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 mikeparcewski/wicked-garden --skill context-engineeringgit clone --depth 1 https://github.com/mikeparcewski/wicked-gardenWrote 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/mikeparcewski/wicked-garden/context-engineering)<a href="https://agentmods.dev/skills/mikeparcewski/wicked-garden/context-engineering"><img src="https://agentmods.dev/badge/skills/mikeparcewski/wicked-garden/context-engineering.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00068 | $0.01134 |
| Opus 5 | $0.00034 | $0.00567 |
| Sonnet 5 | $0.00014 | $0.00227 |
| Haiku 4.5 | $0.00007 | $0.00113 |
Grade A, and why
wicked-garden-agentic-context-engineering 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 7d 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 — 143 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Context Engineering
Techniques for managing context windows, optimizing token usage, and designing efficient memory systems for agentic applications.
Context Window Fundamentals
Context Window: Maximum tokens an LLM can process in a single request (input + output).
Limits vary by provider and model. Check the active model's documentation for the exact value.
Token Efficiency Matters:
- Cost: Charged per token (input + output)
- Latency: More tokens = slower response
- Quality: Irrelevant context can confuse model
State Management Patterns
| Pattern | Use when | Pros | Cons |
|---|---|---|---|
| Shared | Agents need synchronized view | Consistency, simple coordination | Contention, single point of failure |
| Isolated | Agents operate independently | No contention, parallel execution | Inconsistency possible, harder to coordinate |
| Checkpointed | Long-running processes, need recovery | Fault tolerance, replayability | Storage overhead, consistency complexity |
Token Optimization Techniques
1. Aggressive Summarization
Compress old context into summaries to reduce token usage.
2. Selective Context Loading
Only load relevant context based on the current task.
3. Structured Compression
Use JSON/structured formats instead of prose to reduce tokens.
Example:
- Before: "The user's name is John Smith..." (verbose)
- After:
{"name": "John Smith", ...}(compact)
4. Lazy Loading
Load details only when explicitly needed.
5. Reference Instead of Embedding
Reference external documents instead of embedding full text.
See refs/selective-loading.md and refs/caching-and-optimization.md for code examples and detailed strategies.
Memory Patterns
| Memory | Scope | Size | Retention |
|---|---|---|---|
| Short-term (working) | Current session/task | 1K-10K tokens | Minutes to hours |
| Long-term | Cross-session, permanent | Unbounded (vector DB) | Days to forever |
| Episodic | Historical events | Summaries stored | Varies by importance |
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 7d ago First seen · 143 lines · 68 tokens per session scan A 073eb51a8198
wicked-garden-agentic-context-engineering is a skill published in the GitHub repository mikeparcewski/wicked-garden (9 stars, last pushed 2d ago), licensed MIT. It adds 68 tokens to every session and 1,134 once invoked, about $0.0003 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
media-ingest
Ingest video, audio, PDF, book, screenshot, and GitHub repo content into the brain. Multi-format handling with entity extraction and backlink propagation. Covers video-ingest, youtube-ingest, and book-ingest subtypes.
mem0-oss-to-platform
Plan and then execute a migration of a project from the mem0 open-source / self-hosted SDK (the local Memory class) to the mem0 Platform / hosted / managed SDK (the MemoryClient class). Use this whenever a developer wants to move, switch, or migrate their mem0 usage off OSS/self-hosted to the hosted API — e.g.…
Cortex
Operate Cortex, the LifeOS memory system — the typed Knowledge Archive (People, Companies, Ideas, Research with typed related: links) plus recall of prior work sessions, ISAs, and conversations. Search, add, harvest, develop, ingest, distill, graph-navigate, recall. USE WHEN cortex, knowledge, knowledge base, search…
memory
Use when the user asks to remember, recall, forget, update, search, or inspect durable OpenSquilla memory, including profile facts in USER.md and long-term notes in MEMORY.md or memory//.md.
ha-data-stores
Map of Hope Agent's local data stores and safe read-only query workflow. Use when the user asks where Hope Agent stores data, wants to inspect sessions/messages/memory/logs/background jobs/knowledge indexes/settings, asks the model to query local app data, or debugging requires checking persisted state. Trigger…
establishing-project-context
Use when the user asks to establish shared project language, or project work exposes a conflicting, renamed, or deprecated domain term that needs active semantic modeling. Routine small tasks stay on the fast path.