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/phuoctrung-ppt/ai-sdlc-workflowWrote 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/rules/phuoctrung-ppt/ai-sdlc-workflow/007-memory-learning)<a href="https://agentmods.dev/rules/phuoctrung-ppt/ai-sdlc-workflow/007-memory-learning"><img src="https://agentmods.dev/badge/rules/phuoctrung-ppt/ai-sdlc-workflow/007-memory-learning/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/rules/phuoctrung-ppt/ai-sdlc-workflow/007-memory-learning"><img src="https://agentmods.dev/badge/rules/phuoctrung-ppt/ai-sdlc-workflow/007-memory-learning.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 | $0.00346 | $0.00346 |
| Opus 5.5 | $0.00138 | $0.00138 |
| Sonnet 5.5 | $0.00069 | $0.00069 |
| Haiku 4.5 | $0.00035 | $0.00035 |
Grade A, and why
007-memory-learning 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.
What it actually says
Memory & Learning Rules
Memory
| Location | Role |
|---|---|
docs/memory/* |
Primary durable SoT |
.memory/* |
Generated AGENTS cache only (memory-loader.py --sync) |
Read docs/memory/* first. Never hand-edit .memory/* as durable memory.
workflow-state.json — split concerns
| Keys | Owner | Agent access |
|---|---|---|
modulesSinceLastProposal, lastModuleCompleted, lastSkillProposal* |
Learning | Only via python3 .cursor/scripts/learning-counter.py get|inc|reset |
editedFiles, stopBlockCount, overrides, events |
workflow-guard.py |
Never in agent context |
Forbidden: cat / open / hand-edit .cursor/state/workflow-state.json.
record-edit ignores .cursor/state/** and .aisdlc/** so counter updates do not pollute editedFiles.
Learning
- Phase 6
/dev-module: distill memory +learning-counter.py inc --module <name>+ lightweight@learning-agent - Full pass:
/skill-updateor whengetreturnsfullPassRecommended: true - Proposal written →
learning-counter.py reset --proposal <path>
Module deps
Before execute: read docs/module-deps.md; stop if upstream not done.
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 · 38 lines · 346 tokens per session scan A 7fb0875ab331
007-memory-learning is a cursor rule published in the GitHub repository phuoctrung-ppt/ai-sdlc-workflow (2 stars, last pushed 1mo ago), licensed MIT. It adds 346 tokens to every session, about $0.0014 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 2026-08-31.
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