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 rjmurillo/ai-agents --skill work-operating-modelgit clone --depth 1 https://github.com/rjmurillo/ai-agentsWrote 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/rjmurillo/ai-agents/work-operating-model)<a href="https://agentmods.dev/skills/rjmurillo/ai-agents/work-operating-model"><img src="https://agentmods.dev/badge/skills/rjmurillo/ai-agents/work-operating-model/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/rjmurillo/ai-agents/work-operating-model"><img src="https://agentmods.dev/badge/skills/rjmurillo/ai-agents/work-operating-model.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 | $0.00102 | $0.01776 |
| Opus 5 | $0.00051 | $0.00888 |
| Sonnet 5 | $0.00020 | $0.00355 |
| Haiku 4.5 | $0.00010 | $0.00178 |
Grade A, and why
work-operating-model 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 — 131 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Work Operating Model
A conversation-first elicitation skill. Surface what is documented, what is tacit, and where the two disagree. Output is a structured operating model that downstream agents (architect, roadmap, orchestrator) can read.
Triggers
| Trigger Phrase | Operation |
|---|---|
elicit operating model |
Start the 5-layer interview |
interview team operating model |
Start the 5-layer interview |
how does this team actually work |
Start the 5-layer interview |
resume operating model interview |
Continue from the first layer not yet captured or skipped |
validate operating model |
Run scripts/validate_operating_model.py against an output JSON |
When to Use
Use this skill when:
- You need a baseline of team practice before an ADR, retrospective, or scope decision.
- A new agent or skill needs to be customized to a team's actual cadences and decision rights.
- A planning conversation keeps getting derailed by missing context about how the team operates.
Do not use this skill when:
- The question is about code, not people-process. Use the
analystagent. - You need a strategic narrative for an external audience. Use the
explaineragent. - A retrospective is what you actually need. Use the
retrospectiveagent.
Distinction From Adjacent Capabilities
| Capability | Investigates | Output |
|---|---|---|
analyst agent |
Code, repo state, bug paths | Findings document |
explainer agent |
Concepts, decisions for a reader | Explainer prose |
retrospective agent |
A bounded past period | Learning matrix |
| work-operating-model | Team operating reality (people-process) | Structured operating model JSON |
The 5 Layers
The interview proceeds in order. Each layer answers one question and produces one section of the output JSON. Skip a layer only when the team explicitly cannot answer it; record the gap in metadata.skipped_layers.
| # | Layer | Question | Output Section |
|---|---|---|---|
| 1 | Rhythms | When does work happen, and on what cadence? | rhythms |
| 2 | Decisions | Who decides what, and how is the decision recorded? | decisions |
| 3 | Dependencies | Who do you wait on, and who waits on you? | dependencies |
| 4 | Institutional Knowledge | What lives in someone's head and not in a doc? | institutional_knowledge |
| 5 | Friction | What is broken or slow that the team has accepted? | friction |
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 · 131 lines · 102 tokens per session scan A 5aca273423b5
work-operating-model is a skill published in the GitHub repository rjmurillo/ai-agents (45 stars, last pushed yesterday), licensed MIT. It adds 102 tokens to every session and 1,776 once invoked, about $0.0005 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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