AF deep-literature-review-agent
Run a multi-source, agentic literature review with real citations. Searches arXiv, Semantic Scholar Open, OpenAlex, and Crossref; deduplicates; screens abstracts against user-defined inclusion criteria; extracts findings + methods per paper; synthesizes a thematic review.md with BibTeX. Trigger when the user asks for a "literature review", "lit review", "survey the field on X", "systematic review", or "find me N papers on Y".
As a process F 70/100 · Will not run — References files that are not bundled: assets/review_template.md, scripts/search_multi.py, scripts/dedupe.py
How to improve
- The text references files that are not there: add them or drop the references.
- Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
- A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.
Guard findings · 0
✓ No critical or high findings
Files scanned: 1. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
missing-refreference to a missing file: assets/review_template.md - warning
missing-refreference to a missing file: scripts/search_multi.py - warning
missing-refreference to a missing file: scripts/dedupe.py - warning
missing-refreference to a missing file: scripts/extract.py
Process rating: all ten parameters 70/100
- 0Tools and files. 4 referenced file(s) missing: assets/review_template.md, scripts/search_multi.py, scripts/dedupe.py
- 60Result and completion. Output format stated, no completion criterion
- 70When it triggers. States when to use, but not when not to
- 70Inputs and preconditions. Inputs and preconditions are listed
- 100Steps. 38 steps
- 100Failures and branches. 1 branches, has a failure section
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1838 tokens
- 100Running it twice. Mutating operations check current state
- 100Progress reporting. Reports progress
Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.
Quality signals
- +4Description does not say when NOT to use the skill (false activations)
- +1No license
- +2Single-language instructions
- +5Description quotes 5 example trigger phrases
- +3Description length 429: enough signal without eating the budget
- +4Structure: 8 headings
- +3Step-by-step instructions: 38 items
- +3Output format is stated explicitly
- +4Has examples (2 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 80.