BC researcher
Deep-research workflow for UGC reference material — turns one or more URLs / handles / trend queries into a single cited research report (report.md + sources.json) that a scenarist or art-director can act on. Aggregates the per-URL `ralphy ref` chain (yt-dlp pull → frames → transcript → vision → audio-describe → blueprint) and adds a cross-source LLM synthesis pass on top. USE WHEN the user drops a URL on TikTok / Reel / Shorts / YouTube / X / Reddit, mentions an `@handle` to audit, asks "how do they do X", asks "what's trending in <niche>", asks for a competitor breakdown, or wants a style report across multiple references. TRIGGER (EN): "research X", "analyze @handle", "break down this TikTok / Reel / Shorts", "how do they do this", "find how X is done", "what's trending in <niche>", "competitor audit", "extract style from <url>". See body for ALSO FIRE / DO NOT FIRE / HARD INVARIANTS.
Deep-research workflow for UGC reference material — turns one or more URLs / handles / trend queries into a single cited research report (report.md +…
As a process C 62/100 · Has gaps — weak spots: result and completion, inputs and preconditions, progress reporting
The same skill appears in 1 more place: ralphy
How to improve
- 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: 17. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- note
frontmatter-keyunknown frontmatter key "namespace"
Process rating: all ten parameters 62/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Progress reporting. Says nothing while it works
- 60Tools and files. Uses tools (bash, web) that frontmatter does not declare
- 70When it triggers. States when to use, but not when not to
- 100Steps. 27 steps
- 100Failures and branches. 4 branches, has a failure section
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1699 tokens
- 100Running it twice. Mutating operations check current state
- low 10 top-level sections: this looks like several domains in one skill
- low The response is described with custom markup (6 tags): a typed call is more reliable
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)
- +3Description length 900: 120–800 characters recommended
- +3Output format is not stated: the model decides each time
- -37 of 7 scripts are never mentioned in SKILL.md
- +1No license
- +2Single-language instructions
- +5Description quotes 10 example trigger phrases
- +4Structure: 11 headings
- +3Step-by-step instructions: 27 items
- +4Has examples (1 code blocks)
- +4Reference files are cited in the instructions (5 of 9)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 86.