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 commands/marciopuga/cog/historygit clone --depth 1 https://github.com/marciopuga/cogWrote 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/marciopuga/cog/history)<a href="https://agentmods.dev/commands/marciopuga/cog/history"><img src="https://agentmods.dev/badge/commands/marciopuga/cog/history.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 | $0.00044 | $0.00576 |
| Opus 5 | $0.00022 | $0.00288 |
| Sonnet 5 | $0.00009 | $0.00115 |
| Haiku 4.5 | $0.00004 | $0.00058 |
Grade A, and why
history 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 5d 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 — 64 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Use this skill for deep memory search and recall. Trigger if the user says "what did I say about...", "when did we discuss...", "find that conversation about...", "history of...", or asks about past information that needs multi-file search. For simple date/keyword lookups, a quick Grep suffices — this skill is for when you need to piece together a narrative from multiple entries.
Domain
Memory recall — recursive search across all memory files, cross-referencing observations, entities, and action items.
Memory Path
All files under the resolved memory path: $COG_HOME/memory/ if COG_HOME is set, otherwise ~/cog/memory/. All memory/... references below are relative to this root.
Memory Files
Read on activation:
memory/hot-memory.md(for context on what's currently relevant)
Search across:
- All
observations.mdfiles (personal, work domains, cog-meta) - All
entities.mdfiles - All
action-items.mdfiles - All
hot-memory.mdfiles memory/glacier/(via index.md for targeted retrieval)
Process
Pass 1: Locate
- Extract keywords from the user's query (names, topics, dates, phrases)
- Grep the resolved memory root for each keyword
- Note which files matched and how many hits
- If >10 files match, narrow by domain or add query terms
- If 0 matches, try synonyms or related terms
- Check
memory/glacier/index.mdfor archived data matching the query
Pass 2: Extract
- Read the top 3-5 most relevant files (by hit density and recency)
- Extract the specific passages that match the query
- Track the timeline: when did the topic first come up? How did it evolve?
Pass 3: Synthesize
- Combine extracted passages into a coherent answer
- Present findings chronologically with dates
- If something seems incomplete, flag it:
"Found references to X in observations but no entity entry — want me to create one?"
Artifact Formats
Search result: YYYY-MM-DD: <summary of what was found>
Memory gap: Gap: referenced but not in memory — <topic>
Timeline: Chronological list of when a topic appeared and how it evolved
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.
- 5d ago First seen · 64 lines · 44 tokens per session scan A 8302b64e6ec1
history is a command published in the GitHub repository marciopuga/cog (375 stars, last pushed 2mo ago), licensed MIT. It adds 44 tokens to every session and 576 once invoked, about $0.0002 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.
Other commands, from other repositories
minutes-ideas
Surface recent voice memos and ideas captured from any device. Use when the user asks "what ideas did I have?", "what were my recent memos?", "what did I record while walking?", or wants to recall a captured thought.
learn
Force claude-smart to extract learnings from this session now.
session-end
I'll summarize this coding session and update the memory system with our accomplishments.
memory-store
Store an insight, decision, or pattern to memory.
analyze-context
USE WHEN you want to analyze project context before starting work on a task. Calls context + recall, then synthesizes goals, decisions, gotchas, and relevant memories into a pre-task brief.
cc-memory
Configure persistent memory that survives across sessions using a layered approach: split rule files for always-loaded context, auto-memory for organic learning, and optional MCP-backed long-term memory for large codebases.