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 mostlyharmless-ai/watercooler --skill watercooler-onboardinggit 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/watercooler-onboarding)<a href="https://agentmods.dev/skills/mostlyharmless-ai/watercooler/watercooler-onboarding"><img src="https://agentmods.dev/badge/skills/mostlyharmless-ai/watercooler/watercooler-onboarding/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/mostlyharmless-ai/watercooler/watercooler-onboarding"><img src="https://agentmods.dev/badge/skills/mostlyharmless-ai/watercooler/watercooler-onboarding.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 | $0.00079 | $0.12069 |
| Opus 5 | $0.00039 | $0.06034 |
| Sonnet 5 | $0.00016 | $0.02414 |
| Haiku 4.5 | $0.00008 | $0.01207 |
Grade A, and why
watercooler-onboarding scanned grade A with 1 finding 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 10d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
`fetch-papers` curl safety, extract to markdown via the `pdf-to-md` per-PDF Task subagent How it starts
The opening of the file, as written. The whole thing — 868 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Watercooler Repository Bootstrap
Create durable Watercooler seed context for a repository.
Arguments: $ARGUMENTS
Default behavior is Bootstrap: inspect the repository and write a bounded set of Watercooler entries. Read-only output exists only as a dry run:
dry-run,preview,read-only, ororient: do not write; print the exact seed entries that would be writtenrefresh: inspect existing seed threads and write additive refresh entries instead of trying to replace prior context--update-agent-context(orupdate-agent-context): after seeds are written and Step 5.5 verifies theonboardingtag landed, chain into theupdate-agent-contextskill (Phase 1) soCLAUDE.md/AGENTS.mdreflect the freshly-seeded threads. Existing files are backed up first (see Step 5.6). No-op in dry-run mode and when Step 5.5 has unresolved tag failures. Without the flag, Step 6 prints the equivalent command as a recommendation instead.- research pre-pass (default-on): before deep-history and the seeds, run the Step 2.3
research pre-pass — harvest the subject repo's external references (papers, source links)
from the README + docs into an
onboarding-bibliothread, and when a principal source paper exists, fetch + parse its bibliography and pull the salient secondary references in too. Auto-skips the network steps underno-biblio/no-github/local-only/offlineand in dry-run (prints planned entries, writes nothing); the offline harvest of explicit README links still runs. Detail inreferences/research-prepass.md. deep-history(opt-in): after the research pre-pass, run the Step 2.4 deep-history / PR-reasoning layer — mine PR history for abandoned/superseded approaches and write thehistory-*threads. Off by default (forge/PR mining is expensive); honors dry-run (prints planned findings, writes nothing). Detail inreferences/deep-history.md.- role hints (
implementer,planner,critic,tester,pm,scribe) shift the recommended entry path and risk emphasis - any other text is extra prioritization context
Do not produce a standalone read-only summary as the final artifact. A summary is not repository memory. The useful artifact is a small set of typed, sourced Watercooler entries.
Interpretation guidance, anti-laundering rules, and provenance standards live in
references/thesis.md. Load it when claims are uncertain or inferred.
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 10d ago First seen · 868 lines · 79 tokens per session scan A 50c84ec5ae05
watercooler-onboarding is a skill published in the GitHub repository mostlyharmless-ai/watercooler (13 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 79 tokens to every session and 12,069 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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