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/sonichi/sutando/observenpx skills add sonichi/sutando --skill observegit clone --depth 1 https://github.com/sonichi/sutandoWrote 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/sonichi/sutando/observe)<a href="https://agentmods.dev/skills/sonichi/sutando/observe"><img src="https://agentmods.dev/badge/skills/sonichi/sutando/observe.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 | $0.00042 | $0.00835 |
| Opus 5 | $0.00021 | $0.00417 |
| Sonnet 5 | $0.00008 | $0.00167 |
| Haiku 4.5 | $0.00004 | $0.00084 |
Grade A, and why
observe 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 4d 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 — 62 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/observe — NL → observation policy (MVP step 1)
The owner (or a scoped user) says what they want watched, in natural language:
/observe watch doc changes in this room and ping me daily
The core agent COMPILES that into a structured draft; everything deterministic
around the compilation lives in observe_policy.py. Design + resolved
decisions: workspace notes/observe-mvp-slice-design.md.
Processing convention (core agent, on an /observe <text> owner task)
- Compile (LLM-side — you): produce a draft dict:
{room_id, event_types[], mode, cost_cap?, created_by, source_text}room_id: the task'schannel_idunless the text names another room.event_types: map the intent to plane types (message.created,reaction.added,artifact.updated,member.joined, …). Unknown intent → ask, don't guess.mode:observe(context only) |record(journal) |notify(ping the owner) |taskify(promote batches to ambient tasks — never standing-approved).
- Validate:
observe_policy.validate_draft(draft)— errors go back to the user verbatim; do not "fix" a draft silently. - Standing approval:
evaluate_standing_approval(rec, owner_mxid=…, owner_rooms=…).owner_rooms= rooms the owner OWNS — created by the owner or where the owner holds PL≥50 (checked via the room state the core already has; when ownership is unknown, the room is NOT in scope). Familiarity — "the owner sent a task from here" — is NOT ownership: a shared room must never enter standing-approval scope (001 review; the server's four-way authz still gates the subscribe, but the standing-approval semantic is stricter by design). True → save +transition(id, "active")+ subscribe + post the AUTO-ACTIVATED card (visibility is mandatory — never activate silently). False → save draft + post the CONFIRM card and wait for the decision. - Decision grammar (same infrastructure as human-action cards):
policy <id> activate | edit <new text> | cancel— typed reply, reaction on the card, or (when custom-event fan-out lands) an A2UI button.editrecompiles with the new text into the SAME policy id. - Subscribe on activate: events client
subscribe(room_id, event_types)— the events plane's four-way authz is the permission evaluator; a subscribe rejection (e.g. PL too low) goes back to the user as the card's failure line, not swallowed. - Enforcement: active
notify/record/taskifypolicies are consumed by the sparrow drain handlers (taskify today; notify/record handlers are the next slice).cost_capshows the CAP on cards; metering is a later slice.
What ships with it
3 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.
- 4d ago First seen · 62 lines · 42 tokens per session scan A a21b1ba5aec5
observe is a skill published in the GitHub repository sonichi/sutando (391 stars, last pushed today), licensed MIT. It adds 42 tokens to every session and 835 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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