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synaptiaisynaptiai/agent-capability-standard/skills/detect/SKILL.md

detect

Skill6 starsChanged 13 days agoApache-2.0View on GitHub

Determine whether a specific pattern, entity, or condition exists in the given data. Use when searching for patterns, checking existence, validating presence, or finding signals.

1 other project uses a version of this.

Adopted by

Most recently taken up by

The file

We split this skill into the rules it gives an agent

9 rules. About 0 are common, 7 are distinctive, and 2 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈7

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 2

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Scan target systematically: Search the target data for matching signals

    line 41
  2. Distinctive:

    Use Grep for text patterns with appropriate flags (-i for case-insensitive, etc.)

    line 42
  3. Distinctive:

    Use Read for structural inspection when pattern requires context

    line 43
  4. Distinctive:

    Verification tools: Read (to verify file:line references exist)

    line 171
  5. Distinctive:

    Do not access paths outside the specified target scope

    line 181
  6. Distinctive:

    Do not modify any files during detection

    line 182
  7. Distinctive:

    Stop and request clarification if detection criteria are ambiguous

    line 183
  8. Very specific:

    Never use detect for complex entity classification (use identify instead)

    line 198
  9. Very specific:

    Avoid detect for quantitative assessment (use estimate instead)

    line 199

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

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

ParameterRequiredTypeDescription
dataYesanyData to analyze
patternYesstring|objectThe pattern, entity type, or condition to detect
thresholdNonumberDetection threshold (e.g. minimum confidence for a match)

Inputs not listed here are outside the capability's contract: a workflow step that binds them gets W604 (STANDARD v1.1 §5.5).

Procedure

  1. 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
  2. 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
  3. 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
  4. 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
  5. Ground claims: Attach evidence anchors to all detection signals
    • Format: file:line for file-based targets
    • Include snippet of matched content for verification
  6. Format output: Structure results according to the output contract below

Output Contract

Return a structured object:

detected: boolean  # True if pattern/entity was found
target_type: string  # What was being detected (e.g., "security vulnerability", "deprecated API")
matches:
  - signal: string  # Description of what was found
    strength: low | medium | high  # Signal strength
    location: string  # file:line or path where found
locations: array[string]  # Every location where the pattern was found
false_positive_risk: low | medium | high  # Likelihood of spurious detection
confidence: number  # 0.0-1.0 based on evidence quality
evidence_anchors: array[string]  # File:line references
assumptions: array[string]  # Explicit assumptions made
next_actions: array[string]  # 0-3 suggested follow-ups if uncertain

Field Definitions

FieldTypeDescription
detectedbooleanTrue if the target pattern/entity was found
target_typestringCategory of what was searched for
matchesarray<object>Found occurrences: each detection signal with strength and location (empty when nothing found)
locationsarray[string]Every location where the pattern was found
false_positive_riskenumAssessment of detection reliability
confidencenumber0.0-1.0 based on evidence completeness
evidence_anchorsarray[string]File:line references or tool outputs
assumptionsarray[string]Explicitly stated assumptions

Examples

Example 1: Detect SQL Injection Vulnerability

Input:

data: src/api/user_controller.py
pattern: "SQL injection pattern"
threshold: 0.5  # report matches with at least this confidence

Output:

detected: true
target_type: "SQL injection vulnerability"
matches:
  - signal: "String concatenation in SQL query with user input"
    strength: high
    location: "src/api/user_controller.py:47"
  - signal: "Raw SQL execution without parameterization"
    strength: medium
    location: "src/api/user_controller.py:52"
locations:
  - "src/api/user_controller.py:47"
  - "src/api/user_controller.py:52"
false_positive_risk: low
confidence: 0.85
evidence_anchors:
  - "src/api/user_controller.py:47"
  - "src/api/user_controller.py:52"
assumptions:
  - "User input flows from request.params to query variable"
  - "No input sanitization in calling function"
next_actions:
  - "Verify data flow from user input to SQL query"
  - "Check for parameterized query alternatives in codebase"

Evidence pattern: Grep for SQL keywords combined with string formatting patterns, then Read to verify context.


Example 2: Detect Deprecated API Usage

Input:

data: "src/**/*.{jsx,tsx}"
pattern: "componentWillMount|componentWillReceiveProps"

Output:

detected: true
target_type: "deprecated React lifecycle method"
matches:
  - signal: "componentWillMount usage"
    strength: high
    location: "src/components/Dashboard.jsx:23"
locations:
  - "src/components/Dashboard.jsx:23"
false_positive_risk: low
confidence: 0.95
evidence_anchors:
  - "src/components/Dashboard.jsx:23"
assumptions:
  - "Project uses React 16.3+ where these methods are deprecated"
next_actions:
  - "Migrate to componentDidMount or useEffect hook"

Verification

  • Output contains detected boolean matching signal presence
  • At least one evidence anchor exists for positive detections
  • Confidence score correlates with number and strength of matches
  • All referenced file:line locations are valid and accessible
  • False positive risk assessment is justified

Verification tools: Read (to verify file:line references exist)

Safety Constraints

  • mutation: false
  • requires_checkpoint: false
  • requires_approval: false
  • risk: low

Capability-specific rules:

  • Do not access paths outside the specified target scope
  • Do not modify any files during detection
  • Stop and request clarification if detection criteria are ambiguous
  • Report uncertainty rather than guessing when signals are weak

Composition Patterns

Commonly follows:

  • inspect - Detection often follows initial observation of a system
  • search - Detection may refine broad search results

Commonly precedes:

  • identify - Positive detection often leads to classification of what was found
  • estimate-risk - Detection of anomalies feeds into risk assessment
  • plan - Detected issues may trigger remediation planning

Anti-patterns:

  • Never use detect for complex entity classification (use identify instead)
  • Avoid detect for quantitative assessment (use estimate instead)

Workflow references:

  • See reference/composition_patterns.md#risk-assessment for detection in risk workflows
  • See reference/composition_patterns.md#observe-model-act for detection in agentic loops

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