Agent skill

Nightly Eval Investigation

by GoogleChrome in GoogleChrome/modern-web-guidance-src

Downloads and analyzes the latest three distinct nightly evaluation runs (Claude Code, Codex CLI, and Jetski CLI) from the GCS remote dashboard to identify and flag unhealthy or low-performing tasks…

Apache-2.0Auto-check passed

Install Nightly Eval Investigation

skills CLI
$ npx skills add GoogleChrome/modern-web-guidance-src --skill nightly-eval-investigation -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install GoogleChrome/modern-web-guidance-src nightly-eval-investigation --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/GoogleChrome/modern-web-guidance-src.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/nightly-eval-investigation .claude/skills/nightly-eval-investigation && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
nightly-eval-investigation
GitHub stars
1.1k
Token cost
~3.2k tokens
SKILL.md length
1,443 words
Files
3 (incl. scripts)
Skills in repo
12
Repo updated
First seen
Licence
Apache-2.0

At a glance

Downloads and analyzes the latest three distinct nightly evaluation runs (Claude Code, Codex CLI, and Jetski CLI) from the GCS remote dashboard to identify and flag unhealthy or low-performing tasks…

  • Works in 10 steps: Missing Expected Guide… → Too Many Guides Consumed… → Low Guided Pass Rate… → …
  • You need to run a bulk investigation on remote nightly runs
  • SKILL.md covers Quick Reference: Remote…, Flagging & Health Criteria, Workflow Instructions and Report Format Standards
  • Runs TypeScript scripts from its folder; calls gcloud and node

What it does

Nightly Eval Investigation is an agent skill from GoogleChrome/modern-web-guidance-src. Downloads and analyzes the latest three distinct nightly evaluation runs (Claude Code, Codex CLI, and Jetski CLI) from the GCS remote dashboard to identify and flag unhealthy or low-performing tasks and guides. Use this skill whenever you need to run a bulk investigation on remote nightly runs, track agent health, or identify over-prescribed/brittle guides.

Its SKILL.md is about 3.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 3 other files, including scripts (for example `scripts/investigate.ts` and `scripts/publish_report.ts`).

The licence is Apache-2.0.

When your agent uses it

  • You need to run a bulk investigation on remote nightly runs
  • Track agent health
  • Identify over-prescribed/brittle guides

Example prompts

  • “Use the nightly-eval-investigation skill to download and analyzes the latest three distinct nightly evaluation runs (Claude Code, Codex CLI, and…”
  • “/nightly-eval-investigation”

Requirements

  • Node.js

Workflow steps

10 steps, taken from the step headings in SKILL.md.

  1. Missing Expected Guide (MISSING_EXPECTED_GUIDE)
  2. Too Many Guides Consumed (TOO_MANY_GUIDES_CONSUMED)
  3. Low Guided Pass Rate (LOW_GUIDED_PASS_RATE)
  4. High Unguided Pass Rate (HIGH_UNGUIDED_PASS_RATE)
  5. Query and Select the Runs
  6. Pull down Results
  7. Run the Flagging Script
  8. Perform Qualitative Deep-Dives (Investigation Playbook)
  9. Inform the User of the Publishing Script
  10. Present and Link

What it can do on your machine

Read from SKILL.md and the folder at commit 0d002f0. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 2 files in scripts/ (TypeScript), which the agent can run.

    Shell commands in SKILL.md call:

    • gcloud
    • node

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use gcloud, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Nightly Eval Investigation loads about 3.2k tokens when it runs. Until then it costs about 97 tokens; SKILL.md has 1,443 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~97
When it runs · the whole SKILL.md, loaded when a task matches
~3.2k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from GoogleChrome/modern-web-guidance-src at commit 0d002f0, republished under its Apache-2.0 licence (© GoogleChrome). 1,443 words, ~3,239 tokens.

Download SKILL.mdSave it as .claude/skills/nightly-eval-investigation/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.
name
nightly-eval-investigation
description
Downloads and analyzes the latest three distinct nightly evaluation runs (Claude Code, Codex CLI, and Jetski CLI) from the GCS remote dashboard to identify and flag unhealthy or low-performing tasks and guides. Use this skill whenever you need to run a bulk investigation on remote nightly runs, track agent health, or identify over-prescribed/brittle guides.

Nightly Evaluation Investigation

This skill automates the retrieval and multi-agent comparison of remote nightly evaluation runs from Google Cloud Storage (GCS) to diagnose system-wide guidance health, task drift, and over-prescribed guides.

Core Objectives
  1. Cross-Agent Diagnostics: Compare results across three distinct, modern agents (Claude Code, Codex CLI, and Jetski CLI) to locate patterns that are agent-agnostic.
  2. Guide Discovery Audit: Catch cases where agents skip the expected guide or over-retrieve irrelevant guides.
  3. Health Thresholding: Flag tasks that either underperform under guidance or are too easy/over-prescriptive (meaning unguided runs already pass easily).
  4. Structured Reporting: Produce reliable Markdown and JSON artifacts that downstream automation can easily ingest.

[!IMPORTANT] CRITICAL CONSTRAINT - READ-ONLY INVESTIGATION ONLY This skill is strictly for diagnostics, investigation, and suggesting recommendations. The agent MUST NOT under any circumstances edit, modify, create, delete, or touch any files under the guides/ directory (including task files, guides, graders, expectations, or demos). All suggestions for fixes must be documented exclusively in the generated investigation report markdown file under the "Actionable Recommendations" section. No automated or manual code remediation should be performed.


Quick Reference: Remote Dashboard Results (GCS)

All nightly evaluation suites are automatically uploaded to Google Cloud Storage:

  • GCS Bucket: gs://guidance-evals/
  • Naming Pattern: nightly-YYYY-MM-DD_HH-MM-SS-[agent_type]/ (note: legacy folders may contain an optional trailing -[ldap] suffix)
Distinct Agent Categories

We track three core agent implementations in our periodic evaluations:

  1. Claude Code (claude_code or claude)
  2. Codex CLI (codex_cli or codex)
  3. Jetski CLI (jetski_cli or agy or jetski)

Flagging & Health Criteria

For every task evaluated in the runs, the investigation tracks four specific metrics across the 3 runs:

1. Missing Expected Guide (MISSING_EXPECTED_GUIDE)
  • Rule: The expected guide for a task (which matches the task's guide name) is not listed in the run's guidesUsed array.
  • Threshold: Flagged if 2 out of the 3 nightly runs for that task are missing the expected guide.
  • Implication: The prompt in tasks/task.md is failing to force discovery/retrieval of the correct reference guide.
2. Too Many Guides Consumed (TOO_MANY_GUIDES_CONSUMED)
  • Rule: The agent consumes 3 or more guides during a single guided run of the task.
  • Threshold: Flagged if 2 out of the 3 nightly runs for that task are consuming 3 or more guides.
  • Implication: The agent is either experiencing search query sprawl or guide definitions overlap too heavily.
3. Low Guided Pass Rate (LOW_GUIDED_PASS_RATE)
  • Rule: The guided pass rate for a task is under 75%.
  • Threshold: Flagged if all three nightly runs for that task have guided pass rates under 75%.
  • Implication: The guide content is ambiguous, incomplete, or the grading assertions are brittle.
4. High Unguided Pass Rate (HIGH_UNGUIDED_PASS_RATE)
  • Rule: The unguided pass rate for a task is 70% or higher.
  • Threshold: Flagged if all three nightly runs for that task have unguided pass rates of 70% or higher.
  • Implication: The task prompt is overly prescriptive (giving away the solution) or the task itself is too trivial to require guidance.

Workflow Instructions

To perform a nightly evaluation investigation, follow these steps:

Step 1: Query and Select the Runs
  1. List all suites in GCS:
    bash
    gcloud storage ls gs://guidance-evals/
  2. Parse the output to filter directories starting with nightly-.
  3. Group runs by the three distinct agent types (handling both newer clean names and legacy runs containing a user LDAP suffix):
    • Claude Code: matches claude_code or claude
    • Codex CLI: matches codex_cli or codex
    • Jetski CLI: matches jetski_cli, agy, or jetski
  4. For each group, select the single folder with the latest timestamp.
Step 2: Pull down Results
  1. For each of the selected 3 folders, create the directory locally: results/suites/<folder_name>
  2. Sync the suite folder recursively while excluding heavy binaries (Playwright trace.zip and screenshots) to save bandwidth and disk space:
    bash
    gcloud storage rsync gs://guidance-evals/<folder_name> results/suites/<folder_name> --recursive --exclude ".*\.zip$|.*\.png$"
    (Note: This is done automatically by the investigate script in Step 3, but can be run manually if needed.)
Step 3: Run the Flagging Script

Run the automated TypeScript analysis script:

bash
node .agents/skills/nightly-eval-investigation/scripts/investigate.ts

This script will automatically cross-examine the results, flag unhealthy tasks, and output the report and context helper artifacts to the output directory:

  • Markdown: out/nightly-investigation/nightly_investigation_report.md
  • JSON Context: out/nightly-investigation/flagged_tasks_context.json (contains extracted prompts, guide descriptions, and test headers for all flagged tasks to assist in diagnostics)
Show full SKILL.md (761 more words)Show less
Step 4: Perform Qualitative Deep-Dives (Investigation Playbook)

For each task listed in the Flagged Tasks Table of nightly_investigation_report.md, you must perform a qualitative deep-dive to locate the root cause and recommend actionable fixes.

[!IMPORTANT] CRITICAL COMPLIANCE RULES:

  1. Do Not Modify Source Files: This is a passive/diagnostic investigation. Do NOT touch, edit, or modify any files under guides/ (such as task prompts, guide markdown files, or grader TypeScript files). All diagnostic findings and recommendations must be written only inside the markdown report.
  2. Complete All Flagged Tasks: You MUST qualitatively investigate and fully populate the summary for every single flagged task. Leaving placeholder text or skipping any flagged task is unacceptable.
  3. Strict Heading Omission: Under Diagnostic Details, only include headings/bullet points for the specific flags that were triggered for the task. Omit any non-triggered flags completely from the markdown file. Do not write "N/A" or "No issues".
  4. Long-Running, Thorough Investigation: The qualitative investigation phase is a long-running, intensive task. You MUST thoroughly examine each flagged guide/test one by one, inspecting the target prompt (task.md), reference guide (guide.md), and grading code (grader.ts) to build a high-quality, task-specific diagnostic summary and actionable recommendation. Do not rush, skip steps, or bundle tasks.

Follow this linear playbook for each flagged task:

  1. Locate Source Files Find the task's directory under the local workspace: guides/[category]/[use-case]/

    • Prompt: tasks/task.md
    • Guide: guide.md
    • Grader: grader.ts and expectations.md
  2. Review Extracted Failed Assertions The script investigate.ts automatically extracts and intersects the failed assertions across all three agents, writing them directly into the report.

    • Omission Rule: This section is only populated if the task triggered the LOW_GUIDED_PASS_RATE flag. For all other flags, it is completely omitted.
    • Only assertions that failed across all three agents are included.
    • Review these common failures to analyze their root causes.
  3. Map Flags to Diagnostic Explanations Analyze the source files based on the specific flags that were triggered, and write a diagnostic summary explaining why the task was flagged under each active flag.

    [!IMPORTANT] OMISSION RULE: Only include bullets for the specific flags that were triggered for the task. Omit any flags that were not triggered (do not write "N/A" or "No issues").

    • HIGH_UNGUIDED_PASS_RATE: Explain why unguided runs passed. Inspect tasks/task.md to see if the prompt is overly prescriptive (explicitly mentioning CSS attributes or API details that act as giveaways). Inspect grader.ts to check for loose/vacuous assertions.
    • LOW_GUIDED_PASS_RATE: Explain why guided runs failed. Inspect guide.md for bugs, outdated modules, or incorrect syntax. Inspect grader.ts for calibration drift or rigid checks. Check for browser/emulation issues in the sandbox.
    • MISSING_EXPECTED_GUIDE: Explain why the expected guide was missed. Inspect the task prompt in tasks/task.md to see if it lacks search keywords, or the guide metadata/synonyms in guide.md.
    • TOO_MANY_GUIDES_CONSUMED: Explain why multiple guides (3 or more) were consumed. Check for overly broad prompts that cause query sprawl, or overlapping guide descriptions.
  4. Draft Specific, Actionable Recommendations Determine which recommendations to include based on the diagnostics mapped in previous steps. All recommendations must be directly justified by the findings from your investigation of the prompts, guides, and graders. Vague recommendations like "Fix prompt" or "Fix guide" are not acceptable; you must propose exact, concrete changes.

    [!IMPORTANT] OMISSION RULE: Only include recommendation lines for components that actually require changes. If a component does not require updates (e.g., the grader is correct as-is), you MUST OMIT the corresponding recommendation line completely from the markdown file (do not write "Keep as is" or "No changes").

    • Prompt: Propose the specific new phrasing or keywords to add/remove.
    • Guide: Specify the description, metadata, or content updates needed.
    • Grader: Detail the exact logic changes or assertions to modify/loosen.
  5. Synthesize the Markdown Report Write the summary under the task's section in out/nightly-investigation/nightly_investigation_report.md following the template below.

Step 5: Inform the User of the Publishing Script

Once the qualitative diagnostics and recommendations have been fully written and saved to the markdown report, present the final report to the user and inform them that they can run the automated publisher script to create the GitHub parent issue and the task-level engineering/devrel subissues.

[!WARNING] DO NOT EXECUTE THE PUBLISHER SCRIPT YOURSELF: The agent must never run the publisher script (publish_report.ts) or create/publish any GitHub issues itself. It must only inform the user of the command so they can verify the report first and execute it manually.

bash
node .agents/skills/nightly-eval-investigation/scripts/publish_report.ts

Present the final report to the user, providing a clickable link, and remind them of the publishing command:

  • Markdown Report: nightly_investigation_report.md

Report Format Standards

All investigation reports MUST strictly follow these templates to ensure downstream compatibility:

Markdown Standard (out/nightly-investigation/nightly_investigation_report.md)
markdown
# Nightly Evaluation Investigation Report

**Generated:** YYYY-MM-DD
**Suites Investigated:**
- **claude_code**: `[folder_name_1]`
- **codex_cli**: `[folder_name_2]`
- **jetski_cli**: `[folder_name_3]`

---

## Summary of Flagged Tasks

- **Total distinct tasks analyzed:** [total_count]
- **Total flagged tasks:** [flagged_count]

### Flagged Tasks Table

| Task / Guide | Flags | Details |
| :--- | :--- | :--- |
| `[task_name]` | `[FLAG_1]`, `[FLAG_2]` | [Detail explanation of flags] |

## Flagged Tasks Details

### `[task_name]`

#### Flags:
- **`[FLAG_1]`**: [Detail explanation]

#### Run Details:

| Agent | Guided Pass Rate | Unguided Pass Rate | Guides Consumed |
| :--- | :---: | :---: | :--- |
| **claude_code** | [guided_rate]% | [unguided_rate]% | `[guide_1]`, `[guide_2]` |
| **codex_cli** | [guided_rate]% | [unguided_rate]% | `[guide_1]` |
| **jetski_cli** | [guided_rate]% | [unguided_rate]% | *None* |

#### Qualitative Diagnostic Summary:

- **Diagnostic Details**:
  - *Failed Assertions*:
    - "[Exact assertion error message string]" (Test ID: `[test_id]`)
  - **[FLAG_1]**: TODO
  - **[FLAG_2]**: TODO

- **Actionable Recommendations**:
  - [ ] **Prompt**: TODO
  - [ ] **Guide**: TODO
  - [ ] **Grader**: TODO

---

© GoogleChrome, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 2 other files (scripts) in .agents/skills/nightly-eval-investigation of GoogleChrome/modern-web-guidance-src.

  • SKILL.md
  • scripts/investigate.ts
  • scripts/publish_report.ts

Open the folder on GitHubat commit 0d002f0

Compare with similar skills

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Nightly Eval Investigation this skillGoogleChrome/modern-web-guidance-src1.1k—~3.2kAutomated safety check: PassApache-2.0
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Eval-Driven Development Harnessaffaan-m/ECC276k—~1.5kAutomated safety check: PassMIT
Evalalirezarezvani/claude-skills28k—~618Automated safety check: PassMIT
Eval Harnessaffaan-m/ECC276k—~2.2kAutomated safety check: PassMIT
Analyzing Email Headers For Phishing Investigationmukul975/Anthropic-Cybersecurity-Skills34k—~3.2kAutomated safety check: PassApache-2.0

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Questions about Nightly Eval Investigation

What does Nightly Eval Investigation do?

Downloads and analyzes the latest three distinct nightly evaluation runs (Claude Code, Codex CLI, and Jetski CLI) from the GCS remote dashboard to identify and flag unhealthy or low-performing tasks…. Nightly Eval Investigation is an agent skill from GoogleChrome/modern-web-guidance-src. Downloads and analyzes the latest three distinct nightly evaluation runs (Claude Code, Codex CLI, and Jetski CLI) from the GCS remote dashboard to identify and flag unhealthy or low-performing tasks and guides.

When should I use Nightly Eval Investigation?

Nightly Eval Investigation fits situations like: you need to run a bulk investigation on remote nightly runs; track agent health; identify over-prescribed/brittle guides.

How do I install Nightly Eval Investigation in Claude Code?

Run `npx skills add GoogleChrome/modern-web-guidance-src --skill nightly-eval-investigation -a claude-code`. Or copy the skill folder (.agents/skills/nightly-eval-investigation in GoogleChrome/modern-web-guidance-src) into .claude/skills/nightly-eval-investigation in your project. Claude Code loads it when a task matches its description.

How do I install Nightly Eval Investigation in Codex?

Run `npx skills add GoogleChrome/modern-web-guidance-src --skill nightly-eval-investigation -a codex`. Or copy the skill folder (.agents/skills/nightly-eval-investigation in GoogleChrome/modern-web-guidance-src) into .agents/skills/nightly-eval-investigation in your project. Codex loads it when a task matches its description.

Can I use Nightly Eval Investigation in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add GoogleChrome/modern-web-guidance-src --skill nightly-eval-investigation -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/nightly-eval-investigation, .gemini/skills/nightly-eval-investigation, .github/skills/nightly-eval-investigation and .opencode/skills/nightly-eval-investigation in your project.

What does Nightly Eval Investigation need to run?

Going by SKILL.md and its folder, Nightly Eval Investigation needs TypeScript for the scripts in its folder and the command-line tools its instructions call (gcloud and node). Our summary lists: Node.js.

Does Nightly Eval Investigation access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Nightly Eval Investigation safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Nightly Eval Investigation use?

Nightly Eval Investigation is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Nightly Eval Investigation use?

About 3.2k tokens (SKILL.md is roughly 13k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Nightly Eval Investigation?

Skills that share tags, products or a category with Nightly Eval Investigation: Analyzing Offline Evaluations (PostHog/posthog, 40k stars), Eval-Driven Development Harness (affaan-m/ECC, 276k stars), Eval (alirezarezvani/claude-skills, 28k stars) and Eval Harness (affaan-m/ECC, 276k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Nightly Eval Investigation?

GoogleChrome (a GitHub organization) maintains it in GoogleChrome/modern-web-guidance-src, which has 1,143 GitHub stars. The repository holds 12 skills in this directory. The repository was last updated on October 10, 2026.

Source: GoogleChrome/modern-web-guidance-src on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.