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 danilop/claude-total-recall --skill conversation-recallgit clone --depth 1 https://github.com/danilop/claude-total-recallWrote 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/danilop/claude-total-recall/conversation-recall)<a href="https://agentmods.dev/skills/danilop/claude-total-recall/conversation-recall"><img src="https://agentmods.dev/badge/skills/danilop/claude-total-recall/conversation-recall/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/danilop/claude-total-recall/conversation-recall"><img src="https://agentmods.dev/badge/skills/danilop/claude-total-recall/conversation-recall.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 | $0.00060 | $0.01405 |
| Opus 5 | $0.00030 | $0.00702 |
| Sonnet 5 | $0.00012 | $0.00281 |
| Haiku 4.5 | $0.00006 | $0.00140 |
Grade A, and why
conversation-recall 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 12d 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 — 200 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Conversation Recall Skill
Search past Claude Code conversation history using semantic search.
When to Trigger
1. After Compaction
When context is compacted, earlier details are summarized. Search to recover originals:
- You see a
compact_boundarymarker - User references something discussed earlier but details are missing
- User says "like we discussed", "as I mentioned", "continue with..."
User: "Continue implementing the auth system like we discussed"
→ search_project_history(query="auth system implementation")
2. Cross-Session
Find discussions from previous sessions (not just the current one):
- "How did we fix that bug yesterday?"
- "What approach did we decide on last week?"
- "Remember when we refactored the database?"
User: "How did we fix that auth bug last week?"
→ search_project_history(query="auth bug fix")
3. Cross-Project
Find user preferences and patterns across all projects:
- "How do I usually handle errors?"
- "What's my preferred testing approach?"
- "What package manager do I use for Python?"
User: "How do I usually structure React components?"
→ search_global_history(query="React component structure")
Which Tool to Use
| Scope | Tool | Examples |
|---|---|---|
| Current project | search_project_history |
Bugs, decisions, implementations in this codebase |
| All projects | search_global_history |
User preferences, patterns, conventions |
Parameters
Both tools accept:
query(required): Keywords or sentence to searchafter(optional): Filter to messages on/after this date (inclusive). ISO 8601 format.before(optional): Filter to messages before this date (exclusive). ISO 8601 format.context_before_after(default: 3): Messages before/after each matchthreshold(default: 0.2): Minimum similarity (0-1)max_results(default: 10): Results to returnoffset(default: 0): Skip results for paginationinclude_subagents(default: true): Include agent conversations
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.
- 12d ago First seen · 200 lines · 60 tokens per session scan A 020074e258d2
conversation-recall is a skill published in the GitHub repository danilop/claude-total-recall (2 stars, last pushed 7mo ago), licensed MIT. It adds 60 tokens to every session and 1,405 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 2026-08-31.
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