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 skills add ai-analyst-lab/ai-analyst --skill setup-dev-contextgit clone --depth 1 https://github.com/ai-analyst-lab/ai-analystWrote 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/ai-analyst-lab/ai-analyst/setup-dev-context)<a href="https://agentmods.dev/skills/ai-analyst-lab/ai-analyst/setup-dev-context"><img src="https://agentmods.dev/badge/skills/ai-analyst-lab/ai-analyst/setup-dev-context/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/ai-analyst-lab/ai-analyst/setup-dev-context"><img src="https://agentmods.dev/badge/skills/ai-analyst-lab/ai-analyst/setup-dev-context.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00218 | $0.02031 |
| Opus 5 | $0.00109 | $0.01015 |
| Sonnet 5 | $0.00044 | $0.00406 |
| Haiku 4.5 | $0.00022 | $0.00203 |
Grade A, and why
setup-dev-context 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 2d 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 — 245 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/setup-dev-context — Developer Context Setup
Standalone skill for teams integrating AI Analyst into development workflows. Most users (PMs, execs, DS) never need this — only teams doing codebase integration.
Trigger
Invoked as /setup-dev-context
Purpose
Collects codebase-specific context to help AI Analyst understand your development environment. This enables more accurate SQL generation, schema awareness, and integration with your existing data infrastructure.
Sequence
Run the steps in order: the interview confirms values before anything is written.
Step 0: Check Prerequisites (ALWAYS FIRST)
Before doing anything else, verify basic setup is complete:
# Read setup state file
cat .knowledge/setup-state.yaml
Look for phase_2.status: complete. If missing or status is not "complete":
⚠️ Basic setup not complete. Please run `/setup` first to configure your profile and data connection.
You need to complete the initial setup before configuring development context.
STOP here. Do not proceed to interview.
If setup is complete, continue to Step 1.
Why this matters: Dev context assumes a dataset already exists. Without Phase 1-2 setup, there's no dataset to configure conventions for. Checking prerequisites prevents configuration errors downstream.
Step 1: Run the Interview
Ask all 5 questions even when the user supplied some answers, pre-filling what they said for confirmation — users often give partial or mistaken details (e.g. "Snowflake" meaning a Snowflake schema in Postgres).
Present all 5 questions at once (not one-by-one):
I'll ask a few questions about your development environment to provide better support.
1. **Repository type:** What kind of codebase is this?
- [ ] Analytics/data warehouse (dbt, SQL files, ETL)
- [ ] Application backend (API, services)
- [ ] Full-stack application
- [ ] Data science / ML project
- [ ] Other: ___
2. **Data layer:** How is your data organized?
- Database type: (Postgres, BigQuery, Snowflake, DuckDB, other)
- Schema naming convention: (e.g., `analytics.`, `public.`, `dbt_prod.`)
- Key tables location: (path to schema definitions, dbt models, etc.)
3. **SQL conventions:** Does your team follow specific patterns?
- Naming: snake_case / camelCase / other
- Date handling: timezone-aware? Default timezone?
- NULL handling: COALESCE patterns? Default values?
- Any team-specific SQL style guide? (path or URL)
4. **Integration points:** Where does AI Analyst fit in your workflow?
- [ ] Ad-hoc analysis only (no integration needed)
- [ ] Reads from dbt models
- [ ] Connects to production replica
- [ ] Uses exported CSV/Parquet files
- [ ] Accesses data warehouse directly
- Other: ___
5. **File conventions:** (optional)
- Where do analysis outputs go? (default: `outputs/`)
- Any naming conventions for SQL files?
- Git branch strategy for analysis work?
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.
- 2d ago First seen · 245 lines · 218 tokens per session scan A b23404ef2fba
setup-dev-context is a skill published in the GitHub repository ai-analyst-lab/ai-analyst (298 stars, last pushed 3d ago), licensed MIT. It adds 218 tokens to every session and 2,031 once invoked, about $0.0011 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 2026-09-12.
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