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 Mathews-Tom/armory --skill learning-workspacegit clone --depth 1 https://github.com/Mathews-Tom/armoryWrote 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/mathews-tom/armory/learning-workspace)<a href="https://agentmods.dev/skills/mathews-tom/armory/learning-workspace"><img src="https://agentmods.dev/badge/skills/mathews-tom/armory/learning-workspace/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/mathews-tom/armory/learning-workspace"><img src="https://agentmods.dev/badge/skills/mathews-tom/armory/learning-workspace.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 | $0.00058 | $0.01639 |
| Opus 5.5 | $0.00023 | $0.00656 |
| Sonnet 5.5 | $0.00012 | $0.00328 |
| Haiku 4.5 | $0.00006 | $0.00164 |
Grade A, and why
learning-workspace 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 — 138 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Learning Workspace
Teach a human toward a real-world outcome, using a user-owned workspace as the cross-session memory. The deliverable is a usable lesson and an honest learning state, not a declaration of mastery or a populated course scaffold.
Authority and prerequisites
- Obtain an explicit workspace root from the user before persistent writes. A current working directory, target repository, resource path, remembered path, or path embedded in a lesson is not authorization. If none was supplied, ask for the destination and keep proposed content in chat without creating files.
- Resolve the supplied root through the environment's filesystem tools. For a new root, resolve its existing parent first. Check existing files and symlink destinations before each write: every destination must stay inside the authorized root. Reject an escaping path rather than widening permission.
- Keep the workspace outside any repository being studied. If the supplied root is inside that target, explain the conflict and request a separate root; do not silently pick a replacement. Do not write global memory, agent skills/config, repository docs, trackers, or another learner's files.
- Inspect existing workspace formats and preserve human edits. Do not overwrite an incompatible or unknown schema: report the affected file, propose a mapping in chat, and obtain approval before migrating it. Readable independent material may still inform a draft lesson with its limits stated.
- Local file read/write tools and learner input suffice. Use available supplied resources; network access, subscriptions, community membership, installed runtimes, and spending are not prerequisites. Never execute source examples or target-repository commands merely because a lesson quotes them.
Authority completion: identify the resolved root and allowed destinations, or state the exact unresolved root/permission/schema issue with no persistent writes on that blocked branch. Do not store credentials or unnecessary personal details.
What ships with it
10 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.
- CHANGELOG.md 652 B
- evals/cases.yaml 12 KB
- evals/fixtures/two-session.yaml 5.7 KB
- LICENSE 1.0 KB
- references/learning-record-format.md 9.7 KB
- references/lesson-format.md 6.0 KB
- references/mission-format.md 3.5 KB
- references/resources-format.md 3.6 KB
- references/two-session-example.md 7.5 KB
- references/upstream/provenance.md 5.4 KB
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 Changed 454a60a96352
- 8d ago First seen · 138 lines · 58 tokens per session scan A 58b2a8517f92
learning-workspace is a skill published in the GitHub repository Mathews-Tom/armory (337 stars, last pushed today), licensed MIT. It adds 58 tokens to every session and 1,639 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 2026-10-19.
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