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-auditgit 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-audit)<a href="https://agentmods.dev/commands/mckruz/claude-code-sdlc/sdlc-audit"><img src="https://agentmods.dev/badge/commands/mckruz/claude-code-sdlc/sdlc-audit.svg" alt="Measured on agentmods" 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 | $0.00000 | $0.00387 |
| Opus 5 | $0.00000 | $0.00193 |
| Sonnet 5 | $0.00000 | $0.00077 |
| Haiku 4.5 | $0.00000 | $0.00039 |
Grade A, and why
sdlc-audit 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 4d 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.
What it actually says
/sdlc-audit — Gate Effectiveness Analysis
Analyze gate results across completed phases in the current project's state.yaml to identify which gates are useful and which are candidates for adjustment.
Instructions
-
Locate state file: Look for
.sdlc/state.yamlin the current project directory. If not found, tell the user to run/sdlc-setupfirst. -
Read state: Load
.sdlc/state.yamland extractgate_resultsfrom every completed phase. -
Run audit analysis: Execute the audit script:
uv run --project ${CLAUDE_PLUGIN_ROOT}/scripts ${CLAUDE_PLUGIN_ROOT}/scripts/audit_gates.py --state .sdlc/state.yaml -
Display results: Show the audit report with:
- Gate effectiveness summary — table of every gate that was checked, how many times it ran, and how many times it failed
- Always-pass gates — gates that never failed across all phases (candidates for removal or tightening)
- High-fail gates — gates that failed frequently (may indicate process issues or overly strict thresholds)
- Override history — any gates that were overridden, with justification text
- Recommendations — suggested actions based on the analysis
-
Optionally compare profiles: If
--compare <other-state.yaml>is provided, compare gate effectiveness between two projects.
Arguments
- No arguments: audit current project
--compare <path>: compare with another project's state.yaml
Important
- This command is read-only — it analyzes existing data, never modifies state.
- Useful gate auditing requires at least 3-4 completed phases to have meaningful data. If fewer phases are complete, warn the user that results may not be representative.
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.
- 4d ago First seen · 32 lines · 0 tokens per session scan A 141f3714b7f6
sdlc-audit is a command published in the GitHub repository MCKRUZ/claude-code-sdlc (4 stars, last pushed 6d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 387 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
git
Git operations with intelligent commit messages and workflow optimization.
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.
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.