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 cdeust/Cortex --skill cortex-consolidategit clone --depth 1 https://github.com/cdeust/CortexWrote 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/cdeust/cortex/cortex-consolidate)<a href="https://agentmods.dev/skills/cdeust/cortex/cortex-consolidate"><img src="https://agentmods.dev/badge/skills/cdeust/cortex/cortex-consolidate/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/cdeust/cortex/cortex-consolidate"><img src="https://agentmods.dev/badge/skills/cdeust/cortex/cortex-consolidate.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00084 | $0.00732 |
| Opus 5.5 | $0.00034 | $0.00293 |
| Sonnet 5.5 | $0.00017 | $0.00146 |
| Haiku 4.5 | $0.00008 | $0.00073 |
Grade A, and why
cortex-consolidate 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 — 77 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Consolidate — Memory Maintenance and Evolution
Keywords
consolidate, maintenance, cleanup, compress, decay, merge, evolve, sleep, replay, consolidation, memory health, prune, clean up, optimize memories, reduce noise
Overview
Run the full memory maintenance pipeline — modeled after biological memory consolidation. This includes heat decay (cooling unused memories), compression (full text to gist to tags), CLS consolidation (episodic to semantic), causal graph discovery, and sleep-like replay that strengthens important memory clusters.
Use this skill when: After a long session, after bulk imports, periodically (weekly), or when memory_stats shows too many hot memories or high noise.
Workflow
Step 1: Run Full Consolidation
cortex:consolidate({})
This runs the complete pipeline:
- Decay cycle — Cool memories by heat * decay_factor. Memories below cold threshold (0.05) become candidates for compression
- Compression — Old memories compress through stages: full text (7+ days) to gist, gist (30+ days) to tags
- CLS consolidation — Frequently-accessed episodic memories promote to semantic store (like hippocampal-to-cortical transfer)
- Causal discovery — PC Algorithm runs on entity co-occurrences to discover causal relationships
- Sleep compute — Dream-like replay strengthens clusters, summarizes related memories, and re-embeds compressed content
Step 2: Review Results
The response includes:
memories_decayed— how many cooled downmemories_compressed— how many were compressed (and to what level)memories_consolidated— how many promoted from episodic to semanticcausal_edges_discovered— new relationships foundreplay_clusters— memory clusters that were replayed and strengthened
Step 3: Selective Operations
For targeted maintenance instead of the full pipeline:
Forget specific memories:
cortex:forget({
"memory_id": <id>,
"hard": false
})
Soft delete (sets heat to 0) by default. Use "hard": true for permanent deletion. Protected memories require explicit "force": true.
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 First seen · 77 lines · 84 tokens per session scan A 78e9bc2e215f
cortex-consolidate is a skill published in the GitHub repository cdeust/Cortex (75 stars, last pushed 2d ago), licensed MIT. It adds 84 tokens to every session and 732 once invoked, about $0.0003 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.
Other skills, from other repositories
xmemo-memory
Persistent, user-owned memory for agents. Use the standalone runtime to remember, recall, search, preserve restart continuity, manage TODOs and expenses, inspect account overview, activity and stats diagnostics, or diagnose XMemo when MCP tools are unavailable. Not for codebase search, web search, or short-lived…
temporal-query
Answer time-travel questions over stored memory — what was believed at a past point in time, when a belief changed, and what replaced it. Use when the user says "as of", "back in", "at the time", "history of", "timeline", "what did I think then", or asks why a current memory contradicts an older one.
knowledge-audit
Review and clean up stored memories — find duplicates, contradictions, stale entries, and consolidate.
memory-commit
Use when the user explicitly says "remember this", "save this", "ghi nho", "luu lai", "save for next time", or otherwise asks to persist the immediately preceding context. Captures with the appropriate contexttype (decision, preference, fact, skill, task, conversation) so future sessions can retrieve it accurately.
recall-context
Use at session start, before significant decisions, or when a new task references a known project to recall mnemo memories matching the current working directory, recently edited files, or topic keywords. Helps maintain continuity across sessions and avoid redoing past research.
passport-bootstrap
Use when the user installs mnemo-mcp on a fresh machine and wants to restore prior memory state from S3 or Google Drive (Phase 2 passport sync). Triggers on phrases like "set up mnemo on this machine", "restore my memory passport", "import passport", "bootstrap mnemo", or when the user says they got a new laptop / VM…