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 skills/zeeshanhanif/agentic-sdlc-kit/initial-deploymentnpx skills add zeeshanhanif/agentic-sdlc-kit --skill initial-deploymentgit clone --depth 1 https://github.com/zeeshanhanif/agentic-sdlc-kitWrote 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/skills/zeeshanhanif/agentic-sdlc-kit/initial-deployment)<a href="https://agentmods.dev/skills/zeeshanhanif/agentic-sdlc-kit/initial-deployment"><img src="https://agentmods.dev/badge/skills/zeeshanhanif/agentic-sdlc-kit/initial-deployment.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 | $0.00201 | $0.02755 |
| Opus 5 | $0.00101 | $0.01378 |
| Sonnet 5 | $0.00040 | $0.00551 |
| Haiku 4.5 | $0.00020 | $0.00276 |
Grade A, and why
initial-deployment 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 — 238 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Initial Deployment
The bridge from deploy-ready to running. Scaffolding wrote the deployment configs, environment parameterization, and CI — and stopped, by contract, at "the initial deployment is the user's step." This skill is that step, with the same character as scaffolding: real execution against live reality, empirical verification, honest notes, and checkpointed progress that never blindly re-provisions.
Three principles govern it:
- The target is an input, never a decision. The architecture's ADRs and deployment view chose the cloud/platform and topology; this skill reads and executes them. Gaps (a region never stated, a service tier unnamed) are elicited — and flagged as candidate architecture amendments. The process is cloud-agnostic; the run is cloud-specific; CLI names, flags, and console steps are verified against live provider docs at run time — never recited from memory (they drift faster than generators do).
- Money and credentials get gates. Provisioning creates billable resources: the deployment plan (what gets created, environments, rough cost class) is played back for one explicit confirmation before anything is created. Credentials stay in the user's hands: the skill preflights that the provider CLI is authenticated and blocks with instructions when it isn't — it never asks for, stores, or writes a credential or secret value anywhere (configs reference secret stores; values enter those stores through the provider's own mechanism, user-driven).
- Deployed means demonstrated. The run ends with the system observed live: the skeleton's end-to-end path exercised against the deployed environment (closing the done-when's pending deployed half), CD proven by an actual pipeline-driven deploy, restore actually performed once — claims are exhibits, observations are evidence.
When to run
Recommended early — right after scaffolding, deploying the walking skeleton itself: that's the walking-skeleton philosophy (prove the system deploys before features pile on), and it makes every later feature continuously deployable. Running later, after features exist, is fully supported — the same process deploys whatever the repo currently holds, and Phase 8 is more valuable then (accumulated pending-environment NFRs finally become measurable). Say the trade-off plainly when features exist: this first push ships N features' worth of system at once, so a failure has many more candidate causes than an early skeleton deploy would — and the E2E exercise in Phase 6 covers the whole current suite accordingly.
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 238 lines · 201 tokens per session scan A 483428da0f98
initial-deployment is a skill published in the GitHub repository zeeshanhanif/agentic-sdlc-kit (5 stars, last pushed 7d ago), licensed Apache-2.0. It adds 201 tokens to every session and 2,755 once invoked, about $0.0010 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.
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