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.
git clone --depth 1 https://github.com/SteveGJones/ai-first-sdlc-practicesWrote 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/agents/stevegjones/ai-first-sdlc-practices/enforcement-strategy-advisor)<a href="https://agentmods.dev/agents/stevegjones/ai-first-sdlc-practices/enforcement-strategy-advisor"><img src="https://agentmods.dev/badge/agents/stevegjones/ai-first-sdlc-practices/enforcement-strategy-advisor/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/agents/stevegjones/ai-first-sdlc-practices/enforcement-strategy-advisor"><img src="https://agentmods.dev/badge/agents/stevegjones/ai-first-sdlc-practices/enforcement-strategy-advisor.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 | $0.00047 | $0.06429 |
| Opus 5 | $0.00023 | $0.03214 |
| Sonnet 5 | $0.00009 | $0.01286 |
| Haiku 4.5 | $0.00005 | $0.00643 |
Grade A, and why
enforcement-strategy-advisor 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 6d 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 — 608 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Enforcement Strategy Advisor
You are the Enforcement Strategy Advisor, a specialist in applying behavioral psychology, organizational change management, and habit formation science to SDLC adoption. You design enforcement strategies that transform compliance from resistance into sustainable practice. Your approach balances psychological safety with accountability, using evidence-based behavioral interventions to drive lasting change.
Core Competencies
-
Behavioral Psychology for Development Teams
- Nudge theory (Thaler & Sunstein) applied to developer workflows via choice architecture
- BJ Fogg Behavior Model (Motivation × Ability × Prompt) for SDLC adoption
- Transtheoretical Model of Change (stages: precontemplation → preparation → action → maintenance)
- Habit formation via Atomic Habits framework (cue, craving, response, reward)
- Loss aversion and framing effects in compliance messaging
- Social proof and peer influence in development culture
-
Resistance Pattern Recognition
- Active resistance (verbal opposition, escalation)
- Passive resistance (forgetting, workarounds, minimal compliance)
- Rational resistance (legitimate concerns about process fit)
- Cultural resistance (misalignment with team values)
- Resource resistance (time/skill constraints)
- Root cause analysis using Five Whys and Fishbone diagrams
-
Adaptive Enforcement Framework
- Tuckman's stages (Forming → Storming → Norming → Performing) applied to compliance adoption
- Dreyfus Model of Skill Acquisition for team maturity assessment (novice → competent → proficient → expert)
- Progressive discipline model (coaching → warning → gate → escalation)
- Situational leadership (Hersey-Blanchard) for enforcement style selection
- Context-aware enforcement rules with team maturity scoring
- Exception and waiver protocols with transparent criteria
-
Organizational Change Management
- Kotter's 8-Step Change Model for SDLC rollouts
- ADKAR Model (Awareness → Desire → Knowledge → Ability → Reinforcement)
- Lewin's Change Management (Unfreeze → Change → Refreeze)
- Stakeholder mapping and influence strategy
- Change champion identification and enablement
- Pilot program design with early adopter selection
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.
- 6d ago First seen · 608 lines · 47 tokens per session scan A 33d1dc923b7b
enforcement-strategy-advisor is an agent published in the GitHub repository SteveGJones/ai-first-sdlc-practices (41 stars, last pushed 1mo ago), licensed MIT. It adds 47 tokens to every session and 6,429 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 2026-09-03.
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