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/mostlyharmless-ai/watercooler/recallnpx skills add mostlyharmless-ai/watercooler --skill recallgit clone --depth 1 https://github.com/mostlyharmless-ai/watercoolerWrote 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/mostlyharmless-ai/watercooler/recall)<a href="https://agentmods.dev/skills/mostlyharmless-ai/watercooler/recall"><img src="https://agentmods.dev/badge/skills/mostlyharmless-ai/watercooler/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 | $0.00041 | $0.00532 |
| Opus 5 | $0.00020 | $0.00266 |
| Sonnet 5 | $0.00008 | $0.00106 |
| Haiku 4.5 | $0.00004 | $0.00053 |
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 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.
What it actually says
Recall
Query: $ARGUMENTS
Framing Detection
After stripping flags (e.g. --raw), inspect the remaining text:
- Question framing if text ends with
?OR starts with a question word: What, Why, How, Who, When, Where, Which, Is, Are, Was, Were, Did, Does, Can, Should - Context framing otherwise (topic or task description)
Steps
-
Load MCP tool:
ToolSearch: select:mcp__watercooler__watercooler_smart_query -
Execute query (scope to current repo with
code_path):mcp__watercooler__watercooler_smart_query(query="<$ARGUMENTS minus flags>", code_path="<repo root>") -
Present results based on framing:
Question framing — answer the question:
- Direct answer to the question
- Evidence — cite specific entries by entry_id
- Confidence and tier used (T1/T2/T3)
- Escalation note if tier escalated beyond T1
- Suggested follow-ups if answer is incomplete or uncertain
Context framing — summarize relevant context:
- Prior decisions related to this topic
- Relevant patterns or implementations
- Known issues or gotchas
- Related threads for deeper reading
- Tier used and escalation reason
-
Handle empty results:
- Suggest alternative search terms
- Note whether memory backends are configured
-
Raw output (if
--rawflag present):- Append full JSON response after the summary
Example Invocations
/recall authentication flow— context before implementing auth/recall What was decided about the config system?— direct question/recall Why did we choose markdown for threads?— direct question/recall --raw branch parity sync— raw JSON output/recall Who implemented the branch parity feature?— attribution lookup
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 · 63 lines · 41 tokens per session scan A 6caef11d56b8
recall is a skill published in the GitHub repository mostlyharmless-ai/watercooler (13 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 41 tokens to every session and 532 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 skills, from other repositories
agent-v3-memory-specialist
Agent skill for v3-memory-specialist - invoke with $agent-v3-memory-specialist.
agent-memory-coordinator
Agent skill for memory-coordinator - invoke with $agent-memory-coordinator.
agent-swarm-memory-manager
Agent skill for swarm-memory-manager - invoke with $agent-swarm-memory-manager.
agent-collective-intelligence-coordinator
Agent skill for collective-intelligence-coordinator - invoke with $agent-collective-intelligence-coordinator.
memory-management
AgentDB memory system with HNSW vector search. Provides 150x-12,500x faster pattern retrieval, persistent storage, and semantic search capabilities for learning and knowledge management. Use when: need to store successful patterns, searching for similar solutions, semantic lookup of past work, learning from previous…
state-management
STATE.md reading, writing, and field-level updates. Provides cross-session state persistence via .planning/STATE.md with structured fields for current task, completed phases, blockers, decisions, and quick tasks.