QG-01 is a portable, model-agnostic behavioral skill that adds the minimum sufficient quantitative structure needed to understand scale, comparison, likelihood, economics, uncertainty, and decision relevance without numerical theater or false precision.
Quantify when numbers reveal reality. Do not quantify when numbers merely imitate certainty.
AI answers can be qualitatively correct yet quantitatively ungrounded. They may describe something as large, likely, expensive, dominant, fast-growing, or important without establishing magnitude, baseline, time horizon, uncertainty, or economic significance.
QG-01 turns the recurring question "compared with what?" into an explicit reasoning protocol.
Copy the contents of SYSTEM_PROMPT.txt into the persistent instruction layer of your model or agent.
Provide SKILL.md to the agent as a behavioral skill and activate it with:
Activate QG-01 by default for substantive analytical questions.
For a single request:
Run QG-01 on this question.
Depth modes:
QG-01/Lite
QG-01/Standard
QG-01/Deep
Install the Codex Skills marketplace, then install this immutable, validated package:
codex plugin marketplace add https://github.com/andydrewie/codex-skills
codex plugin add quantitative-grounding@andydrewie-codex-skillsThe root plugin.json follows Agent Plugins 1.0. The generated .codex-plugin/plugin.json keeps Codex 0.145 compatible while the root manifest is used by Codex 0.146 and newer.
A number should be included only when it is:
- Relevant: it materially changes interpretation.
- Decision-useful: it improves comparison, prioritization, forecasting, or action.
- Epistemically defensible: it comes from reliable evidence, transparent calculation, or clearly stated assumptions.
When reliable quantification is unavailable, remaining qualitative is the correct behavior.
| File | Purpose |
|---|---|
SKILL.md |
Normative behavioral specification and source of truth |
agents/openai.yaml |
Codex UI metadata and default invocation prompt |
plugin.json |
Portable Agent Plugins 1.0 manifest |
.codex-plugin/plugin.json |
Generated Codex 0.145 compatibility manifest |
skills/quantitative-grounding/ |
Generated fixed-location plugin skill mirror |
SYSTEM_PROMPT.txt |
Compact deployment prompt |
AGENTS.md |
Agent-facing integration and precedence rules |
skill.json |
Machine-readable manifest |
EVALS.md |
Behavioral tests and pass/fail criteria |
schemas/quantitative-frame.schema.json |
Optional structured-output schema |
examples/ |
Human and machine-readable usage examples |
scripts/validate.py |
Dependency-free repository validator |
CANONICAL_SUMMARY.md |
Human-readable formalization |
CHANGELOG.md |
Version history |
If files conflict, SKILL.md controls.
An agent implementing QG-01 should:
- answer the actual question first;
- add one to five high-value quantitative anchors when applicable;
- state the baseline, denominator, period, or reference class when material;
- distinguish fact, reported data, calculation, estimate, scenario, and speculation when provenance matters;
- distinguish event probability from confidence in the estimate;
- prefer ranges and scenarios when inputs do not justify a point estimate;
- omit quantitative framing when it would create false precision.
See AGENTS.md for the complete agent-facing contract.
Run locally:
python scripts/validate.pyCheck Codex skill-structure compatibility with the installed official validator:
python3 ~/.codex/skills/.system/skill-creator/scripts/quick_validate.py skills/quantitative-groundingThe GitHub Actions workflow runs the same validation on every push and pull request.
Canonical skill version: 1.0.3
This packaging-only release adds portable Agent Plugins discovery and the transitional Codex compatibility bridge without changing QG-01 behavior.
Created by Andrew Fai (@andydrewie) through iterative human-AI specification design.
MIT. See LICENSE.