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/alexmond/jhelm/learnnpx skills add alexmond/jhelm --skill learngit clone --depth 1 https://github.com/alexmond/jhelmWhat 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 | $0.00100 | $0.01130 |
| Opus 5 | $0.00050 | $0.00565 |
| Sonnet 5 | $0.00020 | $0.00226 |
| Haiku 4.5 | $0.00010 | $0.00113 |
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 2d 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 — 104 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Save a learning to project memory
$ARGUMENTS
Determine the learning
If triggered automatically (multi-cycle resolution), synthesise the learning from the conversation:
- What was the root cause of the extra cycle(s)?
- What assumption or gap in knowledge caused the first attempt to fail?
- What is the correct approach / API / behaviour?
- What should be checked first next time to avoid the same detour?
If triggered by the user, record exactly what they stated in $ARGUMENTS.
Steps
-
Read the current memory file:
/Users/alex.mondshain/.claude/projects/-Users-alex-mondshain-claude-jhelm/memory/MEMORY.md -
Check whether a relevant topic file already exists in that same directory (e.g.
dependencies.md,testing.md,debugging.md). If so, read it too. -
Decide where to write:
- Short, self-contained insight that fits an existing
MEMORY.mdsection → add it there (keep file ≤ 200 lines). - Detailed or topic-specific learning → append to or create a dedicated topic file, then add/update a one-line reference in
MEMORY.md.
- Short, self-contained insight that fits an existing
-
Write in concise, actionable form:
- Bullet points, not prose.
- Lead with what to do / what to check, follow with why.
- If it supersedes an existing note, update or remove the old one.
-
Scan for reusable scripts (see section below).
-
Confirm to the user what was saved and where (one line is enough).
What to save
- Root causes of multi-cycle failures and the correct fix
- Non-obvious library behaviours or API quirks discovered during the task
- Version constraints and compatibility issues (e.g. BCrypt prefix
$2y$vs$2a$) - Architectural decisions and their rationale
- Workflow or tool preferences the user has stated
- Patterns confirmed to work that aren't obvious from the code
- Platform-specific shell/CLI argument failures — save to the
## macOS / Platform-Specific Shell Quirkssection inMEMORY.mdwith the failing invocation and the correct macOS alternative
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.
- 2d ago First seen · 104 lines · 100 tokens per session scan A 833e43d27200
learn is a skill published in the GitHub repository alexmond/jhelm (4 stars, last pushed 8d ago), licensed Apache-2.0. It adds 100 tokens to every session and 1,130 once invoked, about $0.0005 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.
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