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研究检索只读github未标认证来源可访问许可证需确认审计异常

dreamdream 搜索

Agent Skill

dream 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

535

周安装

23

GitHub Stars

160

下载量

188
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:dream(dream 搜索)
来源仓库:https://github.com/yonatangross/orchestkit
仓库路径:skills/dream
安装命令:
npx skills add https://github.com/yonatangross/orchestkit --skill dream
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/yonatangross/orchestkit --skill dream

简介

用于基于关键词和任务场景的信息检索与筛选。

  • 适合快速定位候选方案或参考资料。dream 属于研究检索类 Skill,可作为该场景下的辅助能力补充。
  • 可结合来源仓库进一步验证实际功能。适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。
  • 安装前建议确认权限范围及是否触发外部请求。
  • 注意搜索结果的真实性需人工交叉核对。

SKILL.md

Dream - Memory Consolidation

Deterministic memory maintenance: detect stale entries, merge duplicates, resolve contradictions, rebuild the MEMORY.md index. All pruning decisions are based on verifiable checks (file exists? function exists? duplicate content?), not LLM judgment.

Argument Resolution

DRY_RUN = "--dry-run" in "$ARGUMENTS"  # Preview changes without writing

Overview

Memory files accumulate across sessions. Over time they develop problems:

  • Stale references — memories pointing to files, functions, or classes that no longer exist
  • Duplicates — multiple memories covering the same topic with overlapping content
  • Contradictions — newer memories superseding older ones without cleanup
  • Index drift — MEMORY.md index out of sync with actual memory files

This skill fixes all four problems using deterministic checks only.


STEP 1: Discover Memory Files

# Find the memory directory (agent-specific or project-level)
# Agent memory lives in: .claude/agent-memory/<agent-id>/
# Project memory lives in: .claude/projects/<hash>/memory/
# Also check: .claude/memory/

memory_dirs = []
Glob(pattern=".claude/agent-memory/*/MEMORY.md")
Glob(pattern=".claude/projects/*/memory/MEMORY.md")
Glob(pattern=".claude/memory/MEMORY.md")

# For each discovered MEMORY.md, glob all *.md files in that directory
for dir in memory_dirs:
    Glob(pattern=f"{dir}/../*.md")  # All memory files alongside MEMORY.md

Read every discovered memory file. Parse frontmatter (name, description, type) and body content. Build an in-memory inventory:

inventory = [{
    "path": "/abs/path/to/file.md",
    "name": frontmatter.name,
    "type": frontmatter.type,  # user, feedback, project, reference
    "description": frontmatter.description,
    "body": body_text,
    "file_refs": [],      # extracted file paths
    "symbol_refs": [],    # extracted function/class names
    "topics": [],         # key phrases for duplicate detection
}]

STEP 2: Detect Staleness

For each memory file, extract references and verify they still exist.

2a: File Path References

Extract paths that look like file references (patterns: paths with / and file extensions, backtick-wrapped paths):

# Regex-like extraction from body text:
# - Paths containing / with common extensions: .py, .ts, .tsx, .js, .json, .md, .yaml, .yml, .sh
# - Backtick-wrapped paths: `src/something/file.ts`
# - Quoted paths in frontmatter descriptions

for ref in file_refs:
    Glob(pattern=ref)  # Check if file exists
    # If no match → mark as STALE_FILE_REF

2b: Symbol References

Extract function/class names (patterns: function_name(), ClassName, def function_name):

for symbol in symbol_refs:
    Grep(pattern=symbol, path=".", output_mode="files_with_matches", head_limit=1)
    # If no match → mark as STALE_SYMBOL_REF

2c: Staleness Classification

FindingClassificationAction
All file refs valid, all symbols foundFRESHKeep
Some file refs missingPARTIALLY_STALEFlag for review
All file refs missing AND all symbols missingFULLY_STALEPrune candidate
No external refs (pure decision/preference)EVERGREENKeep

Only memories classified as FULLY_STALE are auto-pruned. PARTIALLY_STALE memories are reported but kept — the user decides.


STEP 3: Detect Duplicates

Compare memories pairwise within the same directory. Two memories are duplicates when:

  1. Same type (both feedback, both project, etc.)
  2. Overlapping topic — 60%+ of significant words (excluding stopwords) appear in both bodies
  3. Same subjectname or description fields reference the same concept
stopwords = {"the", "a", "an", "is", "are", "was", "were", "be", "been",
             "have", "has", "had", "do", "does", "did", "will", "would",
             "could", "should", "may", "might", "can", "shall", "to", "of",
             "in", "for", "on", "with", "at", "by", "from", "as", "into",
             "through", "during", "before", "after", "this", "that", "it",
             "not", "no", "but", "or", "and", "if", "then", "than", "so"}

def significant_words(text):
    words = set(text.lower().split()) - stopwords
    return {w for w in words if len(w) > 2}

def overlap_ratio(words_a, words_b):
    if not words_a or not words_b:
        return 0.0
    intersection = words_a & words_b
    smaller = min(len(words_a), len(words_b))
    return len(intersection) / smaller if smaller > 0 else 0.0

# For each pair with same type:
#   if overlap_ratio >= 0.6 → DUPLICATE pair
#   Keep the NEWER file (by filesystem mtime), prune the older

STEP 4: Resolve Contradictions

Contradictions occur when two memories of the same type make opposing claims about the same subject. Detection:

  1. Same type + same topic (overlap >= 0.4 but < 0.6 — related but not duplicate)
  2. Negation signals — one body contains negation of the other's assertion:

- "do X" vs "do not X" / "don't X" / "never X" - "use X" vs "avoid X" / "stop using X" - "prefer X" vs "prefer Y" (for same decision domain)

negation_pairs = [
    ("do ", "do not "), ("do ", "don't "),
    ("use ", "avoid "), ("use ", "stop using "),
    ("prefer ", "don't prefer "), ("always ", "never "),
]

# For each pair flagged as contradictory:
#   Keep the NEWER file (more recent decision supersedes)
#   Prune the older file

STEP 5: Execute Changes (or Dry Run)

Dry Run Mode (--dry-run)

If --dry-run flag is present, skip all writes. Output the full report (Step 6) with [DRY RUN] prefix and list what WOULD be changed:

[DRY RUN] Would delete: .claude/agent-memory/foo/stale_old_path.md (FULLY_STALE)
[DRY RUN] Would delete: .claude/agent-memory/foo/duplicate_auth.md (DUPLICATE of auth_patterns.md)
[DRY RUN] Would delete: .claude/agent-memory/foo/old_preference.md (CONTRADICTED by new_preference.md)
[DRY RUN] Would rebuild: .claude/agent-memory/foo/MEMORY.md (3 entries removed, 12 remaining)

Live Mode

# 1. Delete FULLY_STALE files
for stale in fully_stale_files:
    Bash(command=f"rm '{stale['path']}'")

# 2. Delete DUPLICATE files (keep newer)
for dup in duplicate_pairs:
    older = dup["older"]
    Bash(command=f"rm '{older['path']}'")

# 3. Delete CONTRADICTED files (keep newer)
for contradiction in contradiction_pairs:
    older = contradiction["older"]
    Bash(command=f"rm '{older['path']}'")

# 4. Rebuild MEMORY.md index from surviving files

Rebuild MEMORY.md

Read all surviving .md files (excluding MEMORY.md itself). Generate the index:

# <Directory Name> Memory

- [Name](filename.md) -- one-line description from frontmatter

Rules for the rebuilt index:

  • One line per memory file, under 150 characters
  • Sorted alphabetically by filename
  • Total index must stay under 200 lines
  • If over 200 lines after rebuild, warn the user (do not auto-truncate content memories)
# Write the rebuilt MEMORY.md
Write(path="<memory_dir>/MEMORY.md", content=rebuilt_index)

STEP 6: Report

Output a summary table after consolidation:

## Dream Consolidation Report

| Metric | Count |
|--------|-------|
| Memory directories scanned | N |
| Total memory files scanned | N |
| Stale entries pruned | N |
| Duplicates merged | N |
| Contradictions resolved | N |
| Partially stale (kept, flagged) | N |
| Evergreen (no external refs) | N |
| Surviving memories | N |
| MEMORY.md indexes rebuilt | N |

### Changes Made

| File | Action | Reason |
|------|--------|--------|
| `path/to/file.md` | DELETED | Fully stale: all referenced files removed |
| `path/to/old.md` | DELETED | Duplicate of `path/to/new.md` |
| `path/to/outdated.md` | DELETED | Contradicted by `path/to/current.md` |

### Flagged for Review (PARTIALLY_STALE)

| File | Missing References |
|------|-------------------|
| `path/to/file.md` | `src/old/path.ts` no longer exists |

If --dry-run, prefix the entire report with:

[DRY RUN] No files were modified. Run without --dry-run to apply changes.

Error Handling

ConditionResponse
No memory directories foundReport "No memory directories found" and exit
No memory files in directoryReport "Directory empty, nothing to consolidate"
All memories are FRESHReport "All N memories are current, nothing to prune"
MEMORY.md exceeds 200 lines after rebuildWarn user, do not auto-truncate
File deletion failsReport error, continue with remaining files
Memory file has no frontmatterTreat as EVERGREEN (cannot verify refs without metadata)

When NOT to Use

  • To store new decisions -- use /ork:remember
  • To search past decisions -- use /ork:memory search
  • To load context at session start -- use /ork:memory load
  • After fewer than 5 sessions -- memory files are unlikely to have accumulated enough staleness

Related Skills

  • ork:remember -- Store decisions and patterns (write-side)
  • ork:memory -- Search, load, sync, visualize (read-side)

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

37.22%
按下载量换算70

Claude

29.96%
按下载量换算56

Cursor

17.25%
按下载量换算32

Gemini CLI

9.38%
按下载量换算18

安全审计

Gen Agent Trust Hub

未通过

Socket

通过

Snyk

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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