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 agentmods add skills/thinkfleetai/memmesh/dreamnpx skills add ThinkfleetAI/memmesh --skill dreamgit clone --depth 1 https://github.com/ThinkfleetAI/memmeshWrote 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/thinkfleetai/memmesh/dream)<a href="https://agentmods.dev/skills/thinkfleetai/memmesh/dream"><img src="https://agentmods.dev/badge/skills/thinkfleetai/memmesh/dream.svg" alt="Measured on agentmods" 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 | $0.00055 | $0.00423 |
| Opus 5 | $0.00028 | $0.00211 |
| Sonnet 5 | $0.00011 | $0.00085 |
| Haiku 4.5 | $0.00006 | $0.00042 |
Grade A, and why
dream 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 3d 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.
What it actually says
dream
Agent-driven consolidation. MemMesh keeps provenance, so consolidation is supersede/reject, not destructive rewrite.
1. Survey
{ "name": "memory_stats", "arguments": { "projectId": "<repo>" } }
A high total or a large superseded/rejected share signals it's worth a pass.
2. Find redundancy
Pull the set (memory_list) or search hot topics, and identify:
- Duplicates — same fact stored multiple times.
- Contradictions — two memories that can't both be true.
- Stale — superseded facts still cluttering results, or one-off noise.
3. Consolidate (confirm first; never touch pinned items)
- Contradiction / changed fact → keep the newest,
memory_supersedethe olderbyIdthe newer. Provenance is preserved. - Exact duplicate → keep one,
memory_deletethe rest (soft). - Stale noise →
memory_delete(soft) after confirming with the user.
Do not delete anything marked pinned (high importance / impact HIGH /
confirmed) — see the pin skill. When in doubt, supersede rather than delete.
4. Report
Summarize: N duplicates merged, M contradictions resolved, K stale retired, and the new total. Suggest re-running when stats drift again.
Hosted tenants can offload this to the server-side consolidator (
memory.consolidate/dedupin the SDK); locally, this agent-driven pass is the consolidation path.
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.
- 3d ago First seen · 46 lines · 55 tokens per session scan A 54654791e365
dream is a skill published in the GitHub repository ThinkfleetAI/memmesh (440 stars, last pushed 8d ago), licensed Apache-2.0. It adds 55 tokens to every session and 423 once invoked, about $0.0003 per session on Opus 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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