer or your level of confidence is medium or lower, you MUST STOP and call the AskUserQuestionTool to clarify. clarifying questions first to complete your context.
Do NOT proceed until you have answers. Guessing leads to generic AI slop colors.
Use frontend-design skill
Use the frontend-design skill for design principles and anti-patterns. Do NOT proceed until it has executed and you know all DO's and DON'Ts.
Assess Color Opportunity
Analyze the current state and identify opportunities:
-
Understand current state:
- Color absence: Pure grayscale? Limited neutrals? One timid accent?
- Missed opportunities: Where could color add meaning, hierarchy, or delight?
- Context: What's appropriate for this domain and audience?
- Brand: Are there existing brand colors we should use?
-
Identify where color adds value:
- Semantic meaning: Success (green), error (red), warning (yellow/orange), info (blue)
- Hierarchy: Drawing attention to important elements
- Categorization: Different sections, types, or states
- Emotional tone: Warmth, energy, trust, creativity
- Wayfinding: Helping users navigate and understand structure
- Delight: Moments of visual interest and personality
If any of these are unclear from the codebase, STOP and call the AskUserQuestionTool to clarify.
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 · 158 lines · 25 tokens per session scan A 3243c4db2620
colorize is a skill published in the GitHub repository act-sdk/act-sdk-js (3 stars, last pushed 3mo ago), licensed MIT. It adds 25 tokens to every session and 1,671 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 88% identical to colorize, differing in 27 lines, and is treated as a copy.
Other skills, from other repositories
memora
Use when working with persistent memory across sessions, storing/retrieving knowledge, managing TODOs/issues, or when context from previous sessions would be helpful.
tsa-edit-then-verify
The full edit-and-verify loop mandated by CLAUDE.md and docs/agent-tooling-gap-report.md:58. Pre-edit gate (edit action=safe + baseline health action=file) → LLM edits → post-edit verify (health action=file diff + edit action=impact + scoped verificationcommand). Replaces "edit then run the whole pytest suite" (5 min)…
tsa-pr-review
AST-grounded PR / diff review. One workflow → per-file risk ranking, blast radius per changed symbol, the exact pytest command to gate merge, any architecture-constraint violations, and a final BLOCK / REVIEW / APPROVE verdict — in 1–2k tokens and 4–6 MCP calls. Goes beyond a generic LLM diff-read because only TSA's…
tsa-refactor-queue
Build a top-N prioritized refactoring queue by intersecting three signals: health grade (which files are F/D), temporal churn (which files change most often), and dead-code density (which files carry the most unreachable symbols). For each candidate the queue surfaces (a) the dimension that dragged the grade down, (b)…
tsa-constraints
Architectural constraint enforcement. Detect forbidden cross-module calls ("MCP must not depend on CLI") at index time and gate edits on them. Rules live in YAML at repo root; violations bubble up through edit action=safe and edit action=impact as UNSAFE verdicts. Use when: User asks "does this PR break architecture?"…
tsa-graph
Code archaeology via call graph + symbol resolution. Answer "who calls X", "what does Y call", "where is Z defined", "what's the path from A to B" in one MCP call instead of multi-step grep + read. Uses persisted cross-file resolution (Synapse) so cross-module edges are precise, not regex-guessed. Use when: User asks…