tdg-personal/social-graph-ranker
Weighted social-graph ranking for warm intro discovery, bridge scoring, and network gap analysis across X and LinkedIn. Use when the user wants the reusable graph-ranking engine itself, not the broader outreach or network-maintenance workflow layered on top of it.
76
76%
Does it follow best practices?
Impact
Pending
No eval scenarios have been run
Advisory
Suggest reviewing before use
Quality
Discovery
85%Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.
This is a well-crafted description that clearly defines its scope and explicitly differentiates itself from a related skill. Its main weakness is that the trigger terms are quite technical and specialized—users seeking this functionality might use more colloquial language like 'find mutual connections' or 'who can introduce me to X'. The explicit 'Use when' clause with a negative boundary ('not the broader outreach workflow') is a strong distinguishing feature.
Suggestions
Add more natural-language trigger terms users might actually say, such as 'find mutual connections', 'who can introduce me', 'connection strength', or 'networking paths'
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: 'weighted social-graph ranking', 'warm intro discovery', 'bridge scoring', and 'network gap analysis across X and LinkedIn'. These are concrete, well-defined capabilities. | 3 / 3 |
Completeness | Clearly answers both what ('weighted social-graph ranking for warm intro discovery, bridge scoring, and network gap analysis across X and LinkedIn') and when ('Use when the user wants the reusable graph-ranking engine itself, not the broader outreach or network-maintenance workflow'), with an explicit differentiating trigger clause. | 3 / 3 |
Trigger Term Quality | Includes some relevant domain keywords like 'warm intro', 'bridge scoring', 'network gap analysis', 'X', and 'LinkedIn', but these are fairly specialized terms. Common user phrases like 'find connections', 'who can introduce me', or 'mutual contacts' are missing. Users may not naturally say 'bridge scoring' or 'social-graph ranking'. | 2 / 3 |
Distinctiveness Conflict Risk | The description explicitly distinguishes itself from a broader outreach/network-maintenance workflow skill, creating a clear boundary. The specific niche of 'graph-ranking engine' for social networks is highly distinctive and unlikely to conflict with other skills. | 3 / 3 |
Total | 11 / 12 Passed |
Implementation
57%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This skill provides a solid mathematical framework for social graph ranking with clear formulas and a useful output template. Its main weaknesses are the lack of executable code or concrete tool commands for actually performing the computation, and the absence of validation checkpoints in the workflow. The conceptual model is strong but the skill reads more like a specification than an actionable instruction set.
Suggestions
Add executable code (Python or pseudocode with clear library references) for computing B(m), B_ext(m), and R(m) from actual graph data structures, making the math directly implementable.
Add validation checkpoints to the workflow, e.g., verify graph data completeness after step 2, sanity-check score distributions after step 5, and handle edge cases like disconnected targets.
Trim the 'Scoring Signals' section significantly—most of these weighting factors (role alignment, geographic relevance, etc.) are obvious to Claude and don't need enumeration.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is reasonably efficient but includes some unnecessary sections like 'When To Use This Standalone' with example phrases that could be trimmed, and the 'Scoring Signals' section largely restates obvious weighting factors Claude would already know. The math notation is appropriately dense, but surrounding prose could be tighter. | 2 / 3 |
Actionability | The math formulas are concrete and well-defined, and the output shape provides a clear template. However, there's no executable code for actually computing the scores, no concrete commands for pulling graph data, and the workflow steps like 'Pull the user's graph from X, LinkedIn, or both' are vague about how to actually do this. The skill describes a model more than it instructs on execution. | 2 / 3 |
Workflow Clarity | The 6-step workflow is clearly sequenced and logical, but lacks any validation checkpoints. There's no verification step after pulling graph data, no check on whether the target set is well-formed, and no feedback loop for when graph data is incomplete or bridge scores seem anomalous. For a multi-step computational workflow, this is a notable gap. | 2 / 3 |
Progressive Disclosure | The skill is well-structured with clear sections progressing from context to model to workflow to output. Related skills are clearly signaled at the end with one-level-deep references to other skills. The content is appropriately self-contained for the ranking engine while pointing elsewhere for broader workflows. | 3 / 3 |
Total | 9 / 12 Passed |
Validation
90%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 10 / 11 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
frontmatter_unknown_keys | Unknown frontmatter key(s) found; consider removing or moving to metadata | Warning |
Total | 10 / 11 Passed | |
Reviewed
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