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 skills add tranhieutt/software_development_department --skill dreamgit clone --depth 1 https://github.com/tranhieutt/software_development_departmentWrote 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/skills/tranhieutt/software_development_department/dream)<a href="https://agentmods.dev/skills/tranhieutt/software_development_department/dream"><img src="https://agentmods.dev/badge/skills/tranhieutt/software_development_department/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/skills/tranhieutt/software_development_department/dream"><img src="https://agentmods.dev/badge/skills/tranhieutt/software_development_department/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 | $0.00050 | $0.01649 |
| Opus 5 | $0.00025 | $0.00825 |
| Sonnet 5 | $0.00010 | $0.00330 |
| Haiku 4.5 | $0.00005 | $0.00165 |
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 7d 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.
**Memory directory:** find the path from your system prompt — look for the "auto memory" section which says "You have a persistent, file-based memory system at `<path>`". That is your memory directory. If no such section How it starts
The opening of the file, as written. The whole thing — 129 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Dream: Memory Consolidation
You are performing a dream — a reflective pass over your memory files. Synthesize what you've learned recently into durable, well-organized memories so that future sessions orient quickly.
Memory directory: find the path from your system prompt — look for the "auto memory" section which says "You have a persistent, file-based memory system at <path>". That is your memory directory. If no such section exists, default to ~/.claude/projects/<sanitized-cwd>/memory/ where <sanitized-cwd> is the current working directory with / replaced by -.
Session transcripts: look for *.jsonl files in the project directory inside ~/.claude/projects/ that corresponds to the current working directory. These are signal sources — grep narrowly, do NOT read whole files.
If the user passed a topic argument (e.g., /dream feedback), focus Phase 2–3 on that topic only. Still run Phase 1 and Phase 4 in full.
Phase 1 — Orient
mkdir -pthe memory directory if it doesn't exist yet.lsthe memory directory — note all existing topic files.- Read
MEMORY.md(if it exists) to understand the current index. If it doesn't exist, this is a fresh start — you'll create it in Phase 4. - Skim existing topic files to build a picture of what's already recorded — so you improve rather than duplicate.
- If
logs/orsessions/subdirectories exist, note their presence for Phase 2. - Check for topic files missing mandatory YAML frontmatter (name, description, type) — flag these for repair in Phase 3.
Report: memory directory path, file count, MEMORY.md line count (or "new"), any frontmatter issues found.
Phase 2 — Gather recent signal
Look for new information worth persisting. Do NOT exhaustively read transcripts. Look only for things you already suspect matter.
Sources in priority order:
- Daily logs (
logs/YYYY/MM/YYYY-MM-DD.md) if present — append-only stream, check recent entries first. - Drifted facts — scan existing memories for claims that may contradict the current codebase (file paths, function names, flags). Verify with Glob/Grep before marking as stale.
- Transcript search — grep JSONL files narrowly for specific context:
Useful search terms: user corrections ("don't", "no not", "stop"), confirmations ("exactly", "perfect"), role signals ("I'm a", "I own"), decisions ("we're going with", "we decided"), deadlines, external system URLs.grep -rn "<narrow term>" ~/.claude/projects/ --include="*.jsonl" | tail -50
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.
- 7d ago First seen · 129 lines · 50 tokens per session scan B 2737b2466e09
dream is a skill published in the GitHub repository tranhieutt/software_development_department (72 stars, last pushed 3mo ago), licensed MIT. It adds 50 tokens to every session and 1,649 once invoked, about $0.0003 per session on Opus 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 2026-09-03.
Other skills, from other repositories
calibrating
Use after a knowledge-work deliverable ships or gets human edits. Triggers on "that draft worked", "they rewrote half of it", "remember this for next time", or when the user returns with feedback on a shipped doc. Not for code retrospectives.
bochi
A Japanese-speaking thinking and context assistant for the Mac command line. It helps with notes, light conversation, idea development, summaries, and routing deeper discussions to Discord.
cm-continuity
Working memory protocol — maintains context across sessions via CONTINUITY.md. Inspired by Loki Mode. Read at turn start, update at turn end. Captures mistakes and learnings to prevent repeating errors.
cm-continuity
Working memory protocol — maintains context across sessions via CONTINUITY.md. Inspired by Loki Mode. Read at turn start, update at turn end. Captures mistakes and learnings to prevent repeating errors.
cm-notebooklm
Sync high-value dev knowledge (skills, decisions, lessons) into Google NotebookLM for cloud recall, podcasts, and flashcards. Pairs with Dockit/deep-search. Use for NotebookLM, nlm, knowledge base, skill sync, or cross-machine AI memory.
cm-continuity
Working memory protocol — maintains context across sessions via CONTINUITY.md. Inspired by Loki Mode. Read at turn start, update at turn end. Captures mistakes and learnings to prevent repeating errors.