Analyze Usage
openshift-eng/ai-helpers
A skill your agent uses when analyzing BigQuery usage patterns, costs, and query performance for a GCP project
Analyzes BigQuery slot use, query costs and execution bottlenecks from INFORMATION_SCHEMA to diagnose slow queries, slot contention and unpartitioned scans.
Install BigQuery Slot and Cost Optimizer
$ npx skills add google/skills --skill bigquery-slot-cost-optimizer -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install google/skills bigquery-slot-cost-optimizer --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/google/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/cloud/bigquery-slot-cost-optimizer .claude/skills/bigquery-slot-cost-optimizer && rm -rf skills-srcUse ~/.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/
Install the "bigquery-slot-cost-optimizer" agent skill from https://github.com/google/skills/tree/main/skills/cloud/bigquery-slot-cost-optimizer into .claude/skills/bigquery-slot-cost-optimizer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bigquery-slot-cost-optimizer", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/google/skills/tree/main/skills/cloud/bigquery-slot-cost-optimizerType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add google/skills --skill bigquery-slot-cost-optimizer -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install google/skills bigquery-slot-cost-optimizer --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/google/skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/cloud/bigquery-slot-cost-optimizer .agents/skills/bigquery-slot-cost-optimizer && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "bigquery-slot-cost-optimizer" agent skill from https://github.com/google/skills/tree/main/skills/cloud/bigquery-slot-cost-optimizer into .agents/skills/bigquery-slot-cost-optimizer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bigquery-slot-cost-optimizer", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add google/skills --skill bigquery-slot-cost-optimizer -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install google/skills bigquery-slot-cost-optimizer --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/google/skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/cloud/bigquery-slot-cost-optimizer .cursor/skills/bigquery-slot-cost-optimizer && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "bigquery-slot-cost-optimizer" agent skill from https://github.com/google/skills/tree/main/skills/cloud/bigquery-slot-cost-optimizer into .cursor/skills/bigquery-slot-cost-optimizer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bigquery-slot-cost-optimizer", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/google/skills.git --path skills/cloud/bigquery-slot-cost-optimizer--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add google/skills --skill bigquery-slot-cost-optimizer -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install google/skills bigquery-slot-cost-optimizer --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/google/skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/cloud/bigquery-slot-cost-optimizer .gemini/skills/bigquery-slot-cost-optimizer && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "bigquery-slot-cost-optimizer" agent skill from https://github.com/google/skills/tree/main/skills/cloud/bigquery-slot-cost-optimizer into .gemini/skills/bigquery-slot-cost-optimizer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bigquery-slot-cost-optimizer", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install google/skills bigquery-slot-cost-optimizerInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add google/skills --skill bigquery-slot-cost-optimizer -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/google/skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/cloud/bigquery-slot-cost-optimizer .github/skills/bigquery-slot-cost-optimizer && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "bigquery-slot-cost-optimizer" agent skill from https://github.com/google/skills/tree/main/skills/cloud/bigquery-slot-cost-optimizer into .github/skills/bigquery-slot-cost-optimizer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bigquery-slot-cost-optimizer", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add google/skills --skill bigquery-slot-cost-optimizer -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install google/skills bigquery-slot-cost-optimizer --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/google/skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/cloud/bigquery-slot-cost-optimizer .opencode/skills/bigquery-slot-cost-optimizer && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "bigquery-slot-cost-optimizer" agent skill from https://github.com/google/skills/tree/main/skills/cloud/bigquery-slot-cost-optimizer into .opencode/skills/bigquery-slot-cost-optimizer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bigquery-slot-cost-optimizer", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
bigquery-slot-cost-optimizer- GitHub stars
- 21k
- Token cost
- ~2.3k tokens
- SKILL.md length
- 633 words
- Files
- 4 (incl. scripts, references)
- Skills in repo
- 147
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Analyzes BigQuery slot use, query costs and execution bottlenecks from INFORMATION_SCHEMA to diagnose slow queries, slot contention and unpartitioned scans.
- Works in 6 steps: Cloud SDK and client library installation → Project, billing, and regional selection → API enablement → …
- Diagnosing slow BigQuery queries or slot starvation
- SKILL.md covers Trigger conditions and intent…, Prerequisites and environment…, Diagnostic execution workflow and Metric interpretation and…, plus 2 more sections
- Runs Python scripts from its folder; calls python3
What it does
The skill gives an agent a procedure for working out where BigQuery resources go: calculating slot hours, spotting slot contention and queueing, catching Cartesian joins and row-count explosions, and finding scans of unpartitioned tables or missing partition filters. A `scripts/slot_analyzer.py` script does the analysis, with rules and remediation playbooks in two reference files.
Setup matters here. You need the Google Cloud CLI, the `google-cloud-bigquery` Python package, a project with an active billing account and the BigQuery API enabled, Application Default Credentials and the required IAM roles. INFORMATION_SCHEMA views are scoped to a region, so querying the wrong one returns empty job data; pass the matching `--region` and the script normalizes names like `us-central1`. For general BigQuery administration, BigQuery ML or DataFrame work, the skill points to sibling skills.
When your agent uses it
- Diagnosing slow BigQuery queries or slot starvation
- Finding the most expensive queries behind high on-demand costs
- Detecting unpartitioned table scans or join explosions
Example prompts
- “Find the most expensive BigQuery queries in region-us over the last week.”
- “Diagnose slot contention in our BigQuery project and suggest fixes.”
- “Check this query for a Cartesian join and a missing partition filter.”
Requirements
- Google Cloud CLI with Application Default Credentials
- Python with the google-cloud-bigquery package
- A project with billing and the BigQuery API enabled
Workflow steps
6 steps, taken from the first numbered list in SKILL.md.
- Cloud SDK and client library installation
- Project, billing, and regional selection
- API enablement
- Authentication setup
- IAM roles and permissions
- Pricing reference
What it can do on your machine
Read from SKILL.md and the folder at commit 5120a76. 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 1 file in scripts/ (Python), which the agent can run.
Shell commands in SKILL.md call:
python3
From the folder's file list and the shell code blocks in SKILL.md.
Network
Links to these hosts (documentation or services it may open):
docs.cloud.google.comcloud.google.com
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
BigQuery Slot and Cost Optimizer loads about 2.3k tokens when it runs, and up to ~8.2k if it reads all its reference files. Until then it costs about 110 tokens; SKILL.md has 633 words of instructions outside code blocks.
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 google/skills at commit 5120a76, republished under its Apache-2.0 licence (© google). 633 words, ~2,264 tokens.
Download SKILL.mdSave it as
.claude/skills/bigquery-slot-cost-optimizer/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.- name
- bigquery-slot-cost-optimizer
- description
- Analyzes Google Cloud BigQuery slot consumption, query costs, and execution bottlenecks using INFORMATION_SCHEMA. Use when diagnosing slow BigQuery queries, slot starvation, high on-demand query costs, unpartitioned table scans, or join performance issues. Don't use for generic BigQuery administration (use bigquery-basics), BigQuery ML (use bigquery-ai-ml), or DataFrame operations (use bigquery-bigframes).
- metadata.version
- 1.0.0
- metadata.publisher
- metadata.category
- BigDataAndAnalytics
- metadata.tags
- bigquery, performance, cost-optimization, slot-analysis, sql
BigQuery slot and cost optimizer
This skill equips AI agents and cloud engineers with procedural heuristics to analyze BigQuery resource consumption, calculate slot hours, identify slot contention and queueing, mitigate Cartesian joins, and optimize unpartitioned table scans.
Trigger conditions and intent mapping
Activate this skill whenever the user asks to:
- "Optimize BigQuery query performance or reduce slot usage"
- "Find the most expensive queries in BigQuery"
- "Diagnose BigQuery slot contention or queueing"
- "Fix slow running BigQuery jobs or memory spillage"
- "Detect Cartesian joins or row count explosions in BigQuery"
- "Identify unpartitioned table scans or missing partition filters"
Prerequisites and environment setup
Before executing this skill, ensure the environment is configured with the necessary SDKs, permissions, and billing:
Cloud SDK and client library installation:
Install the Google Cloud CLI: Google Cloud SDK installation guide
Install the BigQuery Python client:
bash pip install google-cloud-bigquery
Project, billing, and regional selection:
Set the active project:
bash gcloud config set project <PROJECT_ID>Important: the target Google Cloud project must have an active Cloud Billing account attached.
Regional selection: specify the target BigQuery dataset location or execution region, as BigQuery
INFORMATION_SCHEMAviews are strictly region-scoped (for example, multi-regions likeregion-usorregion-eu, or single regions likeregion-us-central1). Querying the wrong region returns empty job telemetry. Pass the matching region via--region(the script automatically normalizes location names likeus-central1toregion-us-central1). For valid location identifiers, see BigQuery locations.
API enablement:
Enable the BigQuery API on the project:
bash gcloud services enable bigquery.googleapis.com
Authentication setup:
Authenticate the local gcloud environment and configure Application Default Credentials (ADC):
bash gcloud auth login gcloud auth application-default login
IAM roles and permissions:
- The executing principal requires the following minimum IAM roles:
roles/bigquery.jobUser: grants permission to run queries and analyze telemetry.roles/bigquery.resourceViewer: grants read-only access to query metadata inINFORMATION_SCHEMA.JOBS_BY_PROJECTand capacity reservations.
- The executing principal requires the following minimum IAM roles:
Pricing reference:
- Cost estimates in this skill are for planning purposes. Before running
scripts/slot_analyzer.py, retrieve live BigQuery billing rates at runtime from official Google Cloud BigQuery Pricing (and consult BigQuery editions introduction for edition capabilities) after considering user-specific parameters such as target region, chosen edition (Standard,Enterprise,Enterprise Plus), and commitment tier (Pay-as-you-go,1-year,3-year). Pass these runtime-fetched rates explicitly via--ondemand-rate <USD_PER_TIB>and--slot-hour-rate <USD_PER_SLOT_HOUR>.
- Cost estimates in this skill are for planning purposes. Before running
Diagnostic execution workflow
Show full SKILL.md (277 more words)Show less
Execute automated telemetry extraction
Run scripts/slot_analyzer.py to pull and analyze historical query telemetry from INFORMATION_SCHEMA.JOBS_BY_PROJECT, passing the runtime-retrieved pricing rates for your specific region, edition, and commitment tier:
# General analysis passing live regional pricing rates fetched from BigQuery pricing
python3 scripts/slot_analyzer.py --project-id <PROJECT_ID> --days 7 \
--ondemand-rate <USD_PER_TIB> --slot-hour-rate <USD_PER_SLOT_HOUR> --format table
# Output structured JSON for programmatically parsing recommendations
python3 scripts/slot_analyzer.py --project-id <PROJECT_ID> --days 7 \
--ondemand-rate <USD_PER_TIB> --slot-hour-rate <USD_PER_SLOT_HOUR> --format json
# Offline verification mode using synthetic or extracted telemetry
python3 scripts/slot_analyzer.py --mock-data-file path/to/extracted_telemetry.json \
--ondemand-rate <USD_PER_TIB> --slot-hour-rate <USD_PER_SLOT_HOUR> --format table
# Dry-run mode to inspect regional SQL query
python3 scripts/slot_analyzer.py --project-id <PROJECT_ID> --region region-us --dry-runRun python3 scripts/slot_analyzer.py --help to inspect all supported CLI flags, focus modes (--mode), and required pricing rate arguments (--ondemand-rate per TiB and --slot-hour-rate per slot-hour).
Metric interpretation and decision tree
Evaluate the telemetry output using the following decision rules. CRITICAL MANDATE: After classifying the query issue using the decision tree below, you MUST immediately call view_file on references/remediation_playbooks.md to read and execute the corresponding remediation playbook (Rule SLOT-001, Rule JOIN-001, or Rule PART-001) and include all mandatory diagnostic SQL queries and 4-step checklists in your response.
[Query Telemetry Analyzed]
|
+---> If wait_ratio_avg > 0.40 OR slot_contention == TRUE
| --> Classify as slot contention and queueing (Rule SLOT-001)
| --> MANDATORY: Read Rule SLOT-001 in references/remediation_playbooks.md
|
+---> If shuffle_output_bytes_spilled > 0 OR records_written > 10 * records_read
| --> Classify as Cartesian join (Rule JOIN-001)
| --> MANDATORY: Read Rule JOIN-001 in references/remediation_playbooks.md
|
+---> If total_bytes_billed > 10 GB AND no date/partition filters
| --> Classify as unpartitioned scan (Rule PART-001)
| --> MANDATORY: Read Rule PART-001 in references/remediation_playbooks.md
|
+---> Otherwise
--> Check BI Engine, search indexes, or materialized view opportunities
--> MANDATORY: Read references/optimization_rules.mdRemediation playbooks and architectural reference links
To minimize token consumption in SKILL.md, concrete remediation playbooks (Rule SLOT-001, Rule JOIN-001, Rule PART-001), diagnostic SQL queries, and DDL rewrite patterns are housed in references/:
- Rule SLOT-001 (Slot contention and queueing): Rule SLOT-001 playbook
- Rule JOIN-001 (Cartesian and exploding joins): Rule JOIN-001 playbook
- Rule PART-001 (Unpartitioned scans and partition pruning): Rule PART-001 playbook
- Table partitioning, multi-column clustering, and BI Engine: optimization rules
- BigQuery search indexes and materialized views: optimization rules
Verification and validation protocol
Before finalizing query rewrites:
Dry-run validation
Validate query syntax and calculate estimated bytes scanned without incurring cost:
from google.cloud import bigquery
client = bigquery.Client()
job_config = bigquery.QueryJobConfig(dry_run=True, use_query_cache=False)
query_job = client.query(optimized_sql, job_config=job_config)
print(f"Scanned bytes: {query_job.total_bytes_processed / (1024**3):.2f} GB")Offline and dry-run validation
Offline mock telemetry verification: validate heuristic classification, slot contention detection, Cartesian join identification, and cost estimation offline using synthetic or extracted JSON telemetry payloads (
--mock-data-file):bash python3 scripts/slot_analyzer.py --mock-data-file path/to/extracted_telemetry.json \ --ondemand-rate <USD_PER_TIB> --slot-hour-rate <USD_PER_SLOT_HOUR> --format tableCLI dry-run inspection: verify regional SQL query formation and script execution without contacting BigQuery or incurring costs:
bash python3 scripts/slot_analyzer.py --project-id <PROJECT_ID> --region region-us --dry-run
© google, 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 3 other files (scripts, references) in skills/cloud/bigquery-slot-cost-optimizer of google/skills.
- SKILL.md
- references/optimization_rules.md
- references/remediation_playbooks.md
- scripts/slot_analyzer.py
Open the folder on GitHubat commit 5120a76
Compare with similar skills
BigQuery Slot and Cost Optimizer next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| BigQuery Slot and Cost Optimizer this skillgoogle/skills | 21k | — | ~2.3k | Automated safety check: Pass | Apache-2.0 | |
| Analyze Usageopenshift-eng/ai-helpers | 120 | — | ~2.7k | Automated safety check: Pass | Apache-2.0 | |
| Io ConnectorsKilo-Org/kilo-marketplace | 190 | — | ~1.3k | Automated safety check: Pass | Apache-2.0 | |
| Altimate Data Warehouse DelegateAltimateAI/data-engineering-skills | 128 | — | ~1.4k | Automated safety check: Pass | MIT | |
| Expensive Snowflake Query FinderAltimateAI/data-engineering-skills | 128 | — | ~662 | Automated safety check: Pass | MIT | |
| Deploying On GCPancoleman/ai-design-components | 526 | — | ~3.9k | Automated safety check: Pass | MIT |
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Questions about BigQuery Slot and Cost Optimizer
What does BigQuery Slot and Cost Optimizer do?
Analyzes BigQuery slot use, query costs and execution bottlenecks from INFORMATION_SCHEMA to diagnose slow queries, slot contention and unpartitioned scans. The skill gives an agent a procedure for working out where BigQuery resources go: calculating slot hours, spotting slot contention and queueing, catching Cartesian joins and row-count explosions, and finding scans of unpartitioned tables or missing partition filters.py` script does the analysis, with rules and remediation playbooks in two reference files.
When should I use BigQuery Slot and Cost Optimizer?
BigQuery Slot and Cost Optimizer fits situations like: diagnosing slow BigQuery queries or slot starvation; finding the most expensive queries behind high on-demand costs; detecting unpartitioned table scans or join explosions.
How do I install BigQuery Slot and Cost Optimizer in Claude Code?
Run `npx skills add google/skills --skill bigquery-slot-cost-optimizer -a claude-code`. Or copy the skill folder (skills/cloud/bigquery-slot-cost-optimizer in google/skills) into .claude/skills/bigquery-slot-cost-optimizer in your project. Claude Code loads it when a task matches its description.
How do I install BigQuery Slot and Cost Optimizer in Codex?
Run `npx skills add google/skills --skill bigquery-slot-cost-optimizer -a codex`. Or copy the skill folder (skills/cloud/bigquery-slot-cost-optimizer in google/skills) into .agents/skills/bigquery-slot-cost-optimizer in your project. Codex loads it when a task matches its description.
Can I use BigQuery Slot and Cost Optimizer 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 google/skills --skill bigquery-slot-cost-optimizer -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/bigquery-slot-cost-optimizer, .gemini/skills/bigquery-slot-cost-optimizer, .github/skills/bigquery-slot-cost-optimizer and .opencode/skills/bigquery-slot-cost-optimizer in your project.
What does BigQuery Slot and Cost Optimizer need to run?
Going by SKILL.md and its folder, BigQuery Slot and Cost Optimizer needs Python for the scripts in its folder and the command-line tools its instructions call (python3). Our summary lists: Google Cloud CLI with Application Default Credentials; Python with the google-cloud-bigquery package; A project with billing and the BigQuery API enabled.
Does BigQuery Slot and Cost Optimizer access the network?
SKILL.md names 2 domains. As links in the text: docs.cloud.google.com and cloud.google.com. This is read from the text; nothing was executed.
Is BigQuery Slot and Cost Optimizer 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 BigQuery Slot and Cost Optimizer use?
BigQuery Slot and Cost Optimizer 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 BigQuery Slot and Cost Optimizer use?
About 2.3k tokens (SKILL.md is roughly 9.1k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 5.9k tokens, read only when the agent opens those files.
What are the alternatives to BigQuery Slot and Cost Optimizer?
Skills that share tags, products or a category with BigQuery Slot and Cost Optimizer: Analyze Usage (openshift-eng/ai-helpers, 120 stars), Io Connectors (Kilo-Org/kilo-marketplace, 190 stars), Altimate Data Warehouse Delegate (AltimateAI/data-engineering-skills, 128 stars) and Expensive Snowflake Query Finder (AltimateAI/data-engineering-skills, 128 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains BigQuery Slot and Cost Optimizer?
google (a GitHub organization, an official publisher) maintains it in google/skills, which has 21,069 GitHub stars. The repository holds 147 skills in this directory. The repository was last updated on October 9, 2026.
Source: google/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.