Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/phuoctrung-ppt/ai-sdlc-workflownpx agentmods add rules/phuoctrung-ppt/ai-sdlc-workflow/008-office-ui-eventsWrote 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/rules/phuoctrung-ppt/ai-sdlc-workflow/008-office-ui-events)<a href="https://agentmods.dev/rules/phuoctrung-ppt/ai-sdlc-workflow/008-office-ui-events"><img src="https://agentmods.dev/badge/rules/phuoctrung-ppt/ai-sdlc-workflow/008-office-ui-events/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/rules/phuoctrung-ppt/ai-sdlc-workflow/008-office-ui-events"><img src="https://agentmods.dev/badge/rules/phuoctrung-ppt/ai-sdlc-workflow/008-office-ui-events.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 | $0.00391 | $0.00391 |
| Opus 5 | $0.00196 | $0.00196 |
| Sonnet 5 | $0.00078 | $0.00078 |
| Haiku 4.5 | $0.00039 | $0.00039 |
Grade A, and why
008-office-ui-events 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 8d 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
Office UI events
If .aisdlc/ exists, emit on every agent turn start/end.
python3 .cursor/scripts/office-event.py \
--agent <agent-id> \
--status working|waiting|done|error|idle \
--task "<short task>" \
--phase <phase> \
--workflow <module-or-workflow-id> \
[--tokens N] [--tokens-in N] [--tokens-out N]
Token audit (required on done when known)
When finishing a turn / phase, if you can estimate or read usage:
python3 .cursor/scripts/office-event.py \
--agent backend-worker \
--status done \
--task "API done" \
--phase implement-backend \
--workflow {feature_name} \
--tokens 25000 \
--tokens-in 19000 \
--tokens-out 6000
--workflowgroups costs for the TOKEN AUDIT panel (per module).- Tokens accumulate in
.aisdlc/state.jsonand.aisdlc/benchmarks.json. - Prefer real usage meters when the host exposes them; otherwise estimate and label in
--detailasest.
Status
| Status | When |
|---|---|
working |
Start task |
waiting |
Blocked |
done |
Success (+ tokens when possible) |
error |
Failed |
idle |
Clear desk |
UI: ai-sdlc ui → http://127.0.0.1:9669 — click a desk to inspect task + tokens; LIVE BOARD updates in place per agent.
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.
- 8d ago First seen · 52 lines · 391 tokens per session scan A 17d2c4a585c5
008-office-ui-events is a cursor rule published in the GitHub repository phuoctrung-ppt/ai-sdlc-workflow (2 stars, last pushed 2d ago), licensed MIT. It adds 391 tokens to every session, about $0.0020 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 2026-08-31.
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