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/search-threadsnpx skills add mostlyharmless-ai/watercooler --skill search-threadsgit 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/search-threads)<a href="https://agentmods.dev/skills/mostlyharmless-ai/watercooler/search-threads"><img src="https://agentmods.dev/badge/skills/mostlyharmless-ai/watercooler/search-threads.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 | $0.00035 | $0.01020 |
| Opus 5 | $0.00017 | $0.00510 |
| Sonnet 5 | $0.00007 | $0.00204 |
| Haiku 4.5 | $0.00003 | $0.00102 |
Grade A, and why
search-threads 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 — 105 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Search Threads with Filters
Search: $ARGUMENTS
Filter Syntax
Parse these filters from arguments:
role:X- Filter by role (planner, implementer, critic, tester, pm, scribe)type:X- Filter by entry type (Note, Plan, Decision, PR, Closure)after:DATE/before:DATE- Time range (ISO format: 2024-01-15)thread:X- Specific thread topicstatus:X- Thread status (OPEN, CLOSED)agent:X- Filter by agent nameor:(flag, no value) - Use OR token matching instead of AND (see below)- Remaining text becomes the search query
Token matching default is AND. Multi-keyword queries like
"decided committed resolved"require ALL tokens present in the same entry — this often returns 0 hits. Addor:to use OR matching: any token qualifies. Useor:whenever searching for any of several keywords.
Parsing Rules
- Split
$ARGUMENTSon whitespace into tokens - A token is a filter if it matches
^(role|type|after|before|thread|status|agent):[^\s]+$ - The
or:token (no value) setsquery_operator="OR"— consume it as a flag, not query text - The filter key is everything before the first
:, the value is everything after - All non-filter tokens are joined with spaces to form the query text
Edge Cases
role:planner:advanced→ key=role, value=planner:advanced(first colon splits)- Multiple values for same key → last one wins
- Empty value (
role:) → ignore, treat entire token as query text - No query text → search with filters only (empty query string)
Steps
-
Parse arguments into filters and query text using rules above.
-
Load MCP tool:
ToolSearch: select:mcp__watercooler__watercooler_search -
Execute search with parsed query and filters. Pass each filter as its own named parameter — omit any filter param that was not explicitly parsed from the arguments. Do NOT use a
filters={}dict (no such parameter exists):# Example: only role was given mcp__watercooler__watercooler_search( query="config", mode="entries", role="planner" ) # Example: multiple filters mcp__watercooler__watercooler_search( query="", mode="entries", role="planner", entry_type="Decision" ) # Example: thread + status filters mcp__watercooler__watercooler_search( query="", mode="entries", thread_topic="mcp-migration", thread_status="OPEN" ) # Example: no filters, just query text mcp__watercooler__watercooler_search( query="config migration", mode="entries" ) # Example: OR mode — any of several keywords (use when or: flag is set) mcp__watercooler__watercooler_search( query="decided resolved committed opted agreed chosen", query_operator="OR", mode="entries" )
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 · 105 lines · 35 tokens per session scan A 1d963e4bbe39
search-threads is a skill published in the GitHub repository mostlyharmless-ai/watercooler (13 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 35 tokens to every session and 1,020 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.
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