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/tayontech/scope/scope-detection-formatnpx skills add tayontech/SCOPE --skill scope-detection-formatgit clone --depth 1 https://github.com/tayontech/SCOPEWhat 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 | $0.00033 | $0.01208 |
| Opus 5 | $0.00016 | $0.00604 |
| Sonnet 5 | $0.00007 | $0.00242 |
| Haiku 4.5 | $0.00003 | $0.00121 |
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.
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_BLOCKSwith the missing fields and affected detection names.
Inputs
Accept a caller-prepared detection list. Each detection must include these required fields:
nametypeobjectivesplseveritycategorymitre_techniquesource_attack_pathssource_public_exposure_findingssource_run_idspromotion_decisionfidelity_rationalenoise_controlsexpected_volumevalidation_status
Optional fields:
covered_hopscoverage_caveatstuning_guidance
Normalization
Only apply mechanical formatting:
- Keep severity lowercase:
critical,high,medium,low. - Keep
typeasatomic,composite,hunt_query, orcoverage_gap. - Keep
promotion_decisionasalert,hunt_query,coverage_gap, orreject. - Keep
expected_volumeaslow,medium,high, orunknown. - Keep
validation_statusasnot_validated,validated,too_noisy, orfailed. - SPL in
detections.jsonmust be a single-line string with literal newlines replaced by spaces. - Markdown SPL blocks must use fenced
splcode 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."}
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.
- 2d ago First seen · 156 lines · 33 tokens per session scan A f3c8fbbf2459
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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