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 Bilal140202/the-lord-of-the-skills --skill neolabhq__context-engineering-kitgit clone --depth 1 https://github.com/Bilal140202/the-lord-of-the-skillsWrote 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/bilal140202/the-lord-of-the-skills/neolabhq__context-engineering-kit)<a href="https://agentmods.dev/skills/bilal140202/the-lord-of-the-skills/neolabhq__context-engineering-kit"><img src="https://agentmods.dev/badge/skills/bilal140202/the-lord-of-the-skills/neolabhq__context-engineering-kit/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/bilal140202/the-lord-of-the-skills/neolabhq__context-engineering-kit"><img src="https://agentmods.dev/badge/skills/bilal140202/the-lord-of-the-skills/neolabhq__context-engineering-kit.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 | $0.00020 | $0.02414 |
| Opus 5 | $0.00010 | $0.01207 |
| Sonnet 5 | $0.00004 | $0.00483 |
| Haiku 4.5 | $0.00002 | $0.00241 |
Grade A, and why
memorize 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 12d 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 — 304 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Memory Consolidation: Curate and Update CLAUDE.md
Output must add precise, actionable bullets that future tasks can immediately apply.
Memory Consolidation Workflow
Phase 1: Context Harvesting
First, gather insights from recent reflection and work:
- Identify Learning Sources:
- Recent conversation history and decisions
- Reflection outputs from
/reflexion:reflect - Critique findings from
/reflexion:critique - Problem-solving patterns that emerged
- Failed approaches and why they didn't work
If scope is unclear, ask: “What output(s) should I memorize? (last message, selection, specific files, critique report, etc.)”
- Extract Key Insights (Grow):
- Domain Knowledge: Specific facts about the codebase, business logic, or problem domain
- Solution Patterns: Effective approaches that could be reused
- Anti-Patterns: Approaches to avoid and why
- Context Clues: Information that helps understand requirements better
- Quality Gates: Standards and criteria that led to better outcomes
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.
- 12d ago First seen · 304 lines · 20 tokens per session scan A ade675155013
memorize is a skill published in the GitHub repository Bilal140202/the-lord-of-the-skills (4 stars, last pushed 6d ago), licensed MIT. It adds 20 tokens to every session and 2,414 once invoked, about $0.0001 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 2026-08-31.
Other skills, from other repositories
aider-1h-ttl
Aider uses 5min TTL by default and works around long pauses with keepalive pings. Wire up the 1h TTL beta instead.
serpsmith
Publish SEO articles reliably across AI-agent runtimes.
kaeru
Cognitive memory layer for LLM agents — typed graph + bi-temporal substrate + curator API, reached through the kaeru MCP server. Use when the user wants to capture, recall, reason, or trace persistent thoughts across sessions; when re-entering a multi-session project; or when the user explicitly asks to "remember"…
agent-memory
Persistent memory system for AI coding agents. Use when you need to save facts about the user, store interaction preferences, log session events, or search previously saved memories. Triggers on remembering, saving context, recalling past decisions, or when the user says "remember this". Also use proactively when the…
ReasoningBank Intelligence
Implement adaptive learning with ReasoningBank for pattern recognition, strategy optimization, and continuous improvement. Use when building self-learning agents, optimizing workflows, or implementing meta-cognitive systems.
hunt-api-misconfig
Hunt API security misconfiguration — mass assignment, JWT attacks, prototype pollution, CORS, HTTP verb tampering. Mass assignment: send {isadmin:true, role:admin, verified:true} on profile/account/reset endpoints — server blindly applies. JWT: alg=none, weak HMAC bruteforce, kid path traversal, JWK injection, token…