SKILLEMALL.ai

AB cite-holmes

Cite Holmes — deep research that interrogates its own sources (Verified Deep Research). Calibrates scope first (asks 3-5 sharp questions), plans sub-questions, searches iteratively across sources and languages, then machine-verifies every citation (five states: verified/partial/unverified/unreachable/invalid) before a confidence-graded report ships. Never outputs unverified references; treats fabricated DOIs, dead links and missing sources as first-class catch targets. Use whenever the user asks to "deep research", "look into", "investigate", "compare A vs B", "fact check", "verify this claim", "is it true that...", "check these references", "are these citations real", wants a research report with sources, or needs reliable multi-source answers — even if they never say the word "research".

ClawHub Agent Skills author: docsor1212 v1.1.1 MIT-0 9 files body ≈ 1 136 tokens Open the sourceclawhub.ai analyzed 35 h ago

Cite Holmes — deep research that interrogates its own sources (Verified Deep Research).

As a process B 68/100 · Nearly there — weak spots: inputs and preconditions, running it twice, progress reporting

AnalyzerResearchData and analyticstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
100/100
safety, quality, tests
Safety 60%
100
Quality 40%
100
Run on models
none yet
Process rating
B
68/100
Nearly there
Inputs and preconditions w 11
0
Progress reporting w 2
0
Running it twice w 4
30
the three weakest of ten parameters · all ten

How to improve

    For the model run — optional
    • 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: 9. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 68/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 1 mutating operations with no state check
    • 60Tools and files. Uses tools (web, python) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 70When it triggers. States when to use, but not when not to
    • 100Steps. 11 steps
    • 100Failures and branches. 1 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1136 tokens
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing

    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)
    • +2Single-language instructions
    • +5Description quotes 10 example trigger phrases
    • +3Description length 800: enough signal without eating the budget
    • +4Structure: 12 headings
    • +3Step-by-step instructions: 11 items
    • +3Output format is stated explicitly
    • +4Has examples (1 code blocks)
    • +4Reference files are cited in the instructions (2 of 2)
    • +3All 1 scripts are documented
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 100.

    External checks

    ClawHub: clean
    This is a citation-checking research skill whose web access and local report files fit its stated purpose, though users should be aware it can search broadly and fetch URLs during verification.
    LLM: benign (high) · VirusTotal: · 15 Aug 2026