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AB cot-contrarian-detector

Detect crowded speculative positioning in CFTC futures markets (COT report analysis) to find contrarian setups using Jason Shapiro's methodology. Screens large-speculator ("non-commercial") net positioning across 65 futures markets (indices, rates, FX, metals, energy, crypto) via the FMP Commitment of Traders API, computes a 3-year and 26-week COT Index per market, and classifies extremes as CROWDED_LONG / CROWDED_SHORT. Use when the user asks about COT report analysis, crowded positioning, "who is trapped", speculative positioning extremes, contrarian futures setups, or wants to run Jason Shapiro-style analysis. This skill automates crowding DETECTION only (step 1 of 5) — it does not generate trade signals by itself.

tradermonty/claude-trading-skills Agent Skills author: tradermonty 10 files · 6 scripts body ≈ 1 808 tokens Open the sourcegithub.com↗ analyzed 5 d ago

Detect crowded speculative positioning in CFTC futures markets (COT report analysis) to find contrarian setups using Jason Shapiro's methodology.

As a process B 77/100 · Nearly there — weak spots: progress reporting

AnalyzerData and analyticsMarketingtype 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
100/100
safety, quality, tests
Safety 60%
100
Quality 40%
100
Run on models
none yet
Process rating
B
77/100
Nearly there
Progress reporting w 2
0
Failures and branches w 10
50
Tools and files w 18
60
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: 10. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note edit-residue the text marks something as outdated (lines 66, 149): check that old rules are not kept next to new ones — the full check reads the text for contradictions

    Process rating: all ten parameters 77/100

    • 0Progress reporting. Says nothing while it works
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (web, python) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 36 steps
    • 100When it triggers. States when to use and when not to
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1808 tokens
    • 100Running it twice. Mutating operations check current state
    • 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

    • +1No license
    • +2Single-language instructions
    • +5Description quotes 2 example trigger phrases
    • +4Description says when NOT to use the skill
    • +3Description length 727: enough signal without eating the budget
    • +4Structure: 15 headings
    • +3Step-by-step instructions: 36 items
    • +3Output format is stated explicitly
    • +4Has examples (1 code blocks)
    • +4Reference files are cited in the instructions (2 of 2)
    • +3All 3 scripts are documented

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