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.
git clone --depth 1 https://github.com/yonatangross/orchestkitWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/commands/yonatangross/orchestkit/dream)<a href="https://agentmods.dev/commands/yonatangross/orchestkit/dream"><img src="https://agentmods.dev/badge/commands/yonatangross/orchestkit/dream/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/commands/yonatangross/orchestkit/dream"><img src="https://agentmods.dev/badge/commands/yonatangross/orchestkit/dream.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What 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 5.1 | $0.00056 | $0.05183 |
| Opus 5.5 | $0.00022 | $0.02073 |
| Sonnet 5.5 | $0.00011 | $0.01037 |
| Haiku 4.5 | $0.00006 | $0.00518 |
Grade B, and why
dream scanned grade B with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 18d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Reads agent configuration directoriesmediumAgent snooping
.claude/, .codex/, .gemini/ hold keys, settings and other credentials a mod has no legitimate need for.
grep -l -i "<topic-key>" ~/.claude/projects/*/memory/MEMORY.md \ How it starts
The opening of the file, as written. The whole thing — 458 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Auto-generated from skills/dream/SKILL.md
Source: https://github.com/yonatangross/orchestkit
Dream - Memory Consolidation
Deterministic memory maintenance: detect stale entries, merge duplicates, resolve contradictions, rebuild the MEMORY.md index. All pruning decisions are based on verifiable checks (file exists? function exists? duplicate content?), not LLM judgment.
Argument Resolution
DRY_RUN = "--dry-run" in "$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.
Finish line. Done means: MEMORY.md is rebuilt from the surviving files and passes the STEP 5.5 verify, the STEP 6 report is printed, and STEPs 7 to 9 have each run or printed a one-line skip reason.
Budget: one read pass over the discovered memory files (STEP 1), one MEMORY.md rewrite (STEP 5) plus the STEP 5.5 trailer rewrite when the index is over budget, and no subagents (every check is deterministic); stop and report at the finish line or the first cap, whichever comes first.
Cadence (CC 2.1.142+): Reactive compaction now sizes its first summarize attempt to the actual overflow, so long sessions stall mid-turn far less often. The "run nightly" cadence can relax toward "run when memory files accumulate" — consolidation is no longer needed to head off compaction inefficiency.
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
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.
- 18d ago Changed d3c785d9af59
- 22d ago Changed · -36 lines · +1 tokens per session a10db717e8d8
- 24d ago Changed · -5 lines 8d8b60c4ec90
- 1mo ago Changed 50a0e64b9b4b
- 1mo ago Changed 9fe4ec0feb90
- 1mo ago Changed · +20 lines 4cebadefdee1
- 1mo ago First seen · 479 lines · 55 tokens per session scan B aaee491627b6
dream is a command published in the GitHub repository yonatangross/orchestkit (296 stars, last pushed today), licensed MIT. It adds 56 tokens to every session and 5,183 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it B with 1 finding (reads agent configuration directories). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other commands, from other repositories
autocompact
Save context to file for automated compact workflow.
memory-shard-roll
Close finished calendar years out of an over-ceiling log-topic file (worklog/support/decisions) into a locked topic-YYYY.md shard, and update the parent's Shards index.
install-attestor
Install Attestor for Claude Code — one default profile, zero questions.
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.