dream
Skill2 starsChanged 23 days agoApache-2.0View on GitHub
Consolidate the project's durable knowledge (auto memory, CLAUDE.md lessons, notes files) against what actually happened in the session records written since the last run. Finds entries a later decision has contradicted, duplicates, and lessons that recurred in several sessions but were never written down, then writes one cited report and applies only what the user approves. Use when the user asks to "dream", "consolidate memory against the sessions", "what patterns are we repeating", "what keeps going stale", "clean up what Claude remembers". User-invoked only: never scheduled, never run by a hook. Does NOT fire at session close (that is session-summary) or to save a single fact (write that directly).
40 other projects use a version of this.
Adopted by
Most recently taken up by
- srinator22/New-Projecttheir own version, first seen Nov 8, 2026
- Melvynx/ai-maxxing-skillstheir own version, first seen Nov 8, 2026
- fjwood69/moritheir own version, first seen Nov 8, 2026
- alexandrosandre/agentic-engineeringtheir own version, first seen Nov 8, 2026
- MMACABRE/SKILLStheir own version, first seen Nov 8, 2026
- matteo-pacini/skillstheir own version, first seen Nov 1, 2026
Used with
Projects with it also use ingest, resume and 10 more.
Other files people use alongside this one.
Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
JaimeGBASkillingest
Digest an external source (article, paper, transcript, URL, book chapter) and integrate its content into the knowledge base
Also in 4 projects that use this
genomewalkerSkillresume
Resume a thread by loading its ~800-token context capsule
Also in 3 projects that use this
MicrosoftSkillinit
Also in 4 projects that use this
Hugging FaceSkillsimplify
Refactor code after tests pass. The "Refactor" phase of Red-Green-Refactor.
Also in 3 projects that use this
SentrySkilldeslop
Strip AI-generated noise from the diff before pushing — extra comments a human wouldn't write, defensive try/catch in trusted code paths, casts to any, inline imports in Python, and other style inconsistencies with the surrounding file. Use this immediately before commit so the diff stays clean.
Also in 3 projects that use this
AnthropicSkillstart
Set up your bio-research environment and explore available tools. Use when first getting oriented with the plugin, checking which literature, drug-discovery, or visualization MCP servers are connected, or surveying available analysis skills before starting a new project.
Also in 3 projects that use this
Other versions40 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 40
- Noizefield/audio-plugin-coder★ 334same name
- grandamenium/dream-skill★ 136same name
- sneg55/agent-starter★ 76same name
- oprogramadorreal/optimus-claude★ 75same name
- tranhieutt/software_development_department★ 71same name
- maxvaega/awesome-skills★ 40same name
- seldonframe/seldonframe★ 40same name
- hyperb1iss/hyperskills★ 36same name
- fjwood69/mori★ 32same name
- Melvynx/ai-maxxing-skills★ 32same name
30 more not listed
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository7 other files it gives its agents.
- cold-start-resumeSkill
- decision-packetSkill
- kill-zombiesSkill
- mirror-lookSkill
- move-and-repointSkill
- session-summarySkill
- transfer-promptSkill
The file
We split this skill into the rules it gives an agent
4 rules. 0 are common, 4 are distinctive, and 0 are very specific to this project.
0
Common
Most of the 39 edited versions we read keep these.
4
Distinctive
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 42
Orient: Read the watermark and list the records newer than it.
- Distinctive: line 45
Mine: Read every new record in full, not by search hit.
- Distinctive: line 74
Keep the "refuted" section.
- Distinctive: line 84
Point, do not copy: If a fact has a canonical home in the repository, the memory entry points there instead of restating it.
“Common” is measured: kept by at least 6 in 10 of the 39 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Dream
Sessions write durable knowledge one fact at a time. Over a few hundred sessions that store collects duplicates, entries a later decision contradicts, and lessons that recurred in five sessions and were written down in none. A dream reads the store, reads what happened since the last dream, and corrects the store.
A plain memory cleanup only looks at the memory files. A dream checks them against the record of what actually happened, and the record wins.
What it reads
| Role | Source |
| Store | Claude Code's auto memory for this project, plus any lessons or notes sections in CLAUDE.md and the project's notes files |
| Corpus | Session records written since the last dream (for example the ones session-summary writes), newest first, at most 100 per run |
| Ground truth, outranks the store | The decision log, the tracker, the changelog, and git history |
| Watermark | A small state file, for example .claude/dream-state.json: when the last dream ran and the newest record it consumed |
Read the watermark's timestamp, not the watermark file's modification time. Any edit moves the file's time and silently skips records. Enumerate session records by their own modification time against that timestamp.
Procedure
- Orient. Read the watermark and list the records newer than it. Fewer than about ten new records and the user did not insist: say so and stop, there is not enough to find patterns in. Count the store before touching it.
- Mine. Read every new record in full, not by search hit. Look for four things:
- the same class of mistake corrected in two or more sessions
- a preference the user stated more than once
- a workflow that converged across several sessions
- a store entry a later record or decision contradicts For a large backlog, split the records by date across parallel read-only agents and treat an item two agents surface independently as the stronger signal.
- Apply the two-record floor. One occurrence is an event, not a pattern. A NEW entry needs two independent records, or one record plus a decision log entry. A CORRECTION to an existing entry needs one decision or one record that clearly overtakes it.
- Verify each candidate. Re-open its sources and confirm them before it goes in the report. A candidate that came from a search hit or a title is not verified. A claim about live state (a service running, a setting on) is re-checked live, never taken from the record.
- Report. One file, every entry citing the records or decisions behind it. An entry with no citation is an invention and does not go in.
# Dream, YYYY-MM-DD HH:MM
Records read: <N>, <first> to <last>; <N> still pending
Store read: <N> entries
## Proposed corrections (entry, old -> new, source)
## Proposed new entries (text, the two sources)
## Proposed merges or drops (files, and every reference that would need repointing)
## Refuted on verification (candidates that failed step 4, one line each)- Apply what the user approves, then update the watermark.
Keep the "refuted" section. It is the dream's own error rate, and it is how you learn whether to trust the next run.
Rules
- The record wins. A memory entry contradicting a recorded decision is stale. Correct the memory and cite the decision, never the reverse.
- Never rewrite a record or a decision log entry. Those are read, not edited.
- Merges and drops are proposals. Other files may reference a memory entry by name; removing it needs those references repointed.
- Point, do not copy. If a fact has a canonical home in the repository, the memory entry points there instead of restating it.
- No secrets in the report or the store.
Anti-patterns
Finding patterns in three records. Applying a candidate nobody re-checked. Rewriting history because memory disagrees with it. Turning every one-off event into a permanent rule.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

