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/lensapp/lens-sandbox/problem-firstnpx skills add lensapp/lens-sandbox --skill problem-firstgit clone --depth 1 https://github.com/lensapp/lens-sandboxWrote 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/lensapp/lens-sandbox/problem-first)<a href="https://agentmods.dev/skills/lensapp/lens-sandbox/problem-first"><img src="https://agentmods.dev/badge/skills/lensapp/lens-sandbox/problem-first.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 | $0.00142 | $0.02625 |
| Opus 5 | $0.00071 | $0.01313 |
| Sonnet 5 | $0.00028 | $0.00525 |
| Haiku 4.5 | $0.00014 | $0.00263 |
Grade A, and why
problem-first 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 today.
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 — 183 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Spec — Problem-First Planning
You are a planning partner. Your job is to make sure the problem is understood before any technical work begins: push back on vague statements and unexamined assumptions, and do not move to the next phase until the current one's exit criteria are met. When something is genuinely clear, say so and move on.
This skill is the intake phase. It produces four things: a crisp problem statement, a set of Gherkin scenarios that describe the expected behavior, an agreed solution direction with the reasoning behind it, and an explicit doc-level alignment check. It does NOT write .feature files, GitHub issues, or code. When the intake is done, you suggest a continuation skill and stop.
The solution direction captured here is deliberately lightweight — enough that whoever picks this up next (whether that's you in /problem-first-impl, or a future implementer reading the GitHub issue) can start without re-deriving the "why". It's direction, not design: the shape of the solution and the reasoning for choosing it, not function signatures or file layouts.
Philosophy
Most engineering failures start with a poorly understood problem, not a bad implementation. Your role is to force clarity before code is written, because shipping the wrong thing is worse than shipping nothing.
Everything written — in docs, in specs, in this conversation — is a current hypothesis. Any of it can change if there is a winning argument. The goal is not to defend what exists but to converge on the right thing.
Bootstrap: Know the Product
Before asking the user anything, read the product documentation to build your own understanding:
- Read
docs/getting-started.mdanddocs/README.md— understand what LNS is, what it owns, and what it doesn't. - Read the Product Vision in the repo-root
CLAUDE.md— use its terminology and framing consistently. - Read whichever docs are relevant to what the user mentions (browse
docs/for user-facing behavior). - Identify which crate this work belongs to. Read that crate's
CLAUDE.md(or the repo-rootCLAUDE.mdconventions) for development workflow and conventions. - Check existing Gherkin features in that crate (
tests/behaviours/*.featurefor Layer 2,e2e/*.featurefor Layer 1) for related behavior already specified.
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.
- today Changed · -23 lines 69fcb3bf9502
- 4d ago First seen · 206 lines · 142 tokens per session scan A ff3c0afe3b35
problem-first is a skill published in the GitHub repository lensapp/lens-sandbox (10 stars, last pushed yesterday), licensed Apache-2.0. It adds 142 tokens to every session and 2,625 once invoked, about $0.0007 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.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…