triage-rule-hits

name	triage-rule-hits
description	Rule hit triage methodology for Osprey rules. Samples recent hits, classifies each as TP/FP/novel/uncertain, and produces aggregate rule health assessment with actionable recommendations. Use when evaluating rule performance during Phase 5 (Rule Validation) or as a standalone rule maintenance check.
user-invocable	false

Triage Rule Hits

This skill guides the triage of Osprey rule hits — sampling recent hits for a given rule, classifying each as true positive, false positive, novel pattern, or uncertain, and producing an aggregate assessment of rule health. The goal is to surface what needs human attention: false positives that indicate rule drift, novel patterns worth adding to rule design, and overall rule performance.

Use this skill when you need to evaluate whether a rule is performing as intended, during Phase 5 (Rule Validation) of an investigation, or as a standalone rule maintenance workflow.

Input

The skill accepts:

• Rule name (required): The Osprey rule to triage (e.g., election_misinfo_en)
• Time window (optional): How far back to look. Default: 30 days.
• Sample size (optional): How many hits to classify. Default: 50.

Phase 1: Data Collection

1. Rule Hit Sample

Dispatch to data-analyst: "Sample [sample_size] rule hits for rule [rule_name] from osprey_execution_results over the past [time_window] days. Return: hit timestamp, author DID, content text, any other rules that also matched this content, and the rule's output score/value. Use a stratified sample — take hits evenly distributed across the time window rather than just the most recent."

Why stratified sampling: Recent hits may not represent the rule's full behaviour. A rule might work well on current content but have drifted over time, or vice versa.

2. Rule Context

Dispatch to data-analyst: "For rule [rule_name], show aggregate statistics over the past [time_window] days: total hit count, hits per day (average and range), unique authors hit, and a daily hit count trend (date, count)."

3. Hit Volume Check

If the rule hit sample returns zero results:

1. Report: "Rule [rule_name] produced no hits in the past [time_window] days."
2. Suggest: Check if the rule is still active, or expand the time window.
3. Do NOT proceed with classification — return a "no data" summary immediately.

If fewer than 10 hits are returned, proceed with classification but note the small sample size and its effect on confidence.

Phase 2: Classification

Classify each sampled hit. The classification determines whether the rule is catching what it should, what it shouldn't, or something unexpected.

Per-Hit Classification Schema

Field	Type	Description
classification	enum	true_positive, false_positive, novel, uncertain
confidence	enum	high, medium, low
reasoning	string	Brief explanation of why this classification was chosen
pattern_group	string	Group label for similar hits (e.g., "satire", "historical_reference", "new_tactic")

Classification Criteria

Classification	Criteria
true_positive	Content matches the rule's intended detection target. The rule was designed to catch this type of content and it did so correctly.
false_positive	Content does not match the rule's intended target. Common causes: satire or irony misread as sincere, historical references to past events, meta-discussion about the topic (e.g., discussing misinformation rather than spreading it), content in a language with keyword overlap.
novel	Content is genuinely problematic but uses a different pattern than the rule was designed for. The rule caught it via broad matching, but the specific tactic or framing is new. These are valuable signals for rule evolution.
uncertain	Insufficient context to classify confidently. The content is ambiguous, requires cultural context not available, or straddles the line between categories.

Pattern Grouping

As you classify hits, group similar ones under pattern labels. For example:

• FP hits from satire accounts -> pattern_group: "satire"
• FP hits from academic discussion -> pattern_group: "academic_meta"
• Novel hits using a new evasion tactic -> pattern_group: "unicode_substitution"
• TP hits matching the primary pattern -> pattern_group: "primary_detection"

Pattern groups make the aggregate output actionable — instead of "15 false positives," you get "8 FPs from satire, 4 from academic meta-discussion, 3 from historical references."

Phase 3: Output

Default: Triage Summary

## Rule Triage: [rule_name]

**Time Window:** [time_window] days
**Sample Size:** [N] hits classified (of [total] total hits)
**Rule Health:** [rule_health] ([confidence])

### Classification Summary

| Classification | Count | % of Sample |
|----------------|-------|-------------|
| True Positive | [n] | [%] |
| False Positive | [n] | [%] |
| Novel | [n] | [%] |
| Uncertain | [n] | [%] |

### False Positive Patterns

#### [pattern_group_1] ([n] hits)
**Example:** "[post content excerpt]"
**Reasoning:** [why this is a false positive]
**Suggested Action:** [adjust keyword exclusion / add context filter / no action if rare]

#### [pattern_group_2] ([n] hits)
...

### Novel Patterns

#### [pattern_group_1] ([n] hits)
**Description:** [concrete description of the new pattern]
**Example Posts:**
- "[post content 1]" ([DID], [date])
- "[post content 2]" ([DID], [date])
**Suggested Rule Action:** [add explicit detection / create new rule / expand existing pattern]

### Recommendation
[Actionable summary: what to do about FPs, novel patterns, and overall rule health]

Rule Health Assessment

Health	Criteria
healthy	TP rate >= 80%, no significant FP patterns, no novel patterns requiring attention
drifting	TP rate 60-79%, emerging FP patterns that are growing, or novel patterns appearing
needs_update	TP rate 40-59%, or significant FP patterns with clear fix available
needs_review	TP rate < 40%, or novel patterns that fundamentally change what the rule should detect

On Request: BLIND Report

When a full report is requested, load the reporting-results skill:

• Bottom Line: Rule health status + key finding (e.g., "Rule X is drifting due to emerging satire false positives")
• Impact: Hit volume, FP rate and its effect on moderation queue noise, novel patterns and their risk
• Next Steps: Specific rule modifications, new rules to create, hits to re-review
• Details: Full classification results with examples
• Timestamps: Triage time window, assessment timestamp

Select the rule check report type.