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AC citable

Citable builds an interactive cite-checking table for US court filings (briefs, memoranda, petitions, declarations, etc.). Every citation links to its pinpoint location in the record exhibit, the case (CourtListener), the statute (Cornell LII) or the treaty (flagged where one cannot be located), with a status and a note field per citation for the reviewer to fill. When the filing is revised, citable rebuilds the table, flags citation-related changes and carries the saved review forward. Use when the user asks to cite-check, build a citation table, link a brief's citations, or update a table for a revised draft.

lawve-ai/awesome-legal-skills Agent Skills author: lawve-ai 53 files · 43 scripts body ≈ 3 901 tokens Open the sourcegithub.com↗ analyzed 27 h ago

Citable builds an interactive cite-checking table for US court filings (briefs, memoranda, petitions, declarations, etc.). Every citation links to its…

As a process C 62/100 · Has gaps — weak spots: result and completion, consistency

GeneratorResearchtype and topics are labelled automatically from the skill text
Runs in: Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) OpenClaw Goose OpenCode
JSON
Technical rating
A
95/100
safety, quality, tests
Safety 60%
100
Quality 40%
88
Run on models
none yet
Process rating
C
62/100
Has gaps
Result and completion w 14
0
Consistency w 8
40
Failures and branches w 10
50
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: 53. 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 62/100

    • 0Result and completion. Does not say what the result is
    • 40Consistency. Frontmatter name (citable) differs from the folder (citable-cameron-russell)
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 25 steps
    • 100Execution cost. Instruction body is 3901 tokens
    • 100Running it twice. Mutating operations check current state
    • 100Progress reporting. Reports progress
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
    • low The response is described with custom markup (11 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

    • +5Description has no quoted example phrases that should trigger the skill
    • +4Description does not say when NOT to use the skill (false activations)
    • +3Output format is not stated: the model decides each time
    • +4No input/output examples
    • +2Single-language instructions
    • +3Description length 618: enough signal without eating the budget
    • +4Structure: 6 headings
    • +3Step-by-step instructions: 25 items
    • +4Reference files are cited in the instructions (1 of 1)
    • +3All 2 scripts are documented
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 88.