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/djscheuf/agentic-dev-ecosystem-template/sdlc-analysisnpx skills add djscheuf/agentic-dev-ecosystem-template --skill sdlc-analysisgit clone --depth 1 https://github.com/djscheuf/agentic-dev-ecosystem-templateWrote 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/djscheuf/agentic-dev-ecosystem-template/sdlc-analysis)<a href="https://agentmods.dev/skills/djscheuf/agentic-dev-ecosystem-template/sdlc-analysis"><img src="https://agentmods.dev/badge/skills/djscheuf/agentic-dev-ecosystem-template/sdlc-analysis.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 | $0.00033 | $0.00254 |
| Opus 5 | $0.00016 | $0.00127 |
| Sonnet 5 | $0.00007 | $0.00051 |
| Haiku 4.5 | $0.00003 | $0.00025 |
Grade A, and why
sdlc-analysis 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.
What it actually says
SDLC Analysis Workflow
Purpose
Analyze a user story to understand requirements, identify edge cases, assess complexity, and prepare for implementation.
Trigger
- Starting a new feature or bug fix
- Before writing any production code
- When requirements need clarification
Inputs Required
- User story or requirement description
- Acceptance criteria (if available)
- Relevant domain context
Workflow Steps
Step 1: Parse the User Story
extract user-story from the provided document or prompt using the extract-story-intent skill.
Address any verification errors before proceeding.
Step 2: Analyze the User Story
expand the user-story into a comprehensive analysis document using the expand-story-analysis skill on the extracted intent file from the previous step.
Address any verification errors before proceeding.
Step 3: Grade the Analysis
grade the analysis based on the User Story Quality Rubric using the grade-story-analysis skill on the analysis file from the previous step.
Address any verification errors before proceeding.
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 · 37 lines · 33 tokens per session scan A 5dcb11d17676
sdlc-analysis is a skill published in the GitHub repository djscheuf/agentic-dev-ecosystem-template (12 stars, last pushed 3d ago), licensed MIT. It adds 33 tokens to every session and 254 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 2026-08-30.
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