scope-detection-format

A formatting tool for turning already-made security detection decisions into standard Markdown, JSON, and dashboard records. A detection is a rule that looks for suspicious activity in logs or other data.

In plain words
What is it for?
Use it to create detections.md, detections.json, dashboard-ready Splunk sections, and control-schema records from a prepared detection list.
Why use it?
It keeps detection records consistent and machine-readable without changing the caller's detection logic or deciding whether a rule should be used. If required information is missing, it reports what prevents formatting.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/tayontech/scope/scope-detection-format
Any agent
npx skills add tayontech/SCOPE --skill scope-detection-format
Clone the repo
git clone --depth 1 https://github.com/tayontech/SCOPE

Made for: Claude Code, Codex.

Per session 33 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,208 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00033 $0.01208
Opus 5 $0.00016 $0.00604
Sonnet 5 $0.00007 $0.00242
Haiku 4.5 $0.00003 $0.00121

Measured 2d ago against content hash f3c8fbbf2459, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

scope-detection-format 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 2d 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.

skills/scope-detection-format/SKILL.md · 156 lines

How it starts

The opening of the file, as written. The whole thing — 156 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Scope Detection Format

This skill is format-only. It turns caller-provided detection decisions into the two controls artifacts: detections.md for humans and detections.json for the orchestrator, validator, and dashboard.

Boundary

The caller owns detection quality.

  • Do not decide alert vs hunt.
  • Do not promote, reject, or downgrade detections.
  • Do not change SPL logic.
  • Do not invent required values.
  • Do not add attack paths, event names, MITRE IDs, exclusions, noise controls, validation status, volume, or fidelity rationale that the caller did not provide.
  • If a required field is missing, return FORMAT_BLOCKS with the missing fields and affected detection names.

Inputs

Accept a caller-prepared detection list. Each detection must include these required fields:

  • name
  • type
  • objective
  • spl
  • severity
  • category
  • mitre_technique
  • source_attack_paths
  • source_public_exposure_findings
  • source_run_ids
  • promotion_decision
  • fidelity_rationale
  • noise_controls
  • expected_volume
  • validation_status

Optional fields:

  • covered_hops
  • coverage_caveats
  • tuning_guidance

Normalization

Only apply mechanical formatting:

  • Keep severity lowercase: critical, high, medium, low.
  • Keep type as atomic, composite, hunt_query, or coverage_gap.
  • Keep promotion_decision as alert, hunt_query, coverage_gap, or reject.
  • Keep expected_volume as low, medium, high, or unknown.
  • Keep validation_status as not_validated, validated, too_noisy, or failed.
  • SPL in detections.json must be a single-line string with literal newlines replaced by spaces.
  • Markdown SPL blocks must use fenced spl code blocks.
  • Dashboard-readable text must be concise and avoid long inline SPL in prose.

If a value cannot be normalized mechanically, return FORMAT_BLOCKS.

detections.md

Write sections in this order:

# SPL Detections

Generated from: {AUDIT_RUN_DIR}
Account: {ACCOUNT_ID}
Attack paths analyzed: {N}
Detections generated: {N}

---

## Attack Path: {attack_path_name}

**Severity:** {severity}
**Category:** {category}
**Validation Status:** {validated|conditional}
**Runtime Assumptions:** {runtime_assumptions[] or "none"}
**Coverage Caveats:** {coverage_caveats[] or "none"}
**MITRE:** {technique_ids}

### Detection: {detection_name}

- **MITRE:** {mitre_technique}
- **Severity:** {severity}
- **Type:** {atomic|composite|hunt_query|coverage_gap}
- **Promotion:** {alert|hunt_query|coverage_gap|reject}
- **Expected Volume:** {low|medium|high|unknown}
- **Fidelity Rationale:** {fidelity_rationale}
- **Noise Controls:** {noise_controls[]}
- **Related Attack Paths:** {source_attack_paths[]}
- **Description:** {objective}

```spl
{spl as readable multiline query}

False Positives: {false_positive_guidance if caller provided it, otherwise "Not provided."} Tuning Guidance: {tuning_guidance if caller provided it, otherwise "Not provided."}


Read the full file on GitHub · 156 lines

Changes

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.

  1. 2d ago First seen · 156 lines · 33 tokens per session scan A f3c8fbbf2459

Subscribe to this mod's changes

scope-detection-format is a skill published in the GitHub repository tayontech/SCOPE (54 stars, last pushed 2mo ago), licensed MIT. It adds 33 tokens to every session and 1,208 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 2026-08-30.

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