MemPalace Memory Search
MemPalace/mempalace
Mines project files and conversation exports into a local, searchable memory palace and recalls past work by semantic search through the mempalace CLI.
Curate the workspace memory wiki as its appointed orchestrator.
$ npx skills add Prismer-AI/PrismerCloud --skill memory-dream -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install Prismer-AI/PrismerCloud memory-dream --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/Prismer-AI/PrismerCloud.git skills-src && mkdir -p .claude/skills && cp -r skills-src/sdk/cloud/catalog/skills/memory-dream .claude/skills/memory-dream && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "memory-dream" agent skill from https://github.com/Prismer-AI/PrismerCloud/tree/main/sdk/cloud/catalog/skills/memory-dream into .claude/skills/memory-dream/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "memory-dream", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/Prismer-AI/PrismerCloud/tree/main/sdk/cloud/catalog/skills/memory-dreamType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add Prismer-AI/PrismerCloud --skill memory-dream -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install Prismer-AI/PrismerCloud memory-dream --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Prismer-AI/PrismerCloud.git skills-src && mkdir -p .agents/skills && cp -r skills-src/sdk/cloud/catalog/skills/memory-dream .agents/skills/memory-dream && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "memory-dream" agent skill from https://github.com/Prismer-AI/PrismerCloud/tree/main/sdk/cloud/catalog/skills/memory-dream into .agents/skills/memory-dream/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "memory-dream", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add Prismer-AI/PrismerCloud --skill memory-dream -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install Prismer-AI/PrismerCloud memory-dream --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Prismer-AI/PrismerCloud.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/sdk/cloud/catalog/skills/memory-dream .cursor/skills/memory-dream && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "memory-dream" agent skill from https://github.com/Prismer-AI/PrismerCloud/tree/main/sdk/cloud/catalog/skills/memory-dream into .cursor/skills/memory-dream/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "memory-dream", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/Prismer-AI/PrismerCloud.git --path sdk/cloud/catalog/skills/memory-dream--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add Prismer-AI/PrismerCloud --skill memory-dream -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install Prismer-AI/PrismerCloud memory-dream --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Prismer-AI/PrismerCloud.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/sdk/cloud/catalog/skills/memory-dream .gemini/skills/memory-dream && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "memory-dream" agent skill from https://github.com/Prismer-AI/PrismerCloud/tree/main/sdk/cloud/catalog/skills/memory-dream into .gemini/skills/memory-dream/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "memory-dream", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install Prismer-AI/PrismerCloud memory-dreamInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add Prismer-AI/PrismerCloud --skill memory-dream -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/Prismer-AI/PrismerCloud.git skills-src && mkdir -p .github/skills && cp -r skills-src/sdk/cloud/catalog/skills/memory-dream .github/skills/memory-dream && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "memory-dream" agent skill from https://github.com/Prismer-AI/PrismerCloud/tree/main/sdk/cloud/catalog/skills/memory-dream into .github/skills/memory-dream/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "memory-dream", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add Prismer-AI/PrismerCloud --skill memory-dream -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install Prismer-AI/PrismerCloud memory-dream --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Prismer-AI/PrismerCloud.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/sdk/cloud/catalog/skills/memory-dream .opencode/skills/memory-dream && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "memory-dream" agent skill from https://github.com/Prismer-AI/PrismerCloud/tree/main/sdk/cloud/catalog/skills/memory-dream into .opencode/skills/memory-dream/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "memory-dream", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
memory-dreamCurate the workspace memory wiki as its appointed orchestrator.
Memory Dream is an agent skill from Prismer-AI/PrismerCloud. Curate the workspace memory wiki as its appointed orchestrator. Trigger on a Cloud-scheduled Dream task or a user's direct curation request. Inspect candidates for orphan leaves, near-duplicates, stale pages, remote conflicts and oversized hubs; browse the graph, cluster leaves into INDEX/hub/leaf structure, maintain authored overview prose, split hubs, merge duplicates and verify/report changes. Use memorycurate and memorybrowse; preserve machine-generated hub TOCs and graph-derived INDEX Contents…
Its SKILL.md is about 4.8k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in Agent Workflows. The licence is MIT.
Read from SKILL.md and the folder at commit e5d9444. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
No scripts in the folder and no shell commands in SKILL.md.
From the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Memory Dream loads about 4.8k tokens when it runs. Until then it costs about 161 tokens; SKILL.md has 1,985 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
The full file from Prismer-AI/PrismerCloud at commit e5d9444, republished under its MIT licence (© Prismer-AI). 1,985 words, ~4,845 tokens.
.claude/skills/memory-dream/SKILL.md (or your agent's skills folder).Memory pages are PKF (
.pkf). When you fold or merge page bodies, the body syntax (sections, typed links, frontmatter) comes from thepkf-writingskill — load it when you author body content, and validate the result before persisting. This skill only governs the graph: what to merge, where to attach, and how to keep INDEX/hub ownership straight.
Candidate boundary. Dream sees authoritative Memory Pages, not “all PKF.” A message-inline PKF or Library
.pkfAsset remains outside this loop. Runtime automatically classifies durable claims from validated inline PKF; Asset handling still uses thememoryskill. Explicitly materialize authoritative inline bytes only when the user requests exact preservation. Only a resulting Memory Page participates in candidates, revisions, merges and hub TOCs.
You are (when appointed) the workspace memory orchestrator. Your job is the "Dream
phase": periodically reorganize the memory wiki so it stays coherent as it grows — merge
duplicate hubs, attach satellite pages under the right hub, supersede stale/contradicted
pages, keep the top-level INDEX lean, maintain every hub's #overview plus the INDEX's
authored semantic sections, and split hubs that have grown oversized.
There is one automatic trigger authority: the Cloud SchedulerService sweeps every
six hours, applies the 24-hour cadence gate plus authoritative wiki-health signals
(orphan ratio, oversized INDEX, frontier duplicate cluster, or page-growth burst),
deduplicates in-flight work, and dispatches one hidden memory-dream task to the bound
workspace orchestrator. The Cloud performs no Memory LLM reasoning. The appointed
orchestrator executes this skill in its own Runtime and is the sole automatic write
actor. A user can also ask that orchestrator to curate immediately; ordinary per-page
queries do not trigger Dream. The retired daemon FF_MEMORY_DREAM_ENABLED scheduler is
not an active trigger path.
Authority gate. The curation write verbs only work for the appointed workspace orchestrator (the workspace owner, or the agent set as
orchestratorAgentId). If you are not the orchestrator, every write verb returns403 orchestrator_only— that is expected, not a bug. Do not retry; write-time placement discipline (the memory skill's browse-first flow) is what every agent does, and it is enough.
Owner vs orchestrator vs deputy (product209/16 MA-2):
orchestratorAgentId) are the only Dream write actors. The owner speaking
through an agent does NOT grant it Dream rights — the agent itself must hold
the orchestrator binding. Dream never back-derives authority from "the owner
is talking to me".orchestratorAgentId.candidates is the READ half and works for any agent in scope; the write
verbs (promote_to_hub / supersede / rebuild_index and the section-level
section_merge / section_supersede / rewire) are the gated half.403 orchestrator_only and 202 approval deferred are final per-request
verdicts — do not retry, and do not route around them through another agent.memory_curate(op="candidates") # READ what needs work (any agent)
memory_curate(op="candidates", kind="orphans") # one surface; limit caps per kind
memory_browse(query="<topic>") # READ the structure: {index, hubs[+snippet], nearest}
memory_curate(op="promote_to_hub", pageId="<id>",
childPaths=["<path>", …]) # leaf → hub AND attach children (orchestrator only)
memory_curate(op="supersede", pageId="<id>",
reason="merged into <path>") # archive + mark stale (orchestrator only)
memory_curate(op="rebuild_index") # regenerate hub TOCs; INDEX Contents stays graph-derived (orchestrator only)
memory_curate(op="section_merge", pageId="<winnerPageId>",
targetSection="<winnerSlug>", sourcePageId="<loserPageId>",
sourceSection="<loserSlug>",
mergedContent="<merged body>") # fold ONE near-duplicate section (memory211 W5)
memory_curate(op="section_supersede", pageId="<pageId>",
section="<slug>") # retire ONE section in place (memory211 W5)
memory_curate(op="rewire", linkId="<linkId>",
toPageId="<id>") # re-point a broken/wrong link (memory211 W5)candidates is the READ half: {candidates:{orphans,duplicates,stale,conflicts,oversized}}
— unplaced leaves (no outgoing child-of/parent edge to a live hub; an INDEX
index-anchor only guarantees reachability and therefore still counts as flat), near-duplicate clusters
(duplicate_cluster:<peerIds>), stale pages, remote-conflict pages, and oversized
hub/INDEX advisories (kind="oversized" — size is recorded, never machine-enforced;
splitting is YOUR call). It does NOT cluster or decide; that is YOUR LLM's job.promote_to_hub takes childPaths[] — pass the member pages' paths (exactly as
candidates/browse returned them) and the cloud attaches them under the new hub as
child-of children in the same call. Do not promote a hollow hub and re-anchor
members one by one afterwards.rebuild_index is the ONLY hub #toc writer. It emits a complete strict PKF
<section> with stable data-sid, one navigation entry per graph child and the
child's frontmatter description. Structural child-of remains child→hub in the
graph; downward TOC links are navigation, never inverted placement edges. Hub
<h2 id="overview"> prose is agent territory and survives rebuild.#toc to rebuild or edit. Its authored semantic sections are the
orchestrator's editorial territory.section_merge rewrites the winner's section with mergedContent,
splices the loser section out of its page, and the CLOUD writes the
supersedes + derived-from provenance edges for you — a merge with no edge trail is
an unfinished merge. section_supersede retires one section (optionally pointing at the
surviving one). rewire fixes a single broken/wrong link without a page rewrite.run_dream cloud-LLM op — candidates (read) + the write ops are
the whole surface (memory203/13 §0.5: all memory LLM runs in your runtime, never the
cloud's).Enactment is the native memory_curate TOOL (the same tool surface every agent already
holds, alongside memory_search / memory_load / memory_browse / memory_write). Do
NOT look for a separate "memory-dream tool" or shell — memory_curate IS the enactment
surface. (A prismer memory curate … CLI exists for shell contexts; the TOOL is primary.)
If a curate response is degraded:true, no authoritative Cloud mutation occurred;
report the degraded state and never describe the requested convergence as completed.
Run the loop end-to-end in a handful of calls — memory_browse gives you the whole
structure in ONE call, so you never need long exploratory search/load spelunking:
STEP 1 — READ the candidates.
memory_curate(op="candidates")
# → { ok:true, candidates: {
# orphans: { items:[ {pageId, path, reason:"orphan"}, … ], total },
# duplicates: { items:[ {pageId, path, reason:"duplicate_cluster:<peerIds>"}, … ], total },
# stale: { items:[ {pageId, path, reason}, … ], total },
# conflicts: { items:[ {pageId, path, reason:"remote-conflict",
# metadata:{…, latestTwoVersionSummaries}}, … ], total },
# oversized: { items:[ {pageId, path, reason:"oversized:toc_entries,body_chars",
# metadata:{bodyChars, tocEntries, softThresholds, advisory:true}}, … ], total } } }STEP 1b — REVIEW the conflicts. memory_curate(op="candidates", kind="conflicts")
lists pages whose current head landed via a remote-conflict LWW merge (two devices
diverged); each item carries the latest two version summaries (changeSummary /
authoredBy / createdAt) so you can judge which side won. For each one, confirm the
head is correct — memory_load it and check the LWW winner didn't clobber the better
content; if it did, memory_write the corrected body. Conflict is a STATE, not a
brand: a curation touch or clean rewrite of the page clears it (an identical-content
rewrite is a no-op and does NOT), so a reviewed page drops off this list on the next
candidates read.
STEP 1c — REVIEW the oversized hubs. memory_curate(op="candidates", kind="oversized") lists hubs whose body chars or stored TOC entry count exceeds the
advisory soft thresholds, plus INDEX advisories based on its authored body size (its
Contents is graph-derived). These are split suggestions, never enforcement — a
lean hub (overview + TOC) keeps every multi-hop recall fast. For a hub with too many
children, split by sub-topic: pick the natural anchor leaf of each sub-cluster,
promote_to_hub it WITH its childPaths, and the moved children drop out of the
parent's TOC on the next rebuild. A hub that is oversized because its overview prose
grew into an essay: move the essay's durable content into a leaf under the hub and
shrink the overview back to a summary. Use your judgment — an advisory you deliberately
leave alone (and say so in the report) is a valid outcome.
STEP 2 — BROWSE the structure.
memory_browse(query="<dominant candidate topic>")
# → { index, hubs:[{path,title,pageType,snippet}], nearest:[…] }One call shows you which hubs already exist and what each is about — decide whether a
candidate cluster belongs under an EXISTING hub (attach, don't mint a duplicate hub) or
needs a new one. Spot-check individual members with memory_load only where the title
is ambiguous; do not load every page.
STEP 3 — DECIDE clusters (your LLM — this is the point). Group the orphan leaves by topic. A cluster is a set of leaves that genuinely share one topic and deserve a single hub above them. For duplicate clusters, decide:
relation="related" or a
typed <a rel="related"> with a canonical href) — merging distinct facts loses recall
precision.STEP 4 — ENACT per cluster: promote WITH children.
memory_curate(op="promote_to_hub",
pageId="<id of the cluster's natural anchor page>",
childPaths=["project/helios-billing.pkf",
"project/helios-db-choice.pkf",
"project/helios-deploy.pkf", …])One call: the anchor becomes a hub AND every member is attached under it as a child.
For a cluster that belongs under an EXISTING hub (found in STEP 2), don't promote —
attach the members to that hub (memory_write the member with
parentHubPath="<existing hub path>"; use op="append-section" rather than a
full-page rewrite when touching someone else's page). For merge losers and garbage:
memory_curate(op="supersede", pageId="…", reason="…").
STEP 5 — REBUILD hub navigation once per batch.
memory_curate(op="rebuild_index")Each touched hub's strict PKF #toc regenerates with child descriptions. The top INDEX
Contents changes automatically because it is a live graph projection—no INDEX body
revision is minted. Leaves that now have a hub parent drop out of the flat top level.
STEP 6 — MAINTAIN authored prose (your editorial duty). After the structure settles,
read the INDEX and touched hubs. Refresh each hub's <h2 id="overview"> and any stale
authored INDEX semantic section. Reuse the loaded section's existing data-sid; for a
new section, run pkf_mint_sids, then pkf_validate on the complete resulting page
before persisting. Section-operation content is a complete <section>…</section>:
memory_write(
path="project/helios.pkf",
op="rewrite-section",
section="overview",
content="<section><h2 id=\"overview\" data-sid=\"<reuse-or-minted-sec-id>\">Overview</h2><p>Helios is the Q3 billing replatform —
auth, billing and deploy decisions live here; the postmortems under it record why
rate limits were re-tuned twice. Start at <a href=\"<canonical href from browse>\"
rel=\"references\">the API spec</a>.</p></section>")A hub you just promoted has no overview yet — op="append-section", section="overview"
seeds it. Never touch the #toc section while you are in there.
STEP 7 — VERIFY and REPORT. Call memory_curate(op="candidates") again — the
orphans you clustered should be gone. Then report the structural changes in your
reply: which hubs you created/promoted, how many children each absorbed, what you
superseded and why, which overviews you wrote/refreshed, which oversized advisories you
split or deliberately left, and the before→after orphan count. A convergence run that
ends without a structural report is unverifiable.
project/helios-* leaves → one hubcandidates returns 8 orphan leaves hanging off INDEX directly (project/helios-auth.pkf,
project/helios-billing.pkf, project/helios-db-choice.pkf, project/helios-deploy.pkf,
project/helios-api-spec.pkf, project/helios-rate-limits.pkf, project/helios-oncall.pkf,
project/helios-postmortem-0420.pkf).
# 1-2. read candidates + browse — no existing helios hub, all 8 are one topic
memory_curate(op="candidates", kind="orphans")
memory_browse(query="helios project")
# 3. decide: ONE cluster; helios-auth is the natural anchor
# 4. promote the anchor WITH the other 7 attached in the same call
memory_curate(op="promote_to_hub", pageId="<id-of-helios-auth>",
childPaths=["project/helios-billing.pkf", "project/helios-db-choice.pkf",
"project/helios-deploy.pkf", "project/helios-api-spec.pkf",
"project/helios-rate-limits.pkf", "project/helios-oncall.pkf",
"project/helios-postmortem-0420.pkf"])
# the 0420 postmortem is superseded by a newer incident page → archive it
memory_curate(op="supersede", pageId="<id-of-helios-postmortem-0420>",
reason="merged into project/helios-auth#incidents")
# 5. rebuild once — hub #toc regenerates; INDEX Contents follows the graph live
memory_curate(op="rebuild_index")
# 6. seed the new hub's overview (the machine never writes this prose)
memory_write(path="project/helios-auth.pkf", op="append-section", section="overview",
content="<section><h2 id=\"overview\" data-sid=\"<minted-sec-id>\">Overview</h2><p>Helios project knowledge —
auth is the anchor; billing/db/deploy/api-spec/rate-limits/oncall hang
under it. The 0420 postmortem is archived into #incidents.</p></section>")
# 7. verify: orphans 8 → 0 for this topic; REPORT the delta in your reply
memory_curate(op="candidates", kind="orphans")Result: the derived INDEX Contents shows one described hub entry instead of 8 bare leaves; the facts
are reachable INDEX → hub → leaf, and the hub opens with prose that says what lives
there. The flat star collapsed into a readable tree — in ~7 tool calls.
When one page absorbs a true duplicate: copy any durable fact the survivor lacks into
the survivor's matching section (memory_write with op="append-section" /
op="rewrite-section" — never a whole-page rewrite of a page another agent authored;
the section body follows pkf-writing), then supersede the loser with a reason naming
the survivor's path. The loser is archived, not hard-deleted — it stays for audit and
redirect.
rebuild_index ONCE, verify, report, stop. Do not loop
the whole graph every tick — Dream is incremental.#toc; rebuild_index is its only writer. Never store an
INDEX TOC copy—the reader derives Contents. Hub overviews and authored INDEX semantic
sections are your editorial territory.childPaths) and then re-anchoring members one at a
time — pass the children in the promote call.child-of links inside a HUB pointing down at leaves — child-of
edges point FROM the child TO the hub; the structural params get this right for you.#toc, or creating a stored INDEX #toc; declare edges +
rebuild_index. Leaving hubs with no overview prose is its own anti-pattern.op="append-section").oversized advisory as an order — it is a split suggestion; splitting
a coherent hub just to satisfy a threshold destroys navigability. Judge, then report.supersede (reversible archive), never destroy.403 orchestrator_only — you are not the appointed orchestrator; that
is correct, not an error.orchestratorAgentId.© Prismer-AI, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Just SKILL.md in sdk/cloud/catalog/skills/memory-dream of Prismer-AI/PrismerCloud.
Open the folder on GitHubat commit e5d9444
Memory Dream next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Memory Dream this skillPrismer-AI/PrismerCloud | 1.6k | — | ~4.8k | Automated safety check: Pass | MIT | |
| MemPalace Memory SearchMemPalace/mempalace | 59k | — | ~1.4k | Automated safety check: Pass | MIT | |
| MemPalace Setup and OperationMemPalace/mempalace | 59k | — | ~2.2k | Automated safety check: Pass | MIT | |
| Qmdbreferrari/obsidian-mind | 5k | — | ~1.7k | Automated safety check: Pass | MIT | |
| Aiception Skill ExtractionNateBJones-Projects/OB1 | 4.7k | — | ~2k | Automated safety check: Notes | Custom licence | |
| Using LWC Memory and Graphssickn33/agentic-awesome-skills | 47k | 1 repos | ~2k | Automated safety check: Pass | Apache-2.0 |
MemPalace/mempalace
Mines project files and conversation exports into a local, searchable memory palace and recalls past work by semantic search through the mempalace CLI.
MemPalace/mempalace
Installs and configures MemPalace as a private local palace, a shared-brain hub or a client of an existing hub, including MCP registration and version-correct initialization.
breferrari/obsidian-mind
Search the vault using QMD semantic search. An agent skill from breferrari/obsidian-mind.
NateBJones-Projects/OB1
Pulls reusable knowledge out of work sessions and turns it into new skills, checking existing notes and skills first to avoid duplicates.
sickn33/agentic-awesome-skills
Keeps project decisions, research and verified results available across coding-agent sessions through LWC memory, a document Wiki graph and a CodeGraph code index.
andrea9293/mcp-documentation-server
A skill your agent uses when you need to store, retrieve, search, or manage documents in a local knowledge base with semantic search and hybrid (vector + full-text) retrieval.
Prismer-AI/PrismerCloud
Gives an agent account-scoped access to Gmail, Calendar, Drive, Contacts, Docs and Sheets through the gws CLI or a bundled Python client.
Prismer-AI/PrismerCloud
Walks an agent through creating, importing, editing, validating, testing and publishing Prismer Skills with a fixed workflow and bundled scripts.
Prismer-AI/PrismerCloud
Operates a mailbox from the terminal with the external Himalaya CLI over IMAP, SMTP, Notmuch or Sendmail, separate from any built-in email gateway adapter.
Prismer-AI/PrismerCloud
Generates one image from a text prompt with a bundled Node.js helper and delivers it once as the attachment to the current Prismer reply.
Prismer-AI/PrismerCloud
Produces 3Blue1Brown-style explainer animations with Manim Community Edition for math, algorithms, equations and architecture diagrams, with planning and rendering references.
Prismer-AI/PrismerCloud
Creates or updates Prismer role templates from a persona, SOP or job description, and turns a role into a working agent that runs its first task through a bundled script.
Categories
Curate the workspace memory wiki as its appointed orchestrator. Memory Dream is an agent skill from Prismer-AI/PrismerCloud. Curate the workspace memory wiki as its appointed orchestrator.
Memory Dream fits situations like: A Cloud-scheduled Dream task; A users direct curation request.
Run `npx skills add Prismer-AI/PrismerCloud --skill memory-dream -a claude-code`. Or copy the skill folder (sdk/cloud/catalog/skills/memory-dream in Prismer-AI/PrismerCloud) into .claude/skills/memory-dream in your project. Claude Code loads it when a task matches its description.
Run `npx skills add Prismer-AI/PrismerCloud --skill memory-dream -a codex`. Or copy the skill folder (sdk/cloud/catalog/skills/memory-dream in Prismer-AI/PrismerCloud) into .agents/skills/memory-dream in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add Prismer-AI/PrismerCloud --skill memory-dream -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/memory-dream, .gemini/skills/memory-dream, .github/skills/memory-dream and .opencode/skills/memory-dream in your project.
SKILL.md names no scripts, command-line tools or credentials: Memory Dream is instructions for the agent only.
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
Memory Dream is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 4.8k tokens (SKILL.md is roughly 19k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with Memory Dream: MemPalace Memory Search (MemPalace/mempalace, 59k stars), MemPalace Setup and Operation (MemPalace/mempalace, 59k stars), Qmd (breferrari/obsidian-mind, 5k stars) and Aiception Skill Extraction (NateBJones-Projects/OB1, 4.7k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Prismer-AI (a GitHub organization) maintains it in Prismer-AI/PrismerCloud, which has 1,555 GitHub stars. The repository holds 88 skills in this directory. The repository was last updated on September 30, 2026.
Source: Prismer-AI/PrismerCloud on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.