Modern Web Guidance is a source repository for coding-agent skills that provide web-platform guidance, recommended practices, and browser compatibility information. It helps coding agents choose newer web APIs and patterns when generating web applications. The catalogue skills are the guidance produced and maintained by this project.
Borrowing it
Nothing to install: this file belongs to GoogleChrome/modern-web-guidance-src. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/GoogleChrome/modern-web-guidance-src/main/.agents/skills/eval-investigation/SKILL.mdgit clone --depth 1 https://github.com/GoogleChrome/modern-web-guidance-srcWrote 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/googlechrome/modern-web-guidance-src/eval-investigation)<a href="https://agentmods.dev/skills/googlechrome/modern-web-guidance-src/eval-investigation"><img src="https://agentmods.dev/badge/skills/googlechrome/modern-web-guidance-src/eval-investigation/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/googlechrome/modern-web-guidance-src/eval-investigation"><img src="https://agentmods.dev/badge/skills/googlechrome/modern-web-guidance-src/eval-investigation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Rogue Agent · line 213 Skill modifies its own code, configuration, or behavior at runtime. Self-modification enables an agent to escalate privileges, disable safety constraints, or install persistent backdoors.Fix: Prevent the skill from modifying its own code, SKILL.md, or configuration files. Treat skill files as read-only at runtime.
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 | $0.00039 | $0.04746 |
| Opus 5 | $0.00019 | $0.02373 |
| Sonnet 5 | $0.00008 | $0.00949 |
| Haiku 4.5 | $0.00004 | $0.00475 |
Grade A, and why
investigating-eval-results 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 9d 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 — 214 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Investigating Eval Results
This skill helps you diagnose why AI coding agents are failing evaluations, specifically looking for discrepancies between guided and unguided performance.
Core Philosophy: Immediate Resolution
- Fix It Now: Do not create tracking issues or delay work. The goal of an investigation is to identify the root cause and implement the fix immediately in the active session.
- Platform Boundary: When investigating an eval, strictly modify use-case specific files (i.e.,
task.md,grader.ts,expectations.md, demo apps, andguide.md). Do not attempt to fix bugs in the underlying platform infrastructure or Playwright test environment. If you identify infrastructure issues, note them clearly for the user and suggest filing an issue on GitHub for the engineering team, ensuring the use-case investigation remains focused and clean. - Success Rate Goal: The ultimate objective of every investigation is to achieve a 100% Guided Pass Rate. The unguided pass rate does not matter and can be ignored.
- Autonomous Initiative & Iteration: An investigation is not a single pass. You must autonomously loop through fixing files, re-running evaluations, measuring progress, and rolling back failed attempts until you hit 100% success. Never stop early, and run tests multiple times to ensure your fix is consistently non-flaky.
Communication Protocol
Because evaluation runs (gd eval) take time, check in with the user approximately every 30 seconds to provide a helpful narrative summary of what the agent is currently doing.
Whenever you summarize progress during these check-ins, you MUST:
- Include a direct quote or code block of the underlying log lines to substantiate your update.
- Provide a clickable markdown link to the specific log file being referenced so the user can click through to see the full contents.
However, NEVER include timestamps in your updates, as they add absolutely zero value to the user.
Example of a good check-in:
The agent has successfully retrieved the reference guidance using the modern web skill:
[Modern Web Log]: {"tool":"get_best_practices"}Reference: task-log
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.
- 9d ago First seen · 214 lines · 39 tokens per session scan A 2a4908789317
investigating-eval-results is a skill published in the GitHub repository GoogleChrome/modern-web-guidance-src (1,022 stars, last pushed today), licensed Apache-2.0. It adds 39 tokens to every session and 4,746 once invoked, about $0.0002 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.
Other skills, from other repositories
systematic-debugging
4-phase root cause debugging: understand bugs before fixing.
langsmith-observability
LLM observability platform for tracing, evaluation, and monitoring. Use when debugging LLM applications, evaluating model outputs against datasets, monitoring production systems, or building systematic testing pipelines for AI applications.
experimental-code-coverage-local-debugger
Runs code coverage locally via Universal Test Runner (UTR) or helper scripts, mimicking LUCI trybots. Activate when CQ tryjobs fail or underreport coverage, to test local GN/recipe repairs before uploading, or to debug hermetic crashes.
adversarial-reviewer
Adversarial code review that assumes bugs exist and hunts for them. Use when asked to review code, find bugs, audit for correctness, stress-test a PR, or when someone says "tear this apart" or "what's wrong with this". Give no benefit of the doubt — every line is guilty until proven innocent.
cli-e2e
Write, modify, or debug Docker-based Composio CLI end-to-end tests under ts/e2e-tests/cli, including binary invocation, fixture isolation, output assertions, and package manifests. Use for CLI E2E test suites only; use cli-command for CLI source implementation.
ios-simulator
Verify and debug native, React Native, Expo, or Flutter apps on an iOS Simulator with agent-device. Use when an agent needs to launch an app, inspect its live UI, tap, type, scroll, validate a code change, collect failure evidence, or reproduce a workflow on an iPhone or iPad Simulator.