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 ToruAI/toru-claude-agents --skill megg-learngit clone --depth 1 https://github.com/ToruAI/toru-claude-agentsWrote 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/toruai/toru-claude-agents/megg-learn)<a href="https://agentmods.dev/skills/toruai/toru-claude-agents/megg-learn"><img src="https://agentmods.dev/badge/skills/toruai/toru-claude-agents/megg-learn/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/toruai/toru-claude-agents/megg-learn"><img src="https://agentmods.dev/badge/skills/toruai/toru-claude-agents/megg-learn.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 | $0.00022 | $0.00275 |
| Opus 5.5 | $0.00009 | $0.00110 |
| Sonnet 5.5 | $0.00004 | $0.00055 |
| Haiku 4.5 | $0.00002 | $0.00028 |
Grade A, and why
megg-learn 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
Capture Learning
User wants to save something to megg knowledge.
Process
- If argument provided, use it as the learning content
- If no argument, ask: "What should we capture?"
- Determine the appropriate entry type:
- decision: Architectural or design choice made
- pattern: How we do things here (reusable approach)
- gotcha: Trap to avoid, something that caught us
- context: Background info, not actionable
- Extract or ask for relevant topics (tags for categorization)
- Use the
mcp__megg__learntool to save
Example
User: /megg-learn always use absolute paths in hooks
→ Capture as pattern with topics like hooks, paths
Important
- Keep entries concise and actionable
- Topics should be 1-3 words each
- Title should be short (5-10 words max)
- Content can include examples or context
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 · 39 lines · 22 tokens per session scan A 0e178d13d975
megg-learn is a skill published in the GitHub repository ToruAI/toru-claude-agents (15 stars, last pushed 3mo ago), licensed MIT. It adds 22 tokens to every session and 275 once invoked, about $0.0001 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 2026-08-30.
Other skills, from other repositories
principle-guard-the-context-window
Apply when context is filling up: large outputs, long files, repeated reads, fan-out planning. Route bulk to subagents; keep summaries in the main thread, not raw payloads.
session-handoff
Create consistent session handoff documents for context transfer between sessions. Use when closing a session, reaching 75% token consumption, or when user requests handoff. Ensures proper YAML headers for metrics collection, LF line endings, and complete documentation of progress, decisions, and next steps.
domain-extract
Extract domain knowledge from existing project sources and generate domain rules. Also handles vault sync and domain listing.
capture-practice
Captures an insight or practice discovered during work and registers it in the dotforge practices/inbox.
update
Integrate external info (research, status changes, reference material, deliverables) into the ops repo's knowledge base. Drop files in .update/ or pass content inline.
harden-memories
Audit the bd remember store and recommend which entries to prune, so each one earns its per-session bd prime injection cost. Read-only: it proposes a triage table plus the exact bd forget / migration commands; you run them. Use when bd remember entries have accumulated, feel stale, redundant, or conflicting, or when…