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 agentmods add skills/synaptiai/agent-capability-standard/measurenpx skills add synaptiai/agent-capability-standard --skill measuregit clone --depth 1 https://github.com/synaptiai/agent-capability-standardWrote 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/synaptiai/agent-capability-standard/measure)<a href="https://agentmods.dev/skills/synaptiai/agent-capability-standard/measure"><img src="https://agentmods.dev/badge/skills/synaptiai/agent-capability-standard/measure.svg" alt="Measured on agentmods" 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 | $0.00028 | $0.01490 |
| Opus 5 | $0.00014 | $0.00745 |
| Sonnet 5 | $0.00006 | $0.00298 |
| Haiku 4.5 | $0.00003 | $0.00149 |
Grade A, and why
measure 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 6d 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 — 210 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Intent
Quantify a specific metric for a target, providing a numerical value with explicit uncertainty bounds. This capability consolidates all estimation tasks (risk, impact, effort, etc.) into a single parameterized operation.
Success criteria:
- Numerical value provided for requested metric
- Uncertainty bounds explicitly stated
- Measurement method documented
- Units clearly specified
Compatible schemas:
schemas/output_schema.yaml
Inputs
| Parameter | Required | Type | Description |
|---|---|---|---|
target |
Yes | any | What to measure (system, code, entity, process) |
metric |
Yes | string | The metric to quantify (risk, complexity, effort, size, etc.) |
unit |
No | string | Unit of measurement (optional, inferred if not provided) |
method |
No | string | Measurement approach (heuristic, statistical, model-based) |
Procedure
-
Define the metric: Clarify exactly what is being measured
- Establish clear definition of the metric
- Identify appropriate unit of measurement
- Determine measurement methodology
-
Gather measurement inputs: Collect data needed for measurement
- Read relevant files, logs, or data sources
- Identify quantifiable indicators
- Note data quality and completeness
-
Calculate measurement: Apply measurement method to inputs
- Use appropriate calculation for the metric type
- For risk: probability * impact assessment
- For complexity: cyclomatic, cognitive, or structural metrics
- For effort: decomposition and estimation techniques
-
Establish uncertainty bounds: Quantify measurement confidence
- Calculate or estimate lower and upper bounds
- Consider data quality, method limitations
- Express as confidence interval or range
-
Ground the measurement: Document evidence and method
- Reference specific data points used
- Note any assumptions in calculation
- Document measurement methodology
Output Contract
What ships with it
5 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.
- 6d ago First seen · 210 lines · 28 tokens per session scan A 03c1ada5068e
measure is a skill published in the GitHub repository synaptiai/agent-capability-standard (4 stars, last pushed 7d ago), licensed Apache-2.0. It adds 28 tokens to every session and 1,490 once invoked, about $0.0001 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.
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