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BF molt-sift

Data validation and signal extraction service for agents. Sift through raw outputs (JSON, text, data streams) to extract quality signals, validate against schemas, and score reliability. Use when: (1) validating agent outputs before execution, (2) cleaning/normalizing data from multiple sources, (3) running bounty validation jobs, (4) extracting high-confidence signals from noisy data. Integrates with PayAClaw, MoltyGuild, and Clawslist for micro-payment bounties via x402 escrow (Solana).

ClawHub Agent Skills author: NoizceEra v0.1.0 22 files body ≈ 1 911 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process F 41/100 · Will not run — References files that are not bundled: references/deployment.md

AnalyzerAI and agentsInfrastructuretype 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
B
84/100
safety, quality, tests
Safety 60%
93
Quality 40%
70
Run on models
none yet
Process rating
F
41/100
Will not run
References files that are not bundled: references/deployment.md
Tools and files w 18
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten

How to improve

  1. The text references files that are not there: add them or drop the references.
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 · 7

✓ No critical or high findings

Medium and low: 7
  • low Secrets in code secret-high-entropy-token scripts/payaclaw_client.py:69
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    "payout_address": "7pf1…sF7",
    quoted
  • low Secrets in code secret-high-entropy-token scripts/payaclaw_client.py:88
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    "payout_address": "7pf1…sF7",
    quoted
  • low Secrets in code secret-high-entropy-token scripts/solana_payment.py:26
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    self.usdc_mint = "EPjF…jVP"  # USDC on Solana
    quoted
  • low Secrets in code secret-high-entropy-token SKILL.md:141
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    "payment_txn": "5Abc…yZ0",
    quoted
  • low Secrets in code secret-high-entropy-token test_bounty_flow.py:34
    High-entropy token-like string (may be an id, hash or a credential) (test fixture / example file; quoted — discussed, not commanded)
    agent_a_address = "7pf1…sF7"
    fixturequoted
  • low Secrets in code secret-high-entropy-token test_bounty_flow.py:35
    High-entropy token-like string (may be an id, hash or a credential) (test fixture / example file; quoted — discussed, not commanded)
    agent_b_address = "4oJ5…Vkq"
    fixturequoted
  • low Secrets in code secret-high-entropy-token test_bounty_flow.py:223
    High-entropy token-like string (may be an id, hash or a credential) (test fixture / example file; quoted — discussed, not commanded)
    "payout_address": "7pf1…sF7"
    fixturequoted

Files scanned: 22. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning frontmatter-yaml SKILL.md: the frontmatter is not valid YAML (YAML parse error: Nested mappings are not allowed in compact mappings at line 2, column 14: description: Data validation and signal extraction service for agents. Sift thr… ^ ); fields were read line by line. The usual cause is a colon inside an unquoted value
  • warning missing-ref reference to a missing file: references/deployment.md

Process rating: all ten parameters 41/100

Will not run. References files that are not bundled: references/deployment.md
  • 0Tools and files. 1 referenced file(s) missing: references/deployment.md
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Failures and branches. Linear process with no failure handling
  • 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
  • 60Result and completion. Output format stated, no completion criterion
  • 100Steps. 46 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1911 tokens

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)
  • -2localhost URLs: will not work for another user
  • -32 of 6 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +3Description length 493: enough signal without eating the budget
  • +4Structure: 20 headings
  • +3Step-by-step instructions: 46 items
  • +3Output format is stated explicitly
  • +4Has examples (17 code blocks)
  • +4Reference files are cited in the instructions (1 of 1)

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

External checks

ClawHub: suspicious
Molt Sift appears to be a legitimate validation tool, but its bounty and payment features are overstated and under-scoped for a financial-style workflow.
LLM: suspicious (high) · VirusTotal: benign · 28 May 2026