Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/conorbronsdon/agent-context-osnpx agentmods add commands/conorbronsdon/agent-context-os/dream-applyWrote 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/conorbronsdon/agent-context-os/dream-apply)<a href="https://agentmods.dev/commands/conorbronsdon/agent-context-os/dream-apply"><img src="https://agentmods.dev/badge/commands/conorbronsdon/agent-context-os/dream-apply/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/conorbronsdon/agent-context-os/dream-apply"><img src="https://agentmods.dev/badge/commands/conorbronsdon/agent-context-os/dream-apply.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 | $0.00020 | $0.03273 |
| Opus 5.5 | $0.00008 | $0.01309 |
| Sonnet 5.5 | $0.00004 | $0.00655 |
| Haiku 4.5 | $0.00002 | $0.00327 |
Grade A, and why
dream-apply scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 188 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/dream-apply — review + apply a curator pass
Substrate background: docs/dream-architecture.md.
Usage
/dream-apply {ISO-timestamp}
/dream-apply latest # auto-resolves to most recent .dreams/ subdir
Steps
1. Resolve the dream dir
Run the executable validator before reading or applying any artifact:
python3 scripts/dream/validate-memory.py artifact "${ARGUMENTS:-latest}"
Parse the returned JSON. Set MEMORY_DIR from memory_dir, TS from timestamp, and the dream dir from dream_dir. The helper rejects malformed timestamps, path traversal, absolute or control-character arguments, symlinked artifact components, missing proposals.json/REPORT.md, malformed or colliding proposal schemas, unknown/roadmap actions, empty evidence, unsafe control-file targets, and any proposal filename outside the validated memory root. It also requires the memory repository to be clean before review; stop so any host auto-memory change can be reviewed or snapshotted separately.
Do not create or guess a memory directory when a check fails. Stop and direct the user to docs/auto-memory.md.
If $ARGUMENTS is latest or empty, the helper resolves the most recent valid dream artifact by name. Do not reimplement timestamp/path selection in prose.
2. Load the proposal artifact
Read the validated $dream_dir/proposals.json and $dream_dir/REPORT.md. If either becomes missing or changes after validation, rerun the helper and stop on any error.
3. Show the report header
Print the top of REPORT.md (header + counts). Don't dump the whole thing — the user already has it if they want the full text.
4. Walk each proposal
For each proposals[i]:
a. Print a header, then the fields relevant to the action:
─── Proposal {id} ({i+1}/{N}) ───
Action: {action} Confidence: {confidence}
Reasoning: {reasoning}
Evidence:
- {evidence[0]}
- {evidence[1]}
modify(content):Target: {target}thenCurrent:/Proposed:excerpts.archive:Target: {target}then the one-line archive reason.merge(structural):Absorb: {targets}→Survivor: {survivor}(net index lines: {net_index_lines}), then themerged_bodyand theindex_changes.split(structural):Target: {target}→Into: {result_files[*].name}, then each child'spurpose+index_lineand itsbody.add/flag: target + proposed content (add) or the flagged concern (flag).
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.
- 1mo ago Changed 4d92d3b919ec
- 1mo ago First seen · 188 lines · 20 tokens per session scan A cb10230540d9
dream-apply is a command published in the GitHub repository conorbronsdon/agent-context-os (30 stars, last pushed yesterday), licensed MIT. It adds 20 tokens to every session and 3,273 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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