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 saajunaid/caddis-plugin --skill data-lineagegit clone --depth 1 https://github.com/saajunaid/caddis-pluginWrote 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/saajunaid/caddis-plugin/data-lineage)<a href="https://agentmods.dev/skills/saajunaid/caddis-plugin/data-lineage"><img src="https://agentmods.dev/badge/skills/saajunaid/caddis-plugin/data-lineage/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/saajunaid/caddis-plugin/data-lineage"><img src="https://agentmods.dev/badge/skills/saajunaid/caddis-plugin/data-lineage.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 | $0.00038 | $0.02665 |
| Opus 5.5 | $0.00015 | $0.01066 |
| Sonnet 5.5 | $0.00008 | $0.00533 |
| Haiku 4.5 | $0.00004 | $0.00266 |
Grade A, and why
data-lineage 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 22d 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 — 159 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Lineage Skill
This skill generates End-to-End Data Lineage and Telemetry Specifications for enterprise applications. It bridges the gap between frontend user surfaces, backend API routes, relational database objects, and upstream data producers (ETLs, Stored Procedures, and SQL Agent jobs).
The resulting specifications serve as the ground truth for estate-wide observability in watch-sight to enable automated dependency graphs, health probing, and blast-radius impact analysis.
Mandatory Workflow Steps
Step 1: Codebase & Infrastructure Discovery
Conduct targeted research across the target application and observability repositories:
- Frontend Surfaces: Inspect
frontend/src/routes/or router configurations to identify all user-facing pages, views, and navigation tabs. - API Routes: Inspect
src/api/routes/or API routers to map every HTTP endpoint invoked by each frontend page. - Database Repositories & SQL Queries: Trace API controllers into data services and repositories to identify exact SQL tables, views, and columns accessed.
- Data Producers & Execution Timings (DB Server SPs & ETLs): Identify the scheduled jobs, batch Python scripts, and SQL Server Stored Procedures that populate, calculate, or refresh each dataset. Discover their exact execution timings, run frequencies, and triggers (e.g. SQL Server Agent schedules, Windows Task Scheduler jobs, cron expressions, email triggers, or polling loops from runbooks and pipeline schedule references).
- Physical Estate Host Resolution: Query
E:\Projects\watch-sight\config\monitor-targets.jsonanddocs/runbooks/estate-pipelines/to resolve physical server hostnames, IP addresses, ports, database instances, and service accounts.
Step 2: Markdown Document Authoring
Draft the canonical Data Lineage Markdown file:
docs/<app>/<app>-data-lineage.md
Follow the standard 5-section lineage structure detailed below.
Step 3: Confluence XHTML Compilation & Verification
Compile the Markdown document into Confluence Storage Format XHTML using:
python scripts/convert_doc_to_confluence.py docs/<app>/<app>-data-lineage.md --output docs/<app>/<app>-data-lineage.html --diagram-type plantuml
Verify that the output compiles as well-formed XML without errors.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 22d ago Changed 81f4929c2972
- 29d ago First seen · 159 lines · 38 tokens per session scan A 893602f2685f
data-lineage is a skill published in the GitHub repository saajunaid/caddis-plugin (0 stars, last pushed 2d ago), licensed MIT. It adds 38 tokens to every session and 2,665 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-09-19.
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