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 swan-gtm/gtm-skills --skill inbox-zerogit clone --depth 1 https://github.com/swan-gtm/gtm-skillsWrote 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/swan-gtm/gtm-skills/inbox-zero)<a href="https://agentmods.dev/skills/swan-gtm/gtm-skills/inbox-zero"><img src="https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/inbox-zero/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/swan-gtm/gtm-skills/inbox-zero"><img src="https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/inbox-zero.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00046 | $0.01197 |
| Opus 5 | $0.00023 | $0.00598 |
| Sonnet 5 | $0.00009 | $0.00239 |
| Haiku 4.5 | $0.00005 | $0.00120 |
Grade A, and why
inbox-zero 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 9d 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 — 59 lines — stays where its author put it; the contents beside it link to each section on GitHub.
The four-bucket model
For each item the agent encounters, exactly one of these:
| Bucket | When | What the user sees |
|---|---|---|
| 1. Auto-archive | Obvious noise (newsletters, mass mailers, status alerts), items already handled by someone else, items needing no action | One-line FYI in the batched summary, always with a reason |
| 2. Auto-handle | The agent can fully complete the work itself — mark a CRM task done, schedule a follow-up reminder, archive after the other side already replied | One-line FYI in the batched summary |
| 3. Draft → approve | A reply is needed | Reviewed one by one in the batched output |
| 4. Park | Ambiguous, or the user's judgment is required | Left in inbox with a one-line reason |
Steps
-
Sweep all connected surfaces. Pull unread / unactioned items from email, Slack DMs, LinkedIn DMs, the Swan inbox, and the CRM task queue. Count them. If above ~30, switch to
swan-execute-code— load the inbox snapshot into a frame and process in passes. -
Classify silently into the four buckets. For each item:
- Read the message.
- Investigate first when facts are needed — check the CRM for prior touches and the account record, check the subscription, search product documentation, pull recent Slack threads where the customer was discussed. If the question is about a bug, product behavior, or code-level issue, route to the org's internal investigation/auditor agent when one is available (these typically have access to logs and the codebase; that context lands in the draft).
- Decide the bucket. When uncertain → park, don't auto-archive.
-
Execute autonomous actions. Archive bucket 1, complete bucket 2. Keep a running tally of WHY each was actioned, so the FYI summary is specific ("David replied he's on it"), not generic ("archived 14").
-
Draft replies for bucket 3. Substantive, in the sender's voice (load the sender instructions and pull past approved messages first), citing the facts gathered in Step 2. Use
swan-build-sequencewhen the reply belongs to an existing outreach thread; otherwise draft via the appropriate sending tool for the surface.
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.
- 9d ago First seen · 59 lines · 46 tokens per session scan A e185f30cb182
inbox-zero is a skill published in the GitHub repository swan-gtm/gtm-skills (153 stars, last pushed 2d ago), licensed MIT. It adds 46 tokens to every session and 1,197 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 2026-09-03.
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