Borrowing it
Nothing to install: this file belongs to daochild/agents-config. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/daochild/agents-config/main/.agents/skills/sdlc-regulatory/SKILL.mdgit clone --depth 1 https://github.com/daochild/agents-configWrote 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/daochild/agents-config/sdlc-regulatory)<a href="https://agentmods.dev/skills/daochild/agents-config/sdlc-regulatory"><img src="https://agentmods.dev/badge/skills/daochild/agents-config/sdlc-regulatory/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/daochild/agents-config/sdlc-regulatory"><img src="https://agentmods.dev/badge/skills/daochild/agents-config/sdlc-regulatory.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00091 | $0.06851 |
| Opus 5.5 | $0.00036 | $0.02740 |
| Sonnet 5.5 | $0.00018 | $0.01370 |
| Haiku 4.5 | $0.00009 | $0.00685 |
Grade A, and why
sdlc-regulatory 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 1mo 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 — 515 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI-Driven SDLC Regulatory Framework
AI accelerates execution, SDLC controls correctness, risk, and accountability. Source of truth: this skill. It supersedes and absorbs the deleted
SOW.md(AI-Driven Software Development Lifecycle); theSOW §…section labels below are historical provenance from that document, kept to trace each control to its origin.
This skill is the methodology layer consumed by the sdlc-* subagents (BMad-style role agents) and enforced by the
sdlc-gates always-on rule. It encodes: stages, quality gates, risk-based autonomy caps, AI vs human responsibility
split, change traceability, Definition of Done, key risks, success metrics, and a compliance crosswalk.
Anti-pattern (SOW §Why We Need This)
The failure mode this framework exists to prevent:
Requirement → AI → Code → Merge
That flow has no analysis, no validation, no review, and no accountability. AI-driven development adds risks beyond human mistakes: hallucinations, architectural drift, inconsistent implementations, AI-generated technical debt, missing edge cases, security vulnerabilities, over-engineering, incorrect database changes, AI modifying unrelated components, AI reviewing/validating its own incorrect implementation, and code volume outpacing human review capacity. Every gate below exists to block one or more of these failure modes. If a proposed shortcut skips a gate, it is regressing toward this anti-pattern — refuse it.
AI across the SDLC (SOW §1 — Integrate AI Across the SDLC)
AI must participate in all appropriate engineering stages, not just coding. The 13 SOW activities map as follows.
Linear stages (owned by one BMad role each → one gate)
| SOW activity | Owner | Gate |
|---|---|---|
| Requirements analysis | sdlc-analyst |
Requirement |
| Technical specification | sdlc-pm |
Requirement |
| Architecture analysis | sdlc-architect |
Architecture |
| Implementation planning | sdlc-sm |
Architecture |
| Code generation | sdlc-dev |
Implementation |
| Test generation | sdlc-qa |
Testing |
| Code review | sdlc-reviewer |
Code Review |
| Security analysis | sdlc-security |
Security |
| Documentation | every role (artifact per stage) | DoD |
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.
- 1mo ago First seen · 515 lines · 91 tokens per session scan A 757904a88200
sdlc-regulatory is a skill published in the GitHub repository daochild/agents-config (16 stars, last pushed 2mo ago), licensed MIT. It adds 91 tokens to every session and 6,851 once invoked, about $0.0004 per session on Opus 5.5. 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 2026-09-07.
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