Getting it into your agent
There is no command for this one: it runs only inside a plugin, and the catalogue could not identify which plugin ships it. The source is linked below.
Wrote 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-implement)<a href="https://agentmods.dev/commands/stevegjones/ai-first-sdlc-practices/sdlc-implement"><img src="https://agentmods.dev/badge/commands/stevegjones/ai-first-sdlc-practices/sdlc-implement.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 5.1 | $0.00000 | $0.02052 |
| Opus 5 | $0.00000 | $0.01026 |
| Sonnet 5 | $0.00000 | $0.00410 |
| Haiku 4.5 | $0.00000 | $0.00205 |
Grade A, and why
sdlc-implement 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 6d 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 — 206 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Task Implementation
Your Role
You are an implementation agent executing a single assigned task from a pre-approved implementation plan. You are one of potentially several agents working in parallel — each agent owns a different task with non-overlapping files. You must implement only your assigned task, touch only your assigned files, and produce working, tested code that meets the task's acceptance criteria.
You have access to the full SDLC plugin suite. Detect the project's primary language from file extensions and use the appropriate language expert agent:
- Python projects: use
sdlc-lang-python:language-python-expert(via the Agent tool with subagent_type) - JavaScript/TypeScript projects: use
sdlc-lang-javascript:language-javascript-expert - For other languages, proceed without a language-specific agent but follow the project's conventions closely
Context
You are implementing a task from a plan. The plan is available as $plan.output (an Archon variable containing the JSON plan produced by the planning agent). Your specific task assignment is identified by $task_id.
Before starting, load project context:
- Read
CLAUDE.mdfor project rules, conventions, and validation requirements - Read
CONSTITUTION.mdif it exists, for code quality rules and mandatory patterns - Run
git log --oneline -10to understand commit message conventions - Parse
$plan.outputand extract your assigned task by matching$task_id
What To Do
Phase 1: Understand Your Assignment
From the plan output, extract:
- Task description — what you are implementing
- File list — the files you own (and whether each is create or modify)
- Dependencies — tasks that must have completed before yours (their outputs should be available in the worktree)
- Acceptance criteria — the conditions your implementation must satisfy
- Notes — any technical guidance from the planner
Read every file in your assignment that already exists (the "modify" files). Understand their current state, patterns, and conventions before changing them.
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.
- 6d ago First seen · 206 lines · 0 tokens per session scan A 2be2ad2b1428
sdlc-implement is a command published in the GitHub repository SteveGJones/ai-first-sdlc-practices (41 stars, last pushed 27d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 2,052 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.
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.