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 OKHP3/skillz --skill okhp3-decision-model-authoringgit clone --depth 1 https://github.com/OKHP3/skillzWrote 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/okhp3/skillz/okhp3-decision-model-authoring)<a href="https://agentmods.dev/skills/okhp3/skillz/okhp3-decision-model-authoring"><img src="https://agentmods.dev/badge/skills/okhp3/skillz/okhp3-decision-model-authoring/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/okhp3/skillz/okhp3-decision-model-authoring"><img src="https://agentmods.dev/badge/skills/okhp3/skillz/okhp3-decision-model-authoring.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 | $0.00106 | $0.01452 |
| Opus 5 | $0.00053 | $0.00726 |
| Sonnet 5 | $0.00021 | $0.00290 |
| Haiku 4.5 | $0.00011 | $0.00145 |
Grade A, and why
okhp3-decision-model-authoring 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 10d 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 — 154 lines — stays where its author put it; the contents beside it link to each section on GitHub.
okhp3-decision-model-authoring
OverKill Hill P³ · overkillhill.com · github.com/OKHP3
Purpose
Transform PNS decision_points into structured DMN-aligned decision models. Each decision point becomes a decision table with explicit input conditions, output values, and the business rule governing the routing logic.
When to use this skill
- PNS has ≥3 decision_points — this is the mandatory trigger condition
- User needs a decision table to communicate routing logic to implementers
- Business rules are complex enough that prose descriptions are insufficient
- Preparing a decision catalog as part of governance documentation
When NOT to use this skill
- PNS has fewer than 3 decision_points — inline the logic in the PNS
decision_points[]section - Decision logic is trivially binary (yes/no) with no business rule — document in
business_rules[]instead - Do not model decisions before the PNS is validated (score ≥ 75)
DMN Table Structure
Each decision table entry has:
| Field | Description |
|---|---|
decision_id |
Stable identifier matching pns.decision_points[].id |
decision_name |
Human-readable label |
activity_id |
The PNS activity where this decision occurs |
hit_policy |
U (Unique) | F (First) | A (Any) | C (Collect) |
inputs[] |
Each input: name, type (string|number|boolean), values[] |
outputs[] |
Each output: name, type, values[] |
rules[] |
Each rule: id, conditions{}, output{}, annotation |
Authoring Workflow
Step 1 — Extract decision points
Read pns.decision_points[] and group by the activity_id where they occur.
Step 2 — Identify inputs and outputs
For each decision:
- Inputs — the data values or conditions being evaluated (from
criteria) - Outputs — the possible routing outcomes (from
outcomes[].label)
Step 3 — Select hit policy
| Situation | Hit policy |
|---|---|
| Exactly one rule fires per input combination | U (Unique) |
| Rules are ordered; first match wins | F (First) |
| Multiple rules can fire but give the same output | A (Any) |
| Multiple rules can fire and outputs are aggregated | C (Collect) |
What ships with it
6 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.
- 10d ago First seen · 154 lines · 106 tokens per session scan A cb4172e525c2
okhp3-decision-model-authoring is a skill published in the GitHub repository OKHP3/skillz (3 stars, last pushed yesterday), licensed MIT. It adds 106 tokens to every session and 1,452 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 2026-08-31.
Other skills, from other repositories
vastai-sdk
Vast.ai Python SDK — high-level API for GPU instances, volumes, serverless endpoints, and billing.
pm-aarrr
A post-launch product-growth workflow based on AARRR: acquiring users, activating them, retaining them, earning revenue, and gaining referrals.
pm-docs
A workflow for producing product documents such as PRDs, BRDs, and MRDs. These are structured documents describing what a product needs, its business case, and its market.
product-marketing-copywriter
A marketing-copy tool that analyzes audience problems and product benefits, then creates promotional headlines and body text. Marketing copy is writing intended to explain and promote a product.
pm-position
A guided process for defining a product’s market position, value, audience, competitive difference, business model, pricing, and revenue plan. The instructions are written mainly in Chinese.
pm-retro
A retrospective workflow for reviewing a finished agile iteration, recording what happened, and choosing improvements for the next cycle.