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.
git clone --depth 1 https://github.com/DevZonayed/MochiWrote 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/commands/devzonayed/mochi/comms-import)<a href="https://agentmods.dev/commands/devzonayed/mochi/comms-import"><img src="https://agentmods.dev/badge/commands/devzonayed/mochi/comms-import/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/commands/devzonayed/mochi/comms-import"><img src="https://agentmods.dev/badge/commands/devzonayed/mochi/comms-import.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 | $0.00031 | $0.00300 |
| Opus 5.5 | $0.00012 | $0.00120 |
| Sonnet 5.5 | $0.00006 | $0.00060 |
| Haiku 4.5 | $0.00003 | $0.00030 |
Grade A, and why
comms-import 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 1mo 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
Import an exported chat to fill history gaps. The user exports a chat from WhatsApp (Export chat -> without media or with media) and gives the .txt path.
- Confirm the target
chatId(runcomms_list_chatsto resolve a name -> JID if the user gave a name). The chat must already be on this repo's allowlist. - Call
comms_import_history({provider, accountId, chatId, filePath}).- The importer parses each line, normalizes it to the shared message shape, and reconciles by fingerprint — re-importing the same file is idempotent (no duplicates), and live/backfill records win over imported ones for the same content.
- WhatsApp exports are minute-resolution, so imported timestamps are less precise than live capture; that's a documented limitation.
Report counts (added vs merged). Do not print message bodies. Order is reconstructed at read time, so imported old messages slot correctly into the timeline.
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.
- 1mo ago First seen · 15 lines · 31 tokens per session scan A a5cdebce4c77
comms-import is a command published in the GitHub repository DevZonayed/Mochi (3 stars, last pushed 2d ago), licensed MIT. It adds 31 tokens to every session and 300 once invoked, about $0.0001 per session on Opus 5.5. 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.
Other commands, from other repositories
context-stats
Display context window usage and token statistics.
handoff
Write a session handoff document for continuity.
hades-validate-memory
Validate every Asphodel memory file and report results.
pack
Pack target repo with repomix. Snapshot for AI context. Cached per featureid.
reflect
After the user triggers this command, run Learning Loop steps 4-8. Two phases: first half (review + organize) completes, then automatically continues to second half (re-analyze + trim + wrap up).
recover
Restore memory from your GitHub backup when local memory files are lost or corrupted. Also works for cross-device sync.