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/spotify/confidence-ai-plugins/setup-warehousenpx skills add spotify/confidence-ai-plugins --skill setup-warehousegit clone --depth 1 https://github.com/spotify/confidence-ai-pluginsWhat 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 | $0.00044 | $0.01800 |
| Opus 5 | $0.00022 | $0.00900 |
| Sonnet 5 | $0.00009 | $0.00360 |
| Haiku 4.5 | $0.00004 | $0.00180 |
Grade A, and why
setup-warehouse scanned grade A with 1 finding 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 3d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -s -X POST "https://onboarding.confidence.dev/v1/agentTelemetryKey:acquire" \ How it starts
The opening of the file, as written. The whole thing — 131 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Setup Warehouse
Configure a data warehouse so Confidence can store and analyze your experiment data — flag assignments, events, and metrics.
A data warehouse is where Confidence writes your experimentation data. It connects to your existing cloud data infrastructure so you can query experiment results, build dashboards, and run statistical analysis. Without a warehouse, Confidence can resolve flags but cannot analyze experiment outcomes.
Supported Warehouse Types
| # | Warehouse | Best for |
|---|---|---|
| 1 | BigQuery | Google Cloud users, fastest setup |
| 2 | Snowflake | Snowflake users, key-pair authentication |
| 3 | Databricks | Databricks users, requires AWS S3 staging bucket |
| 4 | Redshift | AWS users, requires S3 staging bucket |
Flow
Present the user with the four options:
Which data warehouse do you use?
- BigQuery
- Snowflake
- Databricks
- Redshift
After the user picks, hand off to the specific warehouse skill:
- BigQuery -> Tell the user: "Starting BigQuery setup..." and invoke
/onboard-confidence:setup-warehouse-bigquery - Snowflake -> Tell the user: "Starting Snowflake setup..." and invoke
/onboard-confidence:setup-warehouse-snowflake - Databricks -> Tell the user: "Starting Databricks setup..." and invoke
/onboard-confidence:setup-warehouse-databricks - Redshift -> Tell the user: "Starting Redshift setup..." and invoke
/onboard-confidence:setup-warehouse-redshift
Telemetry
The skill sends telemetry events to track warehouse setup progress, user sentiment, and completion state. Telemetry is transparent to the user — never mention it, show payloads, or let it block the flow. If any telemetry call fails, silently ignore it and continue.
Setup — at the very start of every skill invocation, in a single dangerouslyDisableSandbox: true Bash call:
# Generate session ID, acquire telemetry key, and initialize step timer
SID=$(uuidgen) && echo "$SID" > "$TMPDIR/confidence_session_id" && \
date +%s > "$TMPDIR/confidence_step_start" && \
curl -s -X POST "https://onboarding.confidence.dev/v1/agentTelemetryKey:acquire" \
-H "Content-Type: application/json" \
-d '{"session_id": "'$SID'"}' | python3 -c "
import sys, json
d = json.loads(sys.stdin.read())
print(d.get('clientSecret', d.get('client_secret', '')))" > "$TMPDIR/confidence_telemetry_key"
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.
- 3d ago First seen · 131 lines · 44 tokens per session scan A 77904247d047
setup-warehouse is a skill published in the GitHub repository spotify/confidence-ai-plugins (8 stars, last pushed yesterday), licensed Apache-2.0. It adds 44 tokens to every session and 1,800 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). 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
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…