agentleFS
synaptiaisynaptiai/agent-capability-standard/skills/capability-gap-analysis/SKILL.md

gap-analysis-workflow

Skill6 starsChanged 13 days agoApache-2.0View on GitHub

Identify capability gaps and propose new skills with prioritization. Use when analyzing missing capabilities, planning skill development, performing ontology expansion, or assessing coverage.

The file

Show its rules and text

We split this skill into the rules it gives an agent

13 rules. About 0 are common, 11 are distinctive, and 2 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈11

    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. Very specific:

    Run the composed workflow capability_gap_analysis (reference/workflow_catalog.yaml) using atomic capability skills to systematically …

    line 14
  2. Distinctive:

    Every step output is grounded with evidence anchors

    line 20
  3. Distinctive:

    Create checkpoint marker: if mutation might occur:

    line 35
  4. Very specific:

    Create .claude/checkpoint.ok after confirming rollback strategy

    line 36
  5. Distinctive:

    Read project docs and current skill coverage

    line 39
  6. Distinctive:

    Verification tools: Read (for ontology files), Grep (for capability search), Bash (for validation)

    line 255
  7. Distinctive:

    Do not create new skills during analysis (discovery only)

    line 265
  8. Distinctive:

    Prefer a domain parameter on an existing capability over a new capability

    line 267
  9. Distinctive:

    Document assumptions about missing capabilities

    line 268
  10. Distinctive:

    Never skip relationship mapping before prioritization

    line 284
  11. Distinctive:

    Never propose skills without checking ontology for existing alternatives

    line 285
  12. Distinctive:

    Never prioritize without defined criteria

    line 286
  13. Distinctive:

    Never generate roadmap without dependency analysis

    line 287

≈ “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

Run the composed workflow capability_gap_analysis (reference/workflow_catalog.yaml) using atomic capability skills to systematically identify what capabilities are missing and prioritize their development.

Success criteria:

  • Missing capability list
  • Prioritized backlog
  • Proposed skill specs
  • Every step output is grounded with evidence anchors
  • Audit trail of analysis process

Compatible schemas:

  • reference/capability_ontology.yaml
  • reference/workflow_catalog.yaml

Inputs

ParameterRequiredTypeDescription
user_inputNoobjectdomain (the project/system to assess; step 1 observes it, so set it in practice). The catalog also accepts constraints, but no step binds it: the priority compare always ranks by the fixed criteria leverage and risk_reduction

Procedure

  1. Create checkpoint marker if mutation might occur:
    • Create .claude/checkpoint.ok after confirming rollback strategy
  2. Invoke /observe on user_input.domain and store output as observe_out
    • Read project docs and current skill coverage
  3. Invoke /attribute (domain: dependencies) with observe_out.observation as the effect and store output as attribute_out
    • Map existing workflows vs capabilities
  4. Invoke /discover on attribute_out.causes with method gap_analysis, and store output as discover_out
    • Find capability gaps, hidden dependencies and unmodeled interactions: each entry of discover_out.patterns has type gap, pattern or opportunity
  5. Invoke /compare (domain: architecture) on discover_out.patterns and store output as compare_architecture_out
    • Compare alternative skill architectures
  6. Invoke /compare (domain: priority) on the discovered patterns (discover_out.patterns) with criteria leverage and risk_reduction, and store output as compare_priority_out
    • Rank every discovered pattern (of type gap, pattern or opportunity) by leverage and risk reduction; the step does not filter by type
  7. Invoke /generate (domain: plan) from compare_priority_out.ranking and compare_architecture_out.recommendation, and store output as generate_out
    • Generate an implementation plan for new skills
  8. Invoke /audit and store output as audit_out
    • Record rationale + decision lineage: the ranking and the plan

Output Contract

Return a structured object:

workflow_id: string  # Unique analysis execution ID
user_input: object  # The input as given
status: completed | partial | failed
steps:
  observe_out:
    observation: object
    evidence_anchors: array[string]
  attribute_out:
    causes: array[object]
    strengths: object  # Causal strength per cause
    evidence_anchors: array[string]
  discover_out:
    patterns: array[object]
    relationships: array[object]
    evidence_anchors: array[string]
  compare_architecture_out:
    ranking: array[object]
    recommendation: string  # The top choice
    evidence_anchors: array[string]
  compare_priority_out:
    ranking: array[object]
    evidence_anchors: array[string]
  generate_out:
    content: object  # The implementation plan
    evidence_anchors: array[string]
  audit_out:
    recorded: boolean
    audit_id: string
    evidence_anchors: array[string]
gaps_identified:
  total: integer
  by_priority:
    critical: array[string]
    high: array[string]
    medium: array[string]
    low: array[string]
  evidence_anchors: array[string]
audit_log_pointer: string  # Path to the audit record
confidence: number  # 0.0-1.0
evidence_anchors: array[string]
assumptions: array[string]

Field Definitions

FieldTypeDescription
workflow_idstringUnique identifier for this analysis
statusenumFinal workflow status
stepsobjectEach step's output, keyed by its store_as
gaps_identifiedobjectMissing capabilities organized by priority: the entries of compare_priority_out.ranking that are gap patterns
audit_log_pointerstringWhere the audit record was written
confidencenumber0.0-1.0 based on evidence completeness
evidence_anchorsarrayAll evidence references collected
assumptionsarrayExplicit assumptions made during analysis

Examples

Example 1: Gap Analysis for Autonomous Code Review

Input:

user_input:
  domain: "autonomous code review"

Output:

workflow_id: "gap_20240115_120000_codereview"
user_input:
  domain: "autonomous code review"
status: completed
steps:
  observe_out:
    observation:
      capabilities: 36
      workflows: 12
      review_related_skills: ["critique", "verify", "detect"]
    evidence_anchors:
      - "file:schemas/capability_ontology.yaml"
      - "file:skills/critique/SKILL.md"
  attribute_out:
    causes:
      - id: no_merge_gate
        cause: "No workflow composes critique with an automated merge gate"
      - id: no_regression_domain
        cause: "verify has no regression domain"
    strengths:
      no_merge_gate: 0.8
      no_regression_domain: 0.7
    evidence_anchors:
      - "file:schemas/workflow_catalog.yaml"
  discover_out:
    patterns:
      - type: gap
        description: "verify has no regression domain: review loops repeat critique → plan without verifying"
      - type: gap
        description: "Security findings are detected but never classified (classify has no security_pattern domain)"
      - type: gap
        description: "No detect domain for code smells"
      - type: gap
        description: "No constrain domain gates an automated merge"
      - type: gap
        description: "generate has no review_comment domain"
    relationships:
      - from: "detect"
        to: "critique"
        note: "Code smells inform critique priorities"
    evidence_anchors:
      - "file:schemas/capability_ontology.yaml:edges"
  compare_architecture_out:
    ranking:
      - option: "Domain profiles on existing capabilities"
        score: 0.85
      - option: "New atomic capabilities"
        score: 0.4
    recommendation: "Domain profiles on existing capabilities"
    evidence_anchors:
      - "file:schemas/profiles/profile_schema.yaml"
  compare_priority_out:
    ranking:
      - gap: "verify (domain: regression)"
        priority: critical
      - gap: "classify (domain: security_pattern)"
        priority: critical
      - gap: "detect (domain: code_smell)"
        priority: high
      - gap: "constrain (domain: auto_merge)"
        priority: high
      - gap: "generate (domain: review_comment)"
        priority: medium
    evidence_anchors:
      - "file:schemas/capability_ontology.yaml:nodes"
  generate_out:
    content:
      phases:
        - phase: 1
          name: "Security Foundation"
          items: ["classify (domain: security_pattern)", "verify (domain: regression)"]
        - phase: 2
          name: "Detection Enhancement"
          items: ["detect (domain: code_smell)", "constrain (domain: auto_merge)"]
        - phase: 3
          name: "Automation Expansion"
          items: ["generate (domain: review_comment)"]
      estimated_effort: "2-3 sprints"
    evidence_anchors:
      - "file:templates/SKILL_TEMPLATE_ENHANCED.md"
  audit_out:
    recorded: true
    audit_id: "audit_20240115_121500"
    evidence_anchors:
      - "file:.claude/audit/records.jsonl"
gaps_identified:
  total: 5
  by_priority:
    critical:
      - "verify (domain: regression)"
      - "classify (domain: security_pattern)"
    high:
      - "detect (domain: code_smell)"
      - "constrain (domain: auto_merge)"
    medium:
      - "generate (domain: review_comment)"
    low: []
  evidence_anchors:
    - "file:schemas/capability_ontology.yaml:nodes"
audit_log_pointer: ".claude/audit/records.jsonl"
confidence: 0.85
evidence_anchors:
  - "file:schemas/capability_ontology.yaml"
  - "file:schemas/workflow_catalog.yaml"
  - "file:.claude/audit/records.jsonl"
assumptions:
  - "Reference ontology is current and complete"
  - "Existing skills are correctly implemented"
  - "Leverage and risk reduction reflect actual business needs"

Evidence pattern: Ontology comparison, dependency graph analysis, impact-based prioritization.

Verification

  • Step Completeness: All 7 workflow steps executed with output
  • Evidence Grounding: Every step output includes evidence_anchors
  • Relationships Mapped: attribute_out and discover_out document dependencies and missing connections
  • Architecture Compared: compare_architecture_out has a recommendation
  • Gaps Prioritized: compare_priority_out ranks every discovered pattern, gaps included
  • Plan Generated: generate_out.content is a phased plan
  • Audit Trail: audit_out.recorded is true

Verification tools: Read (for ontology files), Grep (for capability search), Bash (for validation)

Safety Constraints

  • mutation: false
  • requires_checkpoint: false
  • requires_approval: false
  • risk: low (the catalog declares low; max_step_risk computes medium from the audit step)

Capability-specific rules:

  • Do not create new skills during analysis (discovery only)
  • Validate all capability references against ontology
  • Prefer a domain parameter on an existing capability over a new capability
  • Document assumptions about missing capabilities
  • Flag potential security implications of gaps
  • Preserve existing ontology structure

Composition Patterns

Commonly follows:

  • observe - After initial codebase exploration
  • retrieve - After fetching reference documentation

Commonly precedes:

  • generate (domain: plan) - To create detailed skill specifications
  • compare (domain: priority) - To refine gap prioritization
  • explain - To create executive summary

Anti-patterns:

  • Never skip relationship mapping before prioritization
  • Never propose skills without checking ontology for existing alternatives
  • Never prioritize without defined criteria
  • Never generate roadmap without dependency analysis

Workflow references:

  • See reference/workflow_catalog.yaml#capability_gap_analysis for step definitions
  • See reference/capability_ontology.yaml for reference structure

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.