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 docxology/template --skill filesystem-contextgit clone --depth 1 https://github.com/docxology/templateWrote 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/docxology/template/filesystem-context)<a href="https://agentmods.dev/skills/docxology/template/filesystem-context"><img src="https://agentmods.dev/badge/skills/docxology/template/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/docxology/template/filesystem-context"><img src="https://agentmods.dev/badge/skills/docxology/template/filesystem-context.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 | $0.00049 | $0.03199 |
| Opus 5.5 | $0.00020 | $0.01280 |
| 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 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.
This is a copy
95% identical to filesystem-context — 15 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
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.
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.
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.
- 1mo ago First seen · 304 lines · 49 tokens per session scan A 16495df022fa
filesystem-context is a skill published in the GitHub repository docxology/template (20 stars, last pushed today), licensed Apache-2.0. It adds 49 tokens to every session and 3,199 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 95% identical to filesystem-context, differing in 15 lines, and is treated as a copy.
Other skills, from other repositories
learn
Review the current conversation and update project knowledge artifacts - common-gotchas.md (bug patterns), conventions at the highest rung that holds them (code, check, reviewer standard, skill, then AGENTS.md), navigation pointers, agent memory (cross-session). Use when asked to '/learn', 'what did we learn'…
save-learning
Use when you learned something durable — a decision, a hard-won lesson, a useful reference. Captures it into the memory wiki so it compounds.
kiro-steering-custom
Create custom steering documents for specialized project contexts.
memory
Cross-project research memory. Deep-dive past projects' notes, record corrections, and save cross-project insights across all Luxas research projects.
daily-journal
A passive daily work journal that Claude keeps FOR you so you never have to write it yourself. Append short entries after meaningful work (what was done, what you focused on, artifacts touched) to 01-daily/journal/YYYY-MM-DD.md. Run a guided reflection at night or in the morning. Use when you run /daily-journal, say…
honey-memory
Per-project persistent memory files indexed in MEMORY.md.