lookout

lookout is a skill for Claude Code, Codex from IamK77/Skill. It costs 216 tokens per session (3,720 once invoked), scanned A, original, Apache-2.0.

A frontend design checklist for defining a consistent visual system, including colors, fonts, spacing, depth, and animation. A visual system is a shared set of design decisions that components reuse.

In plain words
What is it for?
Use it after a feature works to establish or audit its visual language, or when an interface feels visually inconsistent.
Why use it?
It helps prevent each screen or component from inventing its own visual values. This makes the interface easier to keep consistent and review.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/iamk77/skill/lookout
Any agent
npx skills add IamK77/Skill --skill lookout
Clone the repo
git clone --depth 1 https://github.com/IamK77/Skill

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for lookout

README.md
[![agentmods](https://agentmods.dev/badge/skills/iamk77/skill/lookout.svg)](https://agentmods.dev/skills/iamk77/skill/lookout)
Your own site
<a href="https://agentmods.dev/skills/iamk77/skill/lookout"><img src="https://agentmods.dev/badge/skills/iamk77/skill/lookout.svg" alt="Measured on agentmods" height="20"></a>
Per session 216 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,720 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00216 $0.03720
Opus 5 $0.00108 $0.01860
Sonnet 5 $0.00043 $0.00744
Haiku 4.5 $0.00022 $0.00372

Measured 3d ago against content hash 8120557b2156, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

lookout 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 3d 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.

skills/surface/lookout/SKILL.md · 135 lines

How it starts

The opening of the file, as written. The whole thing — 135 lines — stays where its author put it; the contents beside it link to each section on GitHub.

lookout

!checklist init ${CLAUDE_SKILL_DIR} --force

A lookout watches the real horizon — the live sea, not the chart — and is also the conscience posted to call danger before the ship runs into it. lookout is the sixth skill of the surface suite, the delivery-and-observability lens, and it carries the suite's ethics across the line from philosophy into a launch gate. Its mindset is one shift: shipping is not the finish line, it is the start of the conversation with reality. Its product is a delivery-and-observability setup — preview per PR, RUM and error tracking, an analytics-and-experiment framework — plus the pre-launch ethics gate that the objective function from bearings must pass. It runs across gated stages and will not advance past a GATE until the checklist tool clears it — order enforced, substance yours.

The heavy part, stated up front: the moment the metrics dashboard exists, it starts pulling every decision — so the ethics must already be in place. A/B testing is, mathematically, a gradient-descent optimizer: each experiment keeps the higher-scoring variant, and run thousands of times it slides down the gradient of whatever metric you chose. Point that optimizer at an engagement metric — time-on-site, scroll depth — over a human nervous system, and it will discover manipulation as a local optimum: infinite scroll, variable-ratio rewards, removed stopping cues, all selected because they move the number, with no person ever sitting down to design a dark pattern. This is Goodhart's law on a human, the same shape as AI misalignment: optimize a proxy hard enough and you get the proxy, not the intent. The real villain is the loss function — which is why the objective-function charter is set in bearings, before the dashboard exists, and audited here, before any metric-driven launch.

This is where the agent era bites:

  • The agent treats launch as the end. It ships, the task turns green, and it never wires the feedback loop — no preview to judge perception by eye, no RUM, no error tracking — so the team flies blind exactly where the spec lives (the real user's nervous system).
  • The agent instruments by lab, not field, and tracks vanity. It will trust a fast-machine/fast-network lab number, miss the mid-range-phone-on-4G truth, and happily collect every event "for analytics" — monitoring debt and a privacy liability with no decision attached.
  • The agent will optimize whatever metric it's handed, including engagement. It feels nothing about the reflective-vs-impulsive-self betrayal, so left alone it points the optimizer at the number that grows, and the optimizer finds the manipulation. The conscience is not a thing the agent supplies — it must be a gate.

Read references/the-membrane.md first — the heart; for lookout, the ethics axis is the spine (friction as the moral primitive, manipulation as bypassing agency, the impulsive-vs-reflective self, A/B as a gradient toward exploitation) along with the mind axis (RUM = psychophysics as numbers). Load at the start, re-check at every gate.

Speak the user's language. Almost every call here is the user's — which metric is the north-star, is this experiment honest, is this default serving the user. Read their fluency and gloss a term on first use (preview deploy, feature flag / progressive rollout, RUM vs lab, Core Web Vitals (LCP/INP/CLS), a guardrail metric, the gradient-descent framing of A/B, the reflective vs impulsive self, friction symmetry / GDPR Art. 7). A metric decision the user can't weigh is an ethics imposed, not shared.

Read the full file on GitHub · 135 lines

Files

What ships with it

8 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

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.

  1. 3d ago First seen · 135 lines · 216 tokens per session scan A 8120557b2156

Subscribe to this mod's changes

lookout is a skill published in the GitHub repository IamK77/Skill (2 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 216 tokens to every session and 3,720 once invoked, about $0.0011 per session on Opus 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.

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