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 skills add hardiktiwari/PM-operating-OS --skill continual-learninggit clone --depth 1 https://github.com/hardiktiwari/PM-operating-OSWrote 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/hardiktiwari/pm-operating-os/continual-learning)<a href="https://agentmods.dev/skills/hardiktiwari/pm-operating-os/continual-learning"><img src="https://agentmods.dev/badge/skills/hardiktiwari/pm-operating-os/continual-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/skills/hardiktiwari/pm-operating-os/continual-learning"><img src="https://agentmods.dev/badge/skills/hardiktiwari/pm-operating-os/continual-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 | $0.00051 | $0.01213 |
| Opus 5.5 | $0.00020 | $0.00485 |
| Sonnet 5.5 | $0.00010 | $0.00243 |
| Haiku 4.5 | $0.00005 | $0.00121 |
Grade A, and why
continual-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.
How it starts
The opening of the file, as written. The whole thing — 169 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Continual Learning
The ingestion pipeline for the PM-OS context graph. Reads chat transcripts, classifies what it finds, writes structured entries to memory/, and updates AGENTS.md with curated preferences and facts.
When to Use
- "Run continual learning" / "mine my chats" / "update memory"
- On a schedule (e.g. end of week) to keep the context graph current
- After a batch of important conversations (strategy sessions, PRD reviews, planning)
Inputs
- Transcript root:
~/.cursor/projects/<workspace-slug>/agent-transcripts/ - Existing memory:
AGENTS.md+memory/subdirectories - Incremental index:
.cursor/hooks/state/continual-learning-index.json
Workflow
1. Load state
- Read existing
AGENTS.md. - Load incremental index (if present).
- Discover transcript files. Process only:
- new files not in the index, or
- files whose mtime is newer than the indexed mtime.
2. Extract and classify
For each new/changed transcript, extract high-signal items and classify each into one of these categories:
| Category | What to look for | Write to |
|---|---|---|
| Decision | User made or confirmed a product decision (PRD approval, scope change, launch/kill, prioritization shift, stakeholder alignment) | memory/decisions/ |
| Exec update | User drafted or discussed an executive status update, SLT summary, or program update | memory/exec-updates/ |
| Strategy shift | User discussed or changed strategic direction, pillars, positioning, or goals | memory/strategy-reviews/ |
| Feedback insight | User analyzed customer feedback, VOC themes, or support trends | memory/feedback/ |
| User preference | Recurring correction or stated broad rule about how the AI should behave | AGENTS.md → Learned User Preferences |
| Workspace fact | Durable fact about the workspace, tools, file paths, or workflows | AGENTS.md → Learned Workspace Facts |
Skip: one-off task instructions, transient details (branch names, commit hashes, temp errors), secrets/tokens/credentials, and anything not actionable in future sessions.
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 · 169 lines · 51 tokens per session scan A 450d73132220
continual-learning is a skill published in the GitHub repository hardiktiwari/PM-operating-OS (5 stars, last pushed 5mo ago), licensed MIT. It adds 51 tokens to every session and 1,213 once invoked, about $0.0002 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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