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/critiquenpx skills add act-sdk/act-sdk-js --skill critiquegit 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/critique)<a href="https://agentmods.dev/skills/act-sdk/act-sdk-js/critique"><img src="https://agentmods.dev/badge/skills/act-sdk/act-sdk-js/critique.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 | $0.00030 | $0.01126 |
| Opus 5 | $0.00015 | $0.00563 |
| Sonnet 5 | $0.00006 | $0.00225 |
| Haiku 4.5 | $0.00003 | $0.00113 |
Grade A, and why
critique 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
100% identical to critique — 0 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 — 118 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Conduct a holistic design critique, evaluating whether the interface actually works—not just technically, but as a designed experience. Think like a design director giving feedback.
First: Use the frontend-design skill for design principles and anti-patterns.
Design Critique
Evaluate the interface across these dimensions:
1. AI Slop Detection (CRITICAL)
This is the most important check. Does this look like every other AI-generated interface from 2024-2025?
Review the design against ALL the DON'T guidelines in the frontend-design skill—they are the fingerprints of AI-generated work. Check for the AI color palette, gradient text, dark mode with glowing accents, glassmorphism, hero metric layouts, identical card grids, generic fonts, and all other tells.
The test: If you showed this to someone and said "AI made this," would they believe you immediately? If yes, that's the problem.
2. Visual Hierarchy
- Does the eye flow to the most important element first?
- Is there a clear primary action? Can you spot it in 2 seconds?
- Do size, color, and position communicate importance correctly?
- Is there visual competition between elements that should have different weights?
3. Information Architecture
- Is the structure intuitive? Would a new user understand the organization?
- Is related content grouped logically?
- Are there too many choices at once? (cognitive overload)
- Is the navigation clear and predictable?
4. Emotional Resonance
- What emotion does this interface evoke? Is that intentional?
- Does it match the brand personality?
- Does it feel trustworthy, approachable, premium, playful—whatever it should feel?
- Would the target user feel "this is for me"?
5. Discoverability & Affordance
- Are interactive elements obviously interactive?
- Would a user know what to do without instructions?
- Are hover/focus states providing useful feedback?
- Are there hidden features that should be more visible?
6. Composition & Balance
- Does the layout feel balanced or uncomfortably weighted?
- Is whitespace used intentionally or just leftover?
- Is there visual rhythm in spacing and repetition?
- Does asymmetry feel designed or accidental?
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 · 118 lines · 30 tokens per session scan A 244084b8fefc
critique is a skill published in the GitHub repository act-sdk/act-sdk-js (3 stars, last pushed 3mo ago), licensed MIT. It adds 30 tokens to every session and 1,126 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to critique, differing in 0 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…