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/detectnpx skills add synaptiai/agent-capability-standard --skill detectgit 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/detect)<a href="https://agentmods.dev/skills/synaptiai/agent-capability-standard/detect"><img src="https://agentmods.dev/badge/skills/synaptiai/agent-capability-standard/detect.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 | $0.00034 | $0.01602 |
| Opus 5 | $0.00017 | $0.00801 |
| Sonnet 5 | $0.00007 | $0.00320 |
| Haiku 4.5 | $0.00003 | $0.00160 |
Grade A, and why
detect 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 3d 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 — 198 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Intent
Scan data sources to determine whether a specified pattern, entity, or condition is present. Detection is binary (present/absent) with associated signal strength.
Success criteria:
- Clear boolean determination of presence/absence
- At least one evidence anchor for positive detections
- False positive risk assessment provided
- Confidence score justified by evidence quality
Compatible schemas:
schemas/output_schema.yaml
Inputs
| Parameter | Required | Type | Description |
|---|---|---|---|
target |
Yes | string|object | The data source to scan (file path, URL, or structured data) |
pattern |
Yes | string|regex | The pattern, entity type, or condition to detect |
threshold |
No | object | Detection sensitivity settings (e.g., min_matches, confidence_floor) |
scope |
No | string | Limit search to specific regions (e.g., "functions", "imports", "comments") |
Procedure
-
Define detection criteria: Clarify exactly what constitutes a positive detection
- Convert vague patterns to concrete search terms or regex
- Establish minimum evidence threshold for positive detection
-
Scan target systematically: Search the target data for matching signals
- Use Grep for text patterns with appropriate flags (-i for case-insensitive, etc.)
- Use Read for structural inspection when pattern requires context
- Record location (file:line) for each potential match
-
Evaluate signal strength: For each match, assess how strongly it indicates true presence
- Strong: exact match with clear context
- Medium: partial match or ambiguous context
- Weak: possible match requiring human verification
-
Assess false positive risk: Determine likelihood that detections are spurious
- High risk: generic patterns, noisy data, few matches
- Low risk: specific patterns, clean data, multiple corroborating signals
-
Ground claims: Attach evidence anchors to all detection signals
- Format:
file:linefor file-based targets - Include snippet of matched content for verification
- Format:
What ships with it
2 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.
- 3d ago First seen · 198 lines · 34 tokens per session scan A 79e9ee741307
detect is a skill published in the GitHub repository synaptiai/agent-capability-standard (4 stars, last pushed 4d ago), licensed Apache-2.0. It adds 34 tokens to every session and 1,602 once invoked, about $0.0002 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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