Agent Skills for Context Engineering is a collection of reusable instructions that teach AI agents how to manage their context, coordinate multi-agent systems, and evaluate behavior. Developers use it when building or debugging production agent systems, and the catalogue entries are skills, agents, instructions, and a plugin from this collection.
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 muratcankoylan/Agent-Skills-for-Context-Engineering --skill filesystem-contextgit clone --depth 1 https://github.com/muratcankoylan/Agent-Skills-for-Context-EngineeringWrote 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/muratcankoylan/agent-skills-for-context-engineering/filesystem-context)<a href="https://agentmods.dev/skills/muratcankoylan/agent-skills-for-context-engineering/filesystem-context"><img src="https://agentmods.dev/badge/skills/muratcankoylan/agent-skills-for-context-engineering/filesystem-context/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/muratcankoylan/agent-skills-for-context-engineering/filesystem-context"><img src="https://agentmods.dev/badge/skills/muratcankoylan/agent-skills-for-context-engineering/filesystem-context.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 4 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Memory Poisoning · line 256 Skill manipulates agent memory, state, or stored context. Memory corruption can alter personality, override safety rules, or cause unpredictable behavior.Fix: Protect agent memory and state from modification by untrusted content. Use read-only memory for critical instructions and validate all state changes.
- medium Agent Snooping · line 124 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
- medium Agent Snooping · line 225 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
- medium Excessive Agency · line 259 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00049 | $0.03198 |
| Opus 5.5 | $0.00020 | $0.01279 |
| Sonnet 5.5 | $0.00010 | $0.00640 |
| Haiku 4.5 | $0.00005 | $0.00320 |
Grade A, and why
filesystem-context 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 23d 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.
Copies of this mod
3 near-identical copies found in the catalogue:
- filesystem-context — 100% identical, 0 lines differ
- filesystem-context — 97% identical, 0 lines differ
- filesystem-context — 95% identical, 15 lines differ
How it starts
The opening of the file, as written. The whole thing — 299 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Filesystem-Based Context Engineering
Use the filesystem as the primary overflow layer for agent context because context windows are limited while tasks often require more information than fits in a single window. Files let agents store, retrieve, and update an effectively unlimited amount of context through a single interface.
Prefer dynamic context discovery -- pulling relevant context on demand -- over static inclusion, because static context consumes tokens regardless of relevance and crowds out space for task-specific information.
When to Activate
Activate this skill when:
- Tool outputs are bloating the context window
- Agents need to persist state across long trajectories
- Sub-agents must share information without direct message passing
- Tasks require more context than fits in the window
- Building agents that learn and update their own instructions
- Implementing scratch pads for intermediate results
- Terminal outputs or logs need to be accessible to agents
Do not activate this skill for adjacent work owned by other skills:
- Semantic cross-session memory, entity tracking, or temporal knowledge graphs:
memory-systems. - Conversation summarization, compaction, or durable handoff wording:
context-compression. - Token-efficiency tactics that do not require file-backed storage:
context-optimization. - Multi-agent topology or handoff protocol design:
multi-agent-patterns. - Model edits that evict content from the live window itself:
self-managed-context.
Core Concepts
Diagnose context failures against these four modes, because each requires a different filesystem remedy:
- Missing context -- needed information is absent from the total available context. Fix by persisting tool outputs and intermediate results to files so nothing is lost.
- Under-retrieved context -- retrieved content fails to encapsulate what the agent needs. Fix by structuring files for targeted retrieval (grep-friendly formats, clear section headers).
- Over-retrieved context -- retrieved content far exceeds what is needed, wasting tokens and degrading attention. Fix by offloading bulk content to files and returning compact references.
- Buried context -- niche information is hidden across many files. Fix by combining glob and grep for structural search alongside semantic search for conceptual queries.
What ships with it
2 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.
- 23d ago Changed · +2 lines 91cd7f071e38
- 1mo ago First seen · 297 lines · 49 tokens per session scan A bbde98d3481f
filesystem-context is a skill published in the GitHub repository muratcankoylan/Agent-Skills-for-Context-Engineering (18,208 stars, last pushed 13d ago), licensed MIT. It adds 49 tokens to every session and 3,198 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 2026-08-30.
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