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/floomhq/moto/recallnpx skills add floomhq/moto --skill recallgit clone --depth 1 https://github.com/floomhq/motoWrote 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/floomhq/moto/recall)<a href="https://agentmods.dev/skills/floomhq/moto/recall"><img src="https://agentmods.dev/badge/skills/floomhq/moto/recall.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 5.1 | $0.00060 | $0.00622 |
| Opus 5 | $0.00030 | $0.00311 |
| Sonnet 5 | $0.00012 | $0.00124 |
| Haiku 4.5 | $0.00006 | $0.00062 |
Grade A, and why
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 6d 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 — 98 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Recall Skill
Recover working context after compaction. JSONL transcripts are NEVER deleted - they persist at ~/.claude/projects/*/. Compaction only clears the live context window.
Workflow
Step 1: Get Recent Messages
Use MCP tool (preferred):
recall_recent 5
CLI fallback:
session-recall --recent 10
Step 2: Find Active Task
Search for what was being worked on:
recall_search "workplan|task|fixing|implementing|debugging"
CLI fallback:
session-recall "workplan task fixing implementing"
Step 3: Find Active Workplan
find . ~/Downloads /root -name "WORKPLAN-*.md" -mtime -1 2>/dev/null | sort
Read the most recent one if found. It is the external brain - re-read it fully before continuing.
Step 4: Reconstruct Context
From the recalled messages and workplan, identify:
- What task was in progress
- What step was last completed
- What the next step is
- Any errors or blockers encountered
Use targeted searches if needed:
recall_search "error|failed|blocked|next step"
recall_decisions
CLI fallback:
session-recall --report
session-recall "error failed blocked"
Step 5: Persist Lessons (Optional)
If patterns or corrections were found in the session that should be saved:
recall_apply
MCP Tools Reference
| Tool | Purpose |
|---|---|
recall_search "keyword" |
Search transcripts by keyword |
recall_recent N |
Get last N human messages |
recall_report |
Analyze errors, retries, corrections |
recall_decisions |
Find key decisions made |
recall_list |
List available sessions |
recall_apply |
Persist lessons to CLAUDE.md/MEMORY.md |
CLI Fallback Reference
session-recall "keyword" # Search current session
session-recall --recent 10 # Last 10 human messages
session-recall --report # Session analysis
session-recall --all 5 # Cross-session (last 5 sessions)
Key Fact
Transcripts are at ~/.claude/projects/*/. They are append-only JSONL files. Compaction does not touch them. Any content from any past session is recoverable. Never say "can't recover" compacted content.
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.
- 6d ago First seen · 98 lines · 60 tokens per session scan A 1c412571081c
recall is a skill published in the GitHub repository floomhq/moto (32 stars, last pushed 2mo ago), licensed MIT. It adds 60 tokens to every session and 622 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.
Other skills, from other repositories
moai-foundation-context
Manages context window optimization, session state persistence, and token budget allocation for multi-agent workflows. Use for token budget management, context limits, or session handoff across agents.
catchup
Restore context after /clear by summarizing recent work and project state.
lane-memory
SMA-style project fact corpus under .agents/memory/. Opt-in via adoc stages.memory.enabled. Use when user says память, lane-memory, corpus, CORE, почему бот забыл, or an agent needs durable non-code facts. Not PROGRESS/LESSONS dumps.
doncheli-context-health
Report the current state of the context window and recommend compression or cleanup actions. Activate when user mentions "context health", "context window", "how much context", "context full", "running out of context", "compress context".
context-dump
Sync 7 days of GitHub activity (commits, PRs, issues, reviews) into a structured context dump. Optionally pull from Slack, GDrive, or Asana if configured. Use at the start of a session to get fully caught up before coding.
learning
Extracts reusable patterns from sessions. Use at session end to capture debugging insights and project-specific knowledge.