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 cynco-labs/ai-accounting-skills --skill engagement-setupgit clone --depth 1 https://github.com/cynco-labs/ai-accounting-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/cynco-labs/ai-accounting-skills/engagement-setup)<a href="https://agentmods.dev/skills/cynco-labs/ai-accounting-skills/engagement-setup"><img src="https://agentmods.dev/badge/skills/cynco-labs/ai-accounting-skills/engagement-setup/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/cynco-labs/ai-accounting-skills/engagement-setup"><img src="https://agentmods.dev/badge/skills/cynco-labs/ai-accounting-skills/engagement-setup.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 | $0.00025 | $0.00808 |
| Opus 5.5 | $0.00010 | $0.00323 |
| Sonnet 5.5 | $0.00005 | $0.00162 |
| Haiku 4.5 | $0.00003 | $0.00081 |
Grade A, and why
engagement-setup 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 2mo 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 — 115 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/engagement-setup
Purpose
Lock in engagement context after (or with) document-first discovery.
Preconditions
shared/guardrails.mdshared/operator-lens.md- Firm profile if present (defaults only)
- If folder dump / unknown entity → run
smart-intakefirst, then return here to formalize
Route
| Situation | Action |
|---|---|
| User gave legal name + period + clear docs | Setup directly (below) |
| User: “accounting for this folder” / no name | smart-intake first |
engagement_state.json exists |
resume-engagement |
Workflow
1. Scan client folder
Banks, invoices, payslips, prior FS, SSM, tax files. Prefer facts from files over questions.
2. Entity identification
From SSM / bank account title / prior FS / smart-intake inferences:
- Legal name, registration number (only if on a doc — never invent)
- Entity type
- Address / activities if present
Load references/entity_types.md or jurisdiction pack.
3. Framework selection (derive, don’t quiz)
| Entity | Framework | Tax form |
|---|---|---|
| Berhad (public) | MFRS | Form C |
| Sdn Bhd | MPERS | Form C |
| PLT | MPERS | Form PT |
| Sole prop | Accrual S21A | Form B |
| Partnership | Accrual S21A | Form P |
| Koperasi | MCA | Form TF |
| Trust | Varies | Form TP |
Only ask if entity type is still ambiguous after docs.
4. Financial year
Prefer statement date coverage + prior FS. Soft-confirm if incomplete year.
5. Completeness (honest, not scary)
Blockers for full-year final FS:
- Full-year bank statements (or accepted limited period)
- Clear reporting entity identity
Not first-turn questions — note as gaps:
- Payslips, FAR, inventory, loans, prior FS
6. COA
Entity template + optional industry overlay from payee patterns (trading vs services). Soft default; user rarely needs to choose.
7. Operator + depth on state
Set operator and engagement_type (shared/operator-lens.md):
- Infer from language + firm profile, or one soft-ask if missing
- Default depth for thin setup:
bookkeeping_onlyunless user asked year-end / tax
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.
- 2mo ago First seen · 115 lines · 25 tokens per session scan A 7b8a9e01e5d6
engagement-setup is a skill published in the GitHub repository cynco-labs/ai-accounting-skills (4 stars, last pushed 3mo ago), licensed MIT. It adds 25 tokens to every session and 808 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.
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