tdg-personal/research-ops
Evidence-first current-state research workflow for ECC. Use when the user wants fresh facts, comparisons, enrichment, or a recommendation built from current public evidence and any supplied local context.
69
69%
Does it follow best practices?
Impact
Pending
No eval scenarios have been run
Advisory
Suggest reviewing before use
Quality
Discovery
67%Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.
The description has a solid structure with an explicit 'Use when' clause and covers both what and when. However, it relies on the unexpanded acronym 'ECC' which limits discoverability, and the listed capabilities (facts, comparisons, enrichment, recommendation) are somewhat abstract rather than concretely specific actions. Greater specificity in actions and expanding the acronym would improve selection accuracy.
Suggestions
Expand the 'ECC' acronym so users and Claude can match on the full term (e.g., 'Elliptic Curve Cryptography' or whatever ECC stands for in this context).
Replace abstract terms like 'enrichment' and 'fresh facts' with more concrete actions (e.g., 'gather current pricing data, compare vendor specifications, compile feature matrices').
Add more natural trigger terms users might say, such as specific file types, data sources, or task phrases related to the ECC domain.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Names the domain (ECC research) and some actions ('comparisons, enrichment, recommendation'), but these are somewhat abstract rather than concrete specific actions. 'Fresh facts' and 'evidence-first current-state research workflow' are more descriptive of approach than specific capabilities. | 2 / 3 |
Completeness | Clearly answers both 'what' (evidence-first research workflow for ECC covering facts, comparisons, enrichment, recommendations) and 'when' (explicit 'Use when' clause specifying fresh facts, comparisons, enrichment, or recommendation from current public evidence and local context). | 3 / 3 |
Trigger Term Quality | Includes some relevant keywords like 'research', 'comparisons', 'recommendation', 'facts', and 'ECC', but 'ECC' is an acronym that may not match natural user language without expansion. Missing common variations or more natural phrasing users might use. | 2 / 3 |
Distinctiveness Conflict Risk | The 'ECC' domain scoping helps distinguish it, but terms like 'research', 'comparisons', and 'recommendation' are generic enough to potentially overlap with other research or analysis skills. The unexpanded 'ECC' acronym could cause confusion or missed matches. | 2 / 3 |
Total | 9 / 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 is a well-structured orchestration skill that clearly defines its role relative to other skills in the stack and provides a logical workflow for research tasks. Its main weaknesses are the lack of concrete executable examples (no actual search queries, no sample invocations of referenced skills) and some redundancy across guardrails/pitfalls/verification sections. Adding a feedback loop for when initial research is insufficient would strengthen the workflow.
Suggestions
Add a concrete example showing an actual research request flowing through the workflow (e.g., a sample user query, the classification decision, which skill gets invoked, and the final output)
Consolidate the guardrails, pitfalls, and verification sections—they overlap significantly and could be merged into a single 'Constraints & Checks' section to save tokens
Add an explicit feedback loop in the workflow for when initial evidence is insufficient or contradictory (e.g., 'If exa-search returns no relevant results, escalate to deep-research')
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is reasonably efficient but has some redundancy—guardrails, pitfalls, and verification sections overlap significantly with the workflow and output format sections. The 'When to Use' section restates things Claude could infer. However, it avoids explaining basic concepts. | 2 / 3 |
Actionability | The skill provides structured guidance and references to other skills, but lacks concrete executable examples—no actual commands, code snippets, or copy-paste-ready templates. The output format is a text template but the workflow steps are descriptive rather than executable. | 2 / 3 |
Workflow Clarity | The 5-step workflow is clearly sequenced and logically ordered, with good decision points (classify the ask, choose the lightest path). However, there are no explicit validation checkpoints or feedback loops—if a search returns poor results or evidence is contradictory, there's no guidance on how to recover or iterate. | 2 / 3 |
Progressive Disclosure | The skill clearly positions itself as an orchestration layer and references five other skills by name with brief descriptions of when to use each. References are one level deep and well-signaled. The content is appropriately scoped as an overview. | 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
Table of Contents