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 skills add ilang-ai/autocode --skill product-studygit clone --depth 1 https://github.com/ilang-ai/autocodeWrote 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/ilang-ai/autocode/product-study)<a href="https://agentmods.dev/skills/ilang-ai/autocode/product-study"><img src="https://agentmods.dev/badge/skills/ilang-ai/autocode/product-study/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/skills/ilang-ai/autocode/product-study"><img src="https://agentmods.dev/badge/skills/ilang-ai/autocode/product-study.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 | $0.00136 | $0.03539 |
| Opus 5.5 | $0.00054 | $0.01416 |
| Sonnet 5.5 | $0.00027 | $0.00708 |
| Haiku 4.5 | $0.00014 | $0.00354 |
Grade A, and why
product-study 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 4d 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 — 251 lines — stays where its author put it; the contents beside it link to each section on GitHub.
[PROTOCOL:iLang|v=5.0|layer=declaration]
::GENE{product-study|conf:confirmed|scope:global} T:learn_structure_not_skin T:public_pages_only T:every_claim_has_source_and_confidence T:end_with_an_mvp_the_agent_can_build_in_one_session T:classify_target_first|saas_with_public_pages|open_source_project|app_with_store_listing|undocumented T:acceptance_line=user_does+should_see+machine_checks T:mvp_names_learned_rules_vs_own_decisions T:verify_runs_before_go_live_and_again_on_the_live_url|tool:scripts/acceptance_run.py A:reverse_engineer_or_capture⇒refuse_and_continue_with_public_pages A:report_tests_passed⇒show_expected_vs_actual_table A:copy_ui_copy_images_brand_code⇒describe_structure_instead A:present_blueprint_as_the_product⇒it_is_a_map_the_build_comes_next
::ACTIVATE{product-study} ON:user_names_a_product_to_imitate ON:user_pastes_product_url_with_build_intent
Product study
The user saw a product and wants their own. What they need from you is a map of its structure — features, objects, flows, pricing, ecosystem — gathered from pages anyone can open, with the evidence attached, and ending in an MVP cut small enough to build and deploy in one session. You are a product analyst who walks in through the front door: every line you write can be traced to a public page, and nothing of the original's skin (interface, copy, images, brand, code) ends up in the user's product. That is what makes the result safe to ship and the user's own.
Step 0 — Frame it in one sentence
Before any fetching, tell the user what will happen, in their language:
I'll study X from its public pages, give you a blueprint of how it's built, and cut an MVP we can ship today. We learn the structure and design our own look.
This sentence does two jobs: it sets the expectation that the result is not a pixel copy, and it tells the user the study comes before the build. If you cannot talk to the user mid-run (a batch or sub-agent setting), lead the final reply with it instead. Then ask at most one question, only if the header line of the blueprint cannot be filled without it: who is your version for, and what is the one job it must do? If the user already said it, do not ask.
What ships with it
17 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.
- examples/dino/blueprint.md 9.6 KB
- examples/dino/mvp.md 4.5 KB
- examples/dino/README.md 1.6 KB
- examples/dino/site/acceptance.js 2.9 KB runs code
- examples/dino/site/index.html 13 KB
- examples/dino/sources.json 1.5 KB
- examples/linear/blueprint.md 23 KB
- examples/linear/mvp.md 5.2 KB
- examples/linear/README.md 1.0 KB
- examples/linear/sources.json 9.2 KB
- references/blueprint-template.md 5.6 KB
- references/boundaries.md 3.9 KB
- references/sources-policy.md 5.2 KB
- scripts/acceptance_run.py 6.6 KB runs code
- scripts/blueprint.py 17 KB runs code
- scripts/fetch_public.py 26 KB runs code
- scripts/selftest.py 14 KB runs code
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.
- 4d ago Changed 3dc78088d013
- 11d ago Changed b13318a66b84
- 12d ago Changed · +44 lines 6176c4cff9b1
- 13d ago First seen · 207 lines · 136 tokens per session scan A 721118358a6a
product-study is a skill published in the GitHub repository ilang-ai/autocode (104 stars, last pushed 5d ago), licensed MIT. It adds 136 tokens to every session and 3,539 once invoked, about $0.0005 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 2026-10-14.
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