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emerging-topic-scout

A real-time monitoring system for identifying "incubation period" research hotspots in biological and medical sciences before they are defined by mainstream journals.

48

Quality

51%

Does it follow best practices?

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SecuritybySnyk

Low

Low-risk findings worth noting

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tessl review fix ./scientific-skills/Evidence Insight/emerging-topic-scout/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

50%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The body delivers concrete, executable commands and a usable parameter/config reference, but it is padded with repeated Cloudflare warnings and generic boilerplate, and it inlines large API/config/algorithm blocks that overlap with references/README.md rather than pointing out to them. Workflow steps are templated rather than skill-specific.

Suggestions

Collapse the repeated Cloudflare/bioRxiv-medRxiv caveats into a single Known Issues section and reference it once from Usage/Examples.

Move the full API endpoint reference, config.yaml, and output-format samples into references/ (they already overlap references/README.md) and keep only a minimal quick-start example in SKILL.md.

Replace the generic templated Workflow/Output Requirements/Error Handling sections with the skill's actual flow: install deps → choose source → run main.py → review output, with the py_compile/smoke_test checks inline as checkpoints.

DimensionReasoningScore

Conciseness

The body is noticeably verbose: the bioRxiv/medRxiv Cloudflare caveat is repeated across Overview, Network Access Notice, Usage, Examples, and Known Issues, and several sections (When to Use, Key Features, Implementation Details, Workflow, Output Requirements, Error Handling, Response Template) are generic boilerplate Claude already knows. It is above a 1 because the command/parameter material is accurate and useful, but the padding and repetition place it below the mostly-efficient 3.

2 / 5

Actionability

Concrete executable commands (`python scripts/main.py --sources arxiv --days 7 --output json`), a full parameter table, a config.yaml example, and JSON/markdown output samples give mostly copy-paste-ready guidance. It is below 5 because the installation block hardcodes a personal path (`/Users/z04030865/.openclaw/workspace/...`) and some example blocks are missing closing fence context, leaving minor gaps.

4 / 5

Workflow Clarity

A sequenced Workflow list and explicit validation commands (py_compile Quick Check, smoke_test Audit-Ready) exist, so it is above a 2. However the numbered workflow is generic templated steps rather than this skill's actual install→configure→run→review flow, and the validation checkpoints sit in separate sections rather than inline in the sequence, matching the anchor with steps present but checkpoints implicit/fragmented.

3 / 5

Progressive Disclosure

Bundle files exist (references/README.md, scripts/main.py, smoke_test.py) and the body signals them, but large blocks that belong in references are inlined in SKILL.md — full API endpoint docs, the complete config.yaml, output-format samples, and the trending algorithm — and the API material is duplicated in references/README.md. This fits the anchor with some structure and signaled references but content that should be separate is inline, short of the well-split 4.

3 / 5

Total

12

/

20

Passed

Description

53%Weight 40%Scale 1-5

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 states a clear, third-person purpose with a reasonably distinct niche, but it omits any explicit "Use when..." trigger guidance and relies on a metaphor ("incubation period") rather than natural user keywords. This caps completeness and trigger-term quality at the mid-range.

Suggestions

Add an explicit 'Use when...' clause naming natural triggers, e.g. 'Use when tracking emerging research trends, hot topics, or preprint surges in biomedical literature.'

Replace or gloss the metaphor 'incubation period' with concrete terms users would say (emerging trends, hot topics, preprint monitoring).

Enumerate 2-3 concrete actions (e.g. detect topic velocity spikes, score novelty, cross-reference mentions) to lift specificity beyond one high-level action.

DimensionReasoningScore

Specificity

Names the domain ("biological and medical sciences", "research hotspots") and one concrete action ("identifying...research hotspots" via "real-time monitoring"), but stops at a single high-level action rather than enumerating multiple concrete capabilities. It fits the anchor that names a domain plus 1-2 concrete actions but is not comprehensive, and is not vague enough for a 2.

3 / 5

Completeness

It gives a clear "what" (a monitoring system that identifies research hotspots before mainstream definition) but provides no "when"/"Use when..." trigger guidance, so per the judging guidelines completeness is capped at 3. It is above a 2 because the "what" is explicit and concrete, not vague.

3 / 5

Trigger Term Quality

It surfaces some relevant terms ("research hotspots", "biological and medical sciences", "mainstream journals") but leans on the metaphor "incubation period" and omits the natural phrases a user would actually say, such as "emerging research trends", "hot topics", or "preprints". This matches the anchor with some relevant keywords but missing common variations or synonyms, and falls short of the good keyword coverage at 4.

3 / 5

Distinctiveness Conflict Risk

The niche (early detection of biomedical research hotspots from preprints before journal definition) is fairly distinct with limited overlap risk against generic literature skills. It is below 5 because the description lacks crisp trigger phrases that fully separate it from adjacent trend-tracking skills, and above 3 because the scoped domain materially reduces conflict risk.

4 / 5

Total

13

/

20

Passed

Validation

93%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

15

/

16

Passed

Repository
aipoch/medical-research-skills
Reviewed

Table of Contents

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