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AC pendle-pt-research

Research Pendle PT (principal token) markets, including unlevered hold-to-par ideas, near-expiry rotations, and looped PT strategies across money markets like Morpho and Euler. Use when evaluating Pendle PT opportunities, comparing natural PT APY versus practical loopability, ranking PTs by time to par / implied APY / liquidity / underlying risk, assessing PT collateral support, or comparing manual-only loops against easier execution paths such as Contango.

ClawHub Agent Skills author: Moshu v1.0.0 MIT-0 23 files body ≈ 3 843 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process C 56/100 · Has gaps — weak spots: when it triggers, inputs and preconditions, running it twice

AnalyzerInfrastructuretype and topics are labelled automatically from the skill text
Runs in: OpenClaw 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) Goose OpenCode
JSON
Technical rating
A
93/100
safety, quality, tests
Safety 60%
92
Quality 40%
94
Run on models
none yet
Process rating
C
56/100
Has gaps
Inputs and preconditions w 11
0
Progress reporting w 2
0
When it triggers w 12
20
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 · 8

    ✓ No critical or high findings

    Medium and low: 8
    • low Secrets in code secret-high-entropy-token data/markets.latest.json:3674
      High-entropy token-like string (may be an id, hash or a credential) (test fixture / example file; quoted — discussed, not commanded)
      "ptSymbol": "PT-u…026",
      fixturequoted
    • low Secrets in code secret-high-entropy-token data/markets.latest.json:3712
      High-entropy token-like string (may be an id, hash or a credential) (test fixture / example file; quoted — discussed, not commanded)
      "ptSymbol": "PT-u…026",
      fixturequoted
    • low Secrets in code secret-high-entropy-token data/markets.latest.json:3750
      High-entropy token-like string (may be an id, hash or a credential) (test fixture / example file; quoted — discussed, not commanded)
      "ptSymbol": "PT-u…026",
      fixturequoted
    • low Secrets in code secret-high-entropy-token data/morpho-pt-markets.latest.json:12
      High-entropy token-like string (may be an id, hash or a credential) (test fixture / example file; quoted — discussed, not commanded)
      "loanAssetAddress": "0xA0…B48",
      fixturequoted
    • low Secrets in code secret-high-entropy-token data/morpho-pt-markets.latest.json:38
      High-entropy token-like string (may be an id, hash or a credential) (test fixture / example file; quoted — discussed, not commanded)
      "loanAssetAddress": "0x98…665",
      fixturequoted
    • low Secrets in code secret-high-entropy-token data/morpho-pt-markets.latest.json:64
      High-entropy token-like string (may be an id, hash or a credential) (test fixture / example file; quoted — discussed, not commanded)
      "loanAssetAddress": "0xA0…B48",
      fixturequoted
    • low Secrets in code secret-high-entropy-token data/morpho-pt-markets.latest.json:90
      High-entropy token-like string (may be an id, hash or a credential) (test fixture / example file; quoted — discussed, not commanded)
      "loanAssetAddress": "0xA0…B48",
      fixturequoted
    • low Secrets in code secret-high-entropy-token data/morpho-pt-markets.latest.json:116
      High-entropy token-like string (may be an id, hash or a credential) (test fixture / example file; quoted — discussed, not commanded)
      "loanAssetAddress": "0xA0…B48",
      fixturequoted

    Files scanned: 23. 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 56/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 3 mutating operations with no state check
    • 60Tools and files. Uses tools (web) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 60Failures and branches. 2 branches
    • 85Steps. 189 steps, 2 vague phrases
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3843 tokens
    • low 12 top-level sections: this looks like several domains in one skill

    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)
    • +1No license
    • +2Single-language instructions
    • +3Description length 461: enough signal without eating the budget
    • +4Structure: 23 headings
    • +3Step-by-step instructions: 189 items
    • +3Output format is stated explicitly
    • +4Has examples (5 code blocks)
    • +4Reference files are cited in the instructions (2 of 2)
    • +3All 11 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 94.

    External checks

    ClawHub: clean
    This is a disclosed DeFi research skill that fetches public market data and ranks Pendle PT opportunities, but users should treat its leveraged-loop outputs as research rather than execution advice.
    LLM: benign (high) · VirusTotal: · 29 May 2026