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 agentmods add skills/our-ark/enoch/learnnpx skills add our-ark/enoch --skill learngit clone --depth 1 https://github.com/our-ark/enochWrote 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/our-ark/enoch/learn)<a href="https://agentmods.dev/skills/our-ark/enoch/learn"><img src="https://agentmods.dev/badge/skills/our-ark/enoch/learn.svg" alt="Measured on agentmods" 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 | $0.00000 | $0.00374 |
| Opus 5 | $0.00000 | $0.00187 |
| Sonnet 5 | $0.00000 | $0.00075 |
| Haiku 4.5 | $0.00000 | $0.00037 |
Grade A, and why
learn 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 3d 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.
What it actually says
Learn
Purpose
Assess whether a visible skill published by a non-parent Our-Ark agent offers a bounded capability that Enoch should adapt. An applicable assessment creates an evolution candidate; it never edits Enoch directly.
Use When
- The human selects a named skill with
/learn <skill> from <agent>. - The source agent publishes the skill through the configured forge.
- Enoch should evaluate a portable capability rather than inherit a direct-parent change.
Procedure
- Resolve the source agent's current
mainrevision to an immutable commit. - Read
identity.yaml,skill.yaml, andSKILL.mdfrom that same commit. - Reject hidden, missing, inconsistent, oversized, unsafe-path, self, and direct-parent packages deterministically.
- Build a temporary in-memory snapshot containing the source commit, package contents, version, link, and content hash.
- Give that snapshot, Enoch's mission and declared skills, and a bounded list of current candidates to one fresh read-only Codex session.
- Require one structured result:
applicablewith complete candidate fields, ornot_applicablewith a reason and no candidate. - Validate the returned scope and schema in deterministic code.
- Persist an applicable result as a
learningevolution candidate with the immutable source provenance attached. - Notify the human and leave execution to
/evolve approve <candidate-id>.
Boundary
Learning is assessment, not synchronization, inheritance, or execution. Codex authors the candidate contents, while Enoch validates and persists them. A not-applicable result creates no candidate or separate assessment record.
Validation
Run:
python3 -m unittest discover -s tests -t .
What ships with it
1 file 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.
- 3d ago First seen · 46 lines · 0 tokens per session scan A 32ff3b9eb68b
learn is a skill published in the GitHub repository our-ark/enoch (11 stars, last pushed 3d ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 374 tokens. 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.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…