event-template-mapping
Map visual detection data into structured event monitoring results.
Install
mkdir -p .claude/skills/event-template-mapping && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/16302" && unzip -o skill.zip -d .claude/skills/event-template-mapping && rm skill.zipInstalls to .claude/skills/event-template-mapping
Activation
This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.
Map LLM visual observations or method-level event results into final video monitoring Event Candidate semantics. Use when Codex has frame observations, visual timeline, template_name, template config, method_results, camera_id, score_policy, or review_threshold and needs event_type, confidence, severity, reason, evidence frames, and review flags.Key capabilities
- →Map LLM visual observations to event candidates
- →Translate method-level event results to event candidates
- →Generate a reason for the event from observations
- →Set confidence based on evidence quality and consistency
- →Flag events requiring review based on ambiguity or sparsity
How it works
The skill maps visual observations or method results to event candidates by applying rules based on the visual timeline and event template preconditions. It computes confidence and review status.
Inputs & outputs
When to use event-template-mapping
- →Map visual observations
- →Identify event candidates
- →Set event monitoring semantics
About this skill
Event Template Mapping
Execution Priority
Prioritize mapping visual observations into event candidates. Call scripts/run.py only when structured method_results already exist or the user explicitly asks for template-rule execution.
Input Resolution
If method_results are missing but frame observations are available, map the visual observations directly into event candidates. If both visual observations and structured method results are missing, first extract and review frames through analyze-video or frame-sampling, then call the relevant method-level event skill instructions. Do not invent final event semantics from the user request alone.
LLM Visual Mapping Rules
- Use the visual timeline as the primary evidence source.
- Select the event template whose preconditions are visibly satisfied.
- Generate
reasonfrom concrete observations: subjects, action/state, location, timestamps, and frame ids. - Set confidence from evidence quality, temporal consistency, and template fit.
- Set
requires_review=truewhen the event is serious but visually ambiguous, when frames are sparse, or when only aftermath is visible.
Atomic CLI
Run this skill directly with its own script. The script does not call other skill scripts and does not depend on shared src, tools, or registry modules.
python event-template-mapping/scripts/run.py --method-results-json <method_results.json> --template-name <template_name> --camera-id <camera_id> --config <config.json> --output <event.json>
Parameters: --method-results-json, --template-json, --template-name, --camera-id, --config, --output.
Workflow
- Read template name, join policy, score policy, visual observations, and method results.
- Check whether the visible evidence satisfies required event semantics.
- Compute event confidence and review status.
- Return a standard Event Candidate schema with evidence frame ids and visual reason.
Available Implementation
This skill is implemented as an atomic standalone script in its own scripts/run.py. The script contains the executable logic for this skill and must not import shared src, tools, or registry modules.
Inputs
template_nametemplatemethod_resultscamera_id
Outputs
Returns matched=true plus Event Candidate fields, or matched=false.
Failure Modes
Returns matched=false when join policy fails or score is below score_policy.min_score.
Constraints
Do not generate evidence files. Do not map an event type when the frame evidence does not visibly support it.
When not to use it
- →When the user wants to generate evidence files
- →When the frame evidence does not visibly support an event type
- →When `method_results` and frame observations are both missing without prior frame extraction
Limitations
- →It does not generate evidence files.
- →It cannot map an event type if frame evidence does not visibly support it.
- →It returns `matched=false` when join policy fails or score is below `score_policy.min_score`.
How it compares
This skill automates the translation of raw visual or method data into structured event candidates with confidence and review flags, rather than requiring manual interpretation and categorization of video monitoring inputs.
Compared to similar skills
event-template-mapping side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| event-template-mapping (this skill) | 0 | 2mo | Review | Intermediate |
| video-comparer | 1 | 9mo | Review | Intermediate |
| smartest-tv | 0 | 4mo | Review | Beginner |
| grafana-dashboards | 134 | 5mo | No flags | Intermediate |
Try saying
Example prompts that trigger this skill in your AI assistant.
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