sdlc-implement

A command that implements one software task from either a Linear issue or a written request. Linear is a project-management tool for tracking software issues and their acceptance criteria.

In plain words
What is it for?
Use it to build or fix one feature, such as adding JWT refresh-token logic, from a Linear issue number or a plain-language specification.
Why use it?
It turns the task description into a checklist of completion requirements and uses a retry process to correct problems found during implementation.

Command

Install

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.

agentmods
npx agentmods add commands/saitarrun/devforge-ai/sdlc-implement
Clone the repo
git clone --depth 1 https://github.com/saitarrun/Devforge-ai
Per session 29 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,327 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00029 $0.01327
Opus 5 $0.00015 $0.00664
Sonnet 5 $0.00006 $0.00265
Haiku 4.5 $0.00003 $0.00133

Measured 2d ago against content hash c3cb64c75b48, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

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 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.

commands/sdlc-implement.md · 211 lines

How it starts

The opening of the file, as written. The whole thing — 211 lines — stays where its author put it; the contents beside it link to each section on GitHub.

/sdlc-implement — Standalone Issue Implementation

Implements a single Linear issue or free-form spec using fullstack-engineer + the ralph-loop skill for self-correcting retry logic. Works independently of the full /sdlc pipeline.

Usage

/sdlc-implement SAI-41                          # Linear mode: reads issue from Linear
/sdlc-implement "add JWT refresh token logic"   # Free-form mode: infers DoD from spec

STEP 1: Detect Input Mode

Inspect the argument:

  • If it matches the pattern [A-Z]+-[0-9]+ (e.g. SAI-41, LIN-123) → Linear mode
  • Otherwise → Free-form mode

STEP 2: Read Spec + Extract DoD

Linear Mode

Use the Linear MCP to fetch the issue:

mcp__linear-server__get_issue({ id: "<ARGUMENT>" })

Extract from the response:

  • Title — used as the commit subject
  • Description — full context for the engineer
  • Acceptance criteria — the checklist under "## Acceptance criteria" or "## DoD" becomes the Definition of Done

If no explicit AC section exists, infer DoD from the description: turn each "should", "must", or imperative statement into a checkbox.

Free-Form Mode

The argument string is the spec. Infer DoD by breaking the spec into verifiable outcomes:

  • What file(s) should exist or change?
  • What behaviour should be observable?
  • What should typecheck + tests confirm?

Write the inferred DoD as a checklist (3–5 items minimum).


STEP 3: Load Ralph Loop Skill

Read skills/ralph-loop/SKILL.md to load the self-correcting loop methodology into context before invoking the engineer.


STEP 4: Invoke fullstack-engineer

Spawn the fullstack-engineer agent with the following context:

Spawn: Agent({
  name: "fullstack-engineer",
  description: "Implement: <title or spec summary>",
  prompt: "
    ## Spec
    <full issue description or free-form spec>

    ## Definition of Done
    <DoD checklist extracted in Step 2>

    ## Instructions
    Implement the spec above. Apply the ralph-loop skill:

    ### Inner Loop (apply per implementation attempt)
    1. Run sentinel checks before verifying:
       - Context drift: are you still addressing the full spec?
       - Spec drift: are you writing code outside the declared scope?
    2. Implement the changes.
    3. Verification gateway:
       - Run typecheck: `npm run typecheck` or `tsc --noEmit` (adapt to project)
       - Run relevant tests: `npm test` or equivalent
    4. If all checks pass → DoD satisfied, stop.
    5. If checks fail → retry with fresh context:
       - Re-read the spec + DoD
       - Pin the failure output at the top of your reasoning
       - Do NOT carry forward previous reasoning
       - Max 2 retries. If still failing after 2 retries → stop and surface failure.

    ### Circuit Breaker
    After 2 failed retries: report what failed, what you tried, and what the blocking error is.
    Do not attempt further changes — surface to the user.
  "
})

Read the full file on GitHub · 211 lines

Changes

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.

  1. 2d ago First seen · 211 lines · 29 tokens per session scan A c3cb64c75b48

Subscribe to this mod's changes

sdlc-implement is a command published in the GitHub repository saitarrun/Devforge-ai (5 stars, last pushed 19d ago), licensed Apache-2.0. It adds 29 tokens to every session and 1,327 once invoked, about $0.0001 per session on Opus 5. 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.