lazyweb-skill AGENTS.md

lazyweb-skill AGENTS.md is an instructions file for Codex, OpenCode from aboul3ata/lazyweb-skill. It costs 1,054 tokens per session, scanned A, original, MIT.

Additional guidance for Lazyweb skills, which create HTML reports from research or design investigations. It emphasizes showing evidence, quantifying observations, making a clear recommendation, and recording lessons for future work.

In plain words
What is it for?
Building visual research reports, documenting patterns and recommendations, comparing alternatives, and capturing reusable learnings.
Why use it?
It helps reports support their conclusions with visible proof and gives readers a clear decision instead of an unranked list of options.

Instructions file for CodexOpenCode

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 instructions/aboul3ata/lazyweb-skill/agents-md
Clone the repo
git clone --depth 1 https://github.com/aboul3ata/lazyweb-skill

Made for: Codex, OpenCode.

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 lazyweb-skill AGENTS.md

README.md
[![agentmods](https://agentmods.dev/badge/instructions/aboul3ata/lazyweb-skill/agents-md.svg)](https://agentmods.dev/instructions/aboul3ata/lazyweb-skill/agents-md)
Your own site
<a href="https://agentmods.dev/instructions/aboul3ata/lazyweb-skill/agents-md"><img src="https://agentmods.dev/badge/instructions/aboul3ata/lazyweb-skill/agents-md.svg" alt="Measured on agentmods" height="20"></a>
Per session 1,054 This file is loaded in full into every session.
When invoked 1,054 The same file — it is already loaded in full.
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 $0.01054 $0.01054
Opus 5 $0.00527 $0.00527
Sonnet 5 $0.00211 $0.00211
Haiku 4.5 $0.00105 $0.00105

Measured yesterday against content hash 3d42bdcffb19, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

lazyweb-skill AGENTS.md 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 yesterday.

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.

AGENTS.md · 28 lines

How it starts

The opening of the file, as written. The whole thing — 28 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Lazyweb skill pack — agent guidance

Lazyweb report principles (governs any agent building a Lazyweb report)

Every Lazyweb skill produces an HTML report. Whatever the skill, the report must embody these four principles — they are the product, not a style preference.

  1. Show, don't tell. Prove every claim with a real visual reference or data, never prose alone. Each assertion (pattern, anti-pattern, idea, hypothesis, "what's working", convention check, recommendation, or A/B learning) carries the screenshot(s)/experiment that demonstrate it, sitting with the claim. Quantify prevalence ("5 of 9 references") instead of asserting it ("near-universal"). Never render a proposed layout as ASCII/box-drawing <pre> art — use an HTML/CSS prototype, mock-frame, or generated image.

  2. Be opinionated; carry the decision. Take a clear ranked stance and lead with a single recommended path, marked as such in the human-visible body (not only in the machine handoff block). Tell the user what to do first and what to skip, with a one-line reason each. Never hand over an undifferentiated menu of co-equal options and make the reader choose.

  3. Maximize confidence with evidence. Back each recommendation with what worked for OTHER apps (real screenshots) PLUS supporting data — prevalence across the corpus, and A/B experiment learnings for growth/monetization screens. When experiment data is unavailable, say so and fall back to a stated prevalence count; never ship a recommendation with no visual and no number behind it.

  4. Be truth-seeking. Never overclaim. Label evidence strength honestly (measured vs directional vs single-source/off-category) on each claim, and flag a weak, thin, single-source, or context-mismatched corpus up front. Tag any brand inferred from a URL/vision description as unverified. Never fabricate a reference, a metric, or a company name. The machine-readable handoff and the human-facing body must agree about confidence.

Shared report furniture (use the existing tokens --ink:#1f2328; --mut:#57606a; --line:#d0d7de; --soft:#eef4fb; --accent:#0969da): the light-blue Agent Instructions copy block is always section #1; reuse the shared .deck (evidence carousel — all references visible, scroll-snaps with ◀ ▶ prev/next buttons), .legend + .rec cards (opinionated ranked pick: a decision legend over big-proof recommendation cards, #1 = .rec.lead), .ebadge/.corpus (honest evidence labels), and .mock (mock-frame) components so every skill renders proof, decisions, and honesty the same way. One sanctioned substitution: lazyweb-deep-design-research (report v3) renders its decision as a side-by-side .compare (Control × Recommended in height-locked frames, with a ◀ ▶ variant switcher on the right frame) over an .option-deck of bet cards — no .legend table and no .ebadge chips there; ranking is carried by order + a Recommended flag, and evidence counts by plain words inside the evidence-deck captions — each card shows only two 8-14-word bolded What/Why bullets. It renders no patterns/.pat section — evidence lives in the bet decks and the clustered inspo map. Same decision/honesty semantics, quieter chrome. (It still reuses the shared .deck, .prev, .corpus, .flip, and .mock components.)

Read the full file on GitHub · 28 lines

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. yesterday Changed · +1 lines · +41 tokens per session 3d42bdcffb19
  2. 5d ago First seen · 27 lines · 1,013 tokens per session scan A cc0db1d715a1

Subscribe to this mod's changes

lazyweb-skill AGENTS.md is an instructions file published in the GitHub repository aboul3ata/lazyweb-skill (447 stars, last pushed 2d ago), licensed MIT. It adds 1,054 tokens to every session, about $0.0053 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 2026-08-30.

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