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/robinslange/learning-loop/dreamnpx skills add robinslange/learning-loop --skill dreamgit clone --depth 1 https://github.com/robinslange/learning-loopWhat 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 | $0.00066 | $0.01976 |
| Opus 5 | $0.00033 | $0.00988 |
| Sonnet 5 | $0.00013 | $0.00395 |
| Haiku 4.5 | $0.00007 | $0.00198 |
Grade A, and why
dream scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 144 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Dream: Auto-Memory Consolidation
Seven operators, each defined in operators/. This file orchestrates the four-phase cycle. Read operator files only when executing Phase 3.
When to Use
- SessionStart hook nudges via
hooks/lib/dream-gate.jswhen 24+ hours have passed since the last dream AND 5+ memory files have been modified since then. Nudge only — never auto-runs. - Stop hook nudges after heavy sessions (3+ new memory files in current session).
- Manual:
/dreamruns immediately, ignores gates.
Provenance
Emit events silently via Bash for each operator action.
node "${CLAUDE_PLUGIN_ROOT}/scripts/provenance-emit.js" '{"agent":"dream","skill":"dream","action":"ACTION","target":"FILENAME"}'
Where ACTION is one of: merge, resolve, abstract, compress, prune, link, normalize.
At start: {"action":"session-start"}. At end: {"action":"session-end","merged":N,"resolved":N,"abstracted":N,"compressed":N,"pruned":N,"linked":N,"normalized":N} + run node ${CLAUDE_PLUGIN_ROOT}/scripts/provenance-consolidate.mjs.
Phase 1: Orient
-
Detect the project memory directory:
- Use
$CLAUDE_PROJECT_DIRif available, else use the auto-memory directory for the current project - Verify the directory exists and contains MEMORY.md
- Use
-
Read all
.mdfiles (excluding MEMORY.md, _dream_log.md, _archived/). -
Parse YAML frontmatter:
name,description,type,confidence. -
Build inventory: total count, count by type, sorted by modification date, line count per file.
-
Read MEMORY.md. Check links resolve to actual files. Flag orphaned pointers.
-
Report:
Dreaming: [project name] Memory files: N (N feedback, N project, N user, N reference) Index entries: N (N orphaned)
Phase 2: Gather Signal
- Group by type, sort newest-last within each group. Order: feedback, user, project, reference. Within each group, oldest first: transformer attention favors recent tokens, so placing the memories you want the consolidator to weight most heavily last in each group keeps them in the recency-favored position.
What ships with it
7 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.
- 2d ago First seen · 144 lines · 66 tokens per session scan A f1d20ab784e8
dream is a skill published in the GitHub repository robinslange/learning-loop (11 stars, last pushed 11d ago), licensed Apache-2.0. It adds 66 tokens to every session and 1,976 once invoked, about $0.0003 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 2026-08-30.
Other skills, from other repositories
systematic-debugging
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brainstorming
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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…