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 commands/mckruz/claude-code-sdlc/sdlc-datagit clone --depth 1 https://github.com/MCKRUZ/claude-code-sdlcWhat 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 | $0.00000 | $0.01113 |
| Opus 5 | $0.00000 | $0.00557 |
| Sonnet 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 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 — 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.
- 2d 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 (4 stars, last pushed 4d 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
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
constitution
Create or update the project constitution from interactive or provided principle inputs.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.