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/saitarrun/devforge-ai/sdlc-plangit clone --depth 1 https://github.com/saitarrun/Devforge-aiWrote 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/saitarrun/devforge-ai/sdlc-plan)<a href="https://agentmods.dev/commands/saitarrun/devforge-ai/sdlc-plan"><img src="https://agentmods.dev/badge/commands/saitarrun/devforge-ai/sdlc-plan.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 | $0.00031 | $0.00676 |
| Opus 5 | $0.00015 | $0.00338 |
| Sonnet 5 | $0.00006 | $0.00135 |
| Haiku 4.5 | $0.00003 | $0.00068 |
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 3d 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 — 83 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/sdlc-plan — Phase 1: Plan
Conduct the grill-me interview, emit grill-summary.md + scope.json, publish a PRD, and create one issue per slice. Uses exactly 1 agent: product-manager.
Usage
/sdlc-plan "add a login page"
/sdlc-plan "build a reporting dashboard"
EXECUTION
STEP 1: Invoke product-manager
Spawn: Agent({
name: "product-manager",
description: "Product Manager — grill-me interview + slice decomposition",
prompt: "Feature request: <user's feature description>
Conduct the full grill-me interview (Phase A–D). Do not skip any phase.
After the interview resolves:
1. Write ./projects/<feature-name>/grill-summary.md with the full interview transcript
2. Decompose the feature into tracer bullet slices
3. Write ./projects/<feature-name>/scope.json with capability_flags + slices array
The first slice must always be type: prefactor (project scaffold + health check).
Subsequent slices are type: feature named from the user's perspective.
layers are derived from grill-me answers (has_ui → include 'ui', etc.)."
})
Wait for product-manager to complete. Verify:
./projects/<feature-name>/grill-summary.mdexists./projects/<feature-name>/scope.jsonexists and is valid JSON
STEP 2: Wait for product-manager to complete PRD + issues
The product-manager agent handles the PRD gate, synthesis, and issue creation directly (Steps 5–6 in its process). Wait for it to complete.
Verify on completion:
./projects/<feature-name>/docs/01-prd.mdexistsscope.jsonhaslinear_idorissue_refon every slice
STEP 3: Prompt — Ready to start Build?
✅ PRD published! [N] issues created.
Ready to start Build?
→ This will run /handoff (creating handoffs/plan-handoff.md) then invoke /sdlc-build.
→ Reply "yes" to proceed or "no" to pause here.
On confirm:
- Run
/handoff— stores compressed context at./projects/<feature-name>/handoffs/plan-handoff.md - Invoke
/sdlc-build
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.
- 3d ago First seen · 83 lines · 31 tokens per session scan A 656d55535de8
sdlc-plan is a command published in the GitHub repository saitarrun/Devforge-ai (5 stars, last pushed 20d ago), licensed Apache-2.0. It adds 31 tokens to every session and 676 once invoked, about $0.0002 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.
Other commands, from other repositories
feature-plan
Guide the agent to create or update a feature plan from a short feature request.
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video
description: Generate a video with Modellix from a prompt, an image, or a source video. argument-hint: [prompt] [optional image or video URL] disable-model-invocation: true.
doctor
Check the Modellix CLI install, credential resolution, API connectivity, and balance.
models
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tasks
Inspect Modellix task status and recover tasks after a timeout or unknown submission.