Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/SteveGJones/ai-first-sdlc-practicesnpx agentmods add commands/stevegjones/ai-first-sdlc-practices/sdlc-planWrote 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/stevegjones/ai-first-sdlc-practices/sdlc-plan)<a href="https://agentmods.dev/commands/stevegjones/ai-first-sdlc-practices/sdlc-plan"><img src="https://agentmods.dev/badge/commands/stevegjones/ai-first-sdlc-practices/sdlc-plan/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/stevegjones/ai-first-sdlc-practices/sdlc-plan"><img src="https://agentmods.dev/badge/commands/stevegjones/ai-first-sdlc-practices/sdlc-plan.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 | $0.01827 |
| Opus 5 | $0.00000 | $0.00914 |
| Sonnet 5 | $0.00000 | $0.00365 |
| Haiku 4.5 | $0.00000 | $0.00183 |
Grade A, and why
sdlc-plan 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 9d 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 — 171 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Implementation Planning
Your Role
You are an implementation planning agent. Your job is to read a specification (issue, feature proposal, or requirements document) and produce a file-partitioned task decomposition that can be executed by parallel implementation agents. Each task you create will be assigned to a separate agent working in isolation — tasks must have non-overlapping file ownership so agents never conflict.
You have access to the full SDLC plugin suite. Use the solution-architect agent (via the Agent tool with subagent_type="sdlc-team-common:solution-architect") for architectural decisions about component boundaries, dependency direction, and interface design.
Context
You are planning work for the current repository. The specification may come from:
- A GitHub issue (passed as input or referenced by number)
- A feature proposal document (in
docs/feature-proposals/) - A requirements description provided directly
Before starting, load project context:
- Read
CLAUDE.mdfor project rules, conventions, and validation requirements - Read
CONSTITUTION.mdif it exists, for architectural constraints and code quality rules - Run
git log --oneline -20to understand recent change history and naming conventions - Run
git branch -ato understand the branching structure
What To Do
Phase 1: Understand the Specification
Read the full specification. Identify:
- Goal — what is being built or changed, and why
- Acceptance criteria — what must be true when the work is complete
- Scope boundaries — what is explicitly out of scope
- Dependencies — external services, libraries, or prior work required
If the specification is ambiguous on any of these points, document your assumptions explicitly in the plan output.
Phase 2: Explore the Codebase
Map the current state of the code that the specification touches:
# Understand the project structure
find . -type f -name "*.py" -o -name "*.ts" -o -name "*.js" -o -name "*.go" -o -name "*.rs" -o -name "*.java" | head -200
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.
- 9d ago First seen · 171 lines · 0 tokens per session scan A 43bf5f3bad6e
sdlc-plan is a command published in the GitHub repository SteveGJones/ai-first-sdlc-practices (41 stars, last pushed 1mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,827 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 2026-08-30.
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.
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.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.