SKILLEMALL.ai

AF eia-process-intel

Investigation workflow that turns Chinese EIA filings (环评报告/环评公示) into investment due-diligence intelligence: structured extraction (product scheme, per-step process flow, equipment list, material balance), adversarial field-credibility grading, physics-based cross-validation (low-risk fields as trust anchors to back-calculate capacity/yield/emissions), process-to-equipment mapping inference, and an append-only triple ledger with provenance. Use when: (1) analyzing a company EIA filing or 受理公示, (2) verifying capacity claims / 产能真实性核验, (3) inferring equipment selection from a 设备清单, (4) back-calculating yield from 物料平衡 / material balance, (5) auditing EIA numbers for gaming patterns (批小建大), (6) building a process/equipment fact ledger across deals. Triggers: 环评, EIA report, material balance, equipment list, capacity verification, yield back-calculation, regulatory gaming audit.

ClawHub Agent Skills author: tianzhiceng297-boop v0.3.0 MIT-0 10 files body ≈ 4 154 tokens Open the sourceclawhub.ai analyzed 3 d ago

Investigation workflow that turns Chinese EIA filings (环评报告/环评公示) into investment due-diligence intelligence: structured extraction (product scheme, per-step…

As a process F 36/100 · Will not run — References files that are not bundled: references/mappings/<track>.md

ProcedureData and analyticstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
91/100
safety, quality, tests
Safety 60%
100
Quality 40%
77
Run on models
none yet
Process rating
F
36/100
Will not run
References files that are not bundled: references/mappings/<track>.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 · 0

✓ No critical or high findings

Files scanned: 10. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning missing-ref reference to a missing file: references/mappings/<track>.md
  • note frontmatter-key unknown frontmatter key "slug"
  • note frontmatter-key unknown frontmatter key "displayName"
  • note frontmatter-key unknown frontmatter key "summary"
  • note frontmatter-key unknown frontmatter key "agent_created"

Process rating: all ten parameters 36/100

Will not run. References files that are not bundled: references/mappings/<track>.md
  • 0Tools and files. 1 referenced file(s) missing: references/mappings/<track>.md
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Failures and branches. Linear process with no failure handling
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 6 mutating operations with no state check
  • 40Result and completion. Does not say what the result is
  • 70Execution cost. Instruction body is 4154 tokens
  • 85Steps. 54 steps, 1 vague phrases
  • 100Consistency. Name and required fields are in place
  • 100Progress reporting. Reports progress
  • 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

  • +5Description has no quoted example phrases that should trigger the skill
  • +4Description does not say when NOT to use the skill (false activations)
  • +3Description length 888: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • +2Single-language instructions
  • +4Structure: 20 headings
  • +3Step-by-step instructions: 54 items
  • +4Has examples (1 code blocks)
  • +4Reference files are cited in the instructions (3 of 3)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 77.

External checks

ClawHub: clean
This markdown-only skill is a coherent EIA due-diligence workflow, with disclosed local ledger persistence but no hidden execution or exfiltration behavior found.
LLM: benign (high) · VirusTotal: · 10 Sept 2026