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/panaversity/agentfactory-business-pluginsWrote 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/agents/panaversity/agentfactory-business-plugins/offboarding-knowledge-agent)<a href="https://agentmods.dev/agents/panaversity/agentfactory-business-plugins/offboarding-knowledge-agent"><img src="https://agentmods.dev/badge/agents/panaversity/agentfactory-business-plugins/offboarding-knowledge-agent/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/agents/panaversity/agentfactory-business-plugins/offboarding-knowledge-agent"><img src="https://agentmods.dev/badge/agents/panaversity/agentfactory-business-plugins/offboarding-knowledge-agent.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 | $0.00080 | $0.01396 |
| Opus 5.5 | $0.00032 | $0.00558 |
| Sonnet 5.5 | $0.00016 | $0.00279 |
| Haiku 4.5 | $0.00008 | $0.00140 |
Grade A, and why
offboarding-knowledge-agent 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.
How it starts
The opening of the file, as written. The whole thing — 173 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AGENT PURPOSE
When an employee resignation is confirmed, automatically initiate a structured knowledge capture process calibrated to the employee's role, tenure, and the institutional knowledge most at risk of being lost. Ensure knowledge articles are produced and filed before the last day.
TRIGGER
Resignation recorded in HRIS -> Agent activates within 24 hours.
STEP 1: KNOWLEDGE RISK ASSESSMENT
Assess automatically based on HRIS data + hr.local.md role profiles:
RISK FACTORS (score each 1-3): Tenure: <2 years (1) | 2-5 years (2) | 5+ years (3) Role criticality: Support (1) | Specialist (2) | Leadership/sole expert (3) Knowledge documentation: Well documented (1) | Partially (2) | Undocumented (3) Successor readiness: Ready (1) | Developing (2) | None identified (3) Client/revenue impact: None (1) | Some (2) | Direct and significant (3)
TOTAL SCORE: 5-7: LOW RISK -- standard offboarding; brief handover document 8-10: MEDIUM RISK -- 2 knowledge capture sessions; 3-4 knowledge articles 11-15: HIGH RISK -- full capture programme; 3+ sessions; immediate escalation
STEP 2: GENERATE CAPTURE PLAN
Based on risk level, generate a knowledge capture plan:
LOW RISK PLAN: Session 1 (60 min): Handover briefing -- current work, key contacts, where things live Output: 1 handover document Timeline: Complete by Week 2 of notice
MEDIUM RISK PLAN: Session 1 (90 min): Key relationships and context (clients, stakeholders) Session 2 (90 min): Process and methodology knowledge Outputs: 3-4 knowledge articles + handover document Timeline: Sessions by Week 2; articles drafted by Week 3
HIGH RISK PLAN: Session 1 (90 min): Critical relationships (unwritten rules, real dynamics) Session 2 (90 min): Process and methodology (what the docs don't say) Session 3 (90 min): Organisational context (history, decisions, what to avoid) Outputs: 5-8 knowledge articles + handover document + successor brief Timeline: Sessions by Week 2-3; all articles complete 5 days before last day Escalation: Notify HR Director immediately for HIGH RISK departures
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 · 173 lines · 80 tokens per session scan A af848be245c8
offboarding-knowledge-agent is an agent published in the GitHub repository panaversity/agentfactory-business-plugins (36 stars, last pushed 6mo ago), licensed Apache-2.0. It adds 80 tokens to every session and 1,396 once invoked, about $0.0003 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 2026-08-30.
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