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.
git clone --depth 1 https://github.com/MCKRUZ/claude-code-sdlcWrote 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/commands/mckruz/claude-code-sdlc/sdlc-data)<a href="https://agentmods.dev/commands/mckruz/claude-code-sdlc/sdlc-data"><img src="https://agentmods.dev/badge/commands/mckruz/claude-code-sdlc/sdlc-data/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/commands/mckruz/claude-code-sdlc/sdlc-data"><img src="https://agentmods.dev/badge/commands/mckruz/claude-code-sdlc/sdlc-data.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 | $0.00000 | $0.01113 |
| Opus 5.5 | $0.00000 | $0.00445 |
| Sonnet 5.5 | $0.00000 | $0.00223 |
| Haiku 4.5 | $0.00000 | $0.00111 |
Grade A, and why
sdlc-data 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 1mo 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 — 74 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/sdlc-data — Author the Data Contract, Readiness, and Lineage for a Feature
Give Data a first-class drafting seat: the data contract (PII-classified), the data-readiness
assessment, and the lineage / audit design for a feature or spec. It sharpens Scope, surfaces the
PII that drives risk tier, and turns readiness gaps into tracked decisions. Interview-driven like
/sdlc-coach: the data-analyst agent assesses what's there, asks focused questions, and drafts as
answers arrive. It proposes; a named human decides (the One Rule). Works inside an SDLC project
or standalone.
Instructions
-
Resolve context:
- Workflow mode (default):
.sdlc/state.yamlexists. Read the feature-brief / spec Scope and anydata-*artifacts; outputs land in.sdlc/artifacts/02-design/data/. - Standalone mode (
--repo <path>, or no.sdlc/found): operate on the given repo with provisional context; write to--output(default alongside the repo) and note the missing context in the artifact headers.
- Workflow mode (default):
-
Assess what exists: Read the feature-brief's Data touchpoints section, any success-criteria baseline, and the current data artifacts — which fields are named, which sources are known, what is still unclassified.
-
Run the interview: Spawn the
data-analystagent (Data discipline). It runs the coach-style dialogue — which fields the feature reads or writes, their sources and types, which are PII or customer-linked, whether the data is actually available and complete, and how it flows end to end (lineage + retention). It drafts three artifacts from the templates intemplates/phases/02-design/data/:data-contract.md— the field table with an explicit PII? column.data-readiness.md— availability / completeness / quality, with gaps flagged (advisory).lineage-audit.md— source → transform → sink flow with retention and audit points.
-
Confirm PII and route readiness gaps with the human:
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.
- 1mo ago First seen · 74 lines · 0 tokens per session scan A 63a25bd2be20
sdlc-data is a command published in the GitHub repository MCKRUZ/claude-code-sdlc (8 stars, last pushed 10d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,113 tokens. 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 2026-08-31.
Other commands, from other repositories
boot
Start a new PromptKit prompt assembly session (alias for /bootstrap).
music-suno-prompt
Grounded Suno prompt synthesis from local knowledge corpus + persona canon + label canon. No vibes-prompting.
audit-prompt
Evaluate an existing prompt for clarity, effectiveness, and edge cases.
laravel-ai-sdk
Build AI features with the first-party Laravel AI SDK (Laravel 13+); use the laravel:ai-sdk skill exactly as written.
dare-llm-integration
Integração segura e eficiente com LLMs (Gemini, Claude, OpenAI, Ollama) em projetos DARE.
vlm-ocr-evaluation
Run the vlm-ocr skill in its evaluate phase: compare candidate OCR systems against a stratified human-transcribed ground-truth sample and pick a model on measured CER/WER before committing to a bulk run.