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DD climate-aligned-contracts-felix-cohen

Draft, adapt, and review contracts and clauses aligned with The Chancery Lane Project's methodology for reducing carbon emissions through legal agreements. Use when Claude needs to: (1) Draft new climate-aligned clauses (e.g., net zero commitments, carbon accounting, supply chain decarbonization), (2) Adapt or modify existing contracts to incorporate climate objectives, (3) Review and analyze clauses for alignment with climate goals and decarbonization strategies, (4) Provide guidance on The Chancery Lane Project's house style and drafting methodology for climate-conscious legal work.

Not recommendedcritical or high security findings · low grade D
lawve-ai/awesome-legal-skills Agent Skills author: lawve-ai 80 files body ≈ 1 245 tokens Open the sourcegithub.com↗ analyzed 76 min ago

Draft, adapt, and review contracts and clauses aligned with The Chancery Lane Project's methodology for reducing carbon emissions through legal agreements.

As a process D 49/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

AnalyzerResearchtype 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
D
77/100
safety, quality, tests
Safety 60%
73
Quality 40%
84
Run on models
none yet
Process rating
D
49/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

What is at stake

The skill contains fragments that, in the wrong hands, cost money or data. Below: what the installer risks and what the author should do.

Obfuscation
If you install

The files contain invisible characters, encoded commands or comments hidden from readers but visible to the model. What you read differs from what the agent sees.

For the author

Remove invisible characters (they usually sneak in through copy-paste) and encoded strings: no catalog will pass them. Instructions for the model must be readable by a human too.

How to improve

  1. Address the high-severity findings: each costs 18 safety points. If one is a false positive, add the rule id to guard.allow in spec.yaml.
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 · 10

  • high Obfuscation uni-zero-width references/clauses/consolidated-sustainability-clauses-for-supply-chain-contracts.md:57
    Zero-width / invisible characters (possible hidden text) (60 occurrences)
    1.1 Initial Assessment(a) Within 3 (three) months of the Commencement Date, the Supplier shall measure the Supplier Carbon Footprint (the **Proposed Measurement…
Medium and low: 9
  • low Secrets in code secret-high-entropy-token references/clauses/carbon-contract-clauses-for-environmental-performance-and-associated-incentives-and-remedies.md:73
    High-entropy token-like string (may be an id, hash or a credential)
    1.3 \[The parties agree to set respective organisational Net Zero Targets that align with either the criteria of the [Race to Zero](https://racetozero.unfccc.int/system/race-to-zero/?_gl=…
  • low Secrets in code secret-password-literal references/clauses/climate-aligned-construction-waste-management-2.md:96
    Hard-coded password / key literal (may be an example)
    - [*EPA: Construction Waste Management*](https://nepis.epa.gov/Exe/ZyNET.exe/9101NS49.TXT?ZyActionD=…&Client=EPA&Index=…&Docs=&Query=&Time=&EndTime=&SearchMethod=1&TocRestrict=n&
  • low Secrets in code secret-high-entropy-token references/clauses/climate-change-clauses-for-heads-of-terms.md:50
    High-entropy token-like string (may be an id, hash or a credential)
    1.2 The Parties \[have signed up to the [Race to Zero](https://racetozero.unfccc.int/system/race-to-zero/?_gl=…
  • low Secrets in code secret-high-entropy-token references/clauses/climate-change-due-diligence-questionnaire-for-suppliers-3.md:66
    High-entropy token-like string (may be an id, hash or a credential)
    2\. Has Supplier set a Net Zero Target, a Science Based Target or a Carbon Budget, signed up to [Race to Zero](https://racetozero.unfccc.int/system/race-to-zero/?_gl=…
  • low Secrets in code secret-high-entropy-token references/clauses/climate-due-diligence-questionnaire-for-ma-transactions.md:64
    High-entropy token-like string (may be an id, hash or a credential)
    1\. Has the Company set a Net Zero Target, a Science Based Target, a Carbon Budget or signed up to [Race to Zero](https://racetozero.unfccc.int/system/race-to-zero/?_gl=…
  • low Secrets in code secret-high-entropy-token references/clauses/climate-purchase-agreement-and-underwriting-sponsor-warranties.md:56
    High-entropy token-like string (may be an id, hash or a credential)
    1.7 The Company has set a Net Zero Target \[validated by the [Science Based Targets Initiative](https://sciencebasedtargets.org/)\] \[\[and\] a Carbon Budget\] \[and has signed up to [Race to Zero](ht
  • low Secrets in code secret-high-entropy-token references/clauses/climate-recitals.md:38
    High-entropy token-like string (may be an id, hash or a credential)
    (A) The parties \[have signed up to the [Race to Zero](https://racetozero.unfccc.int/system/race-to-zero/?_gl=…
  • low Secrets in code secret-high-entropy-token references/clauses/entire-business-net-zero-objectives.md:46
    High-entropy token-like string (may be an id, hash or a credential)
    1.1 within \[*time period*\] of entering into this agreement publicly set an organisational Net Zero Target \[validated by the [Science Based Targets initiative](https://sciencebasedtargets.org/)\]\[a
  • low Secrets in code secret-high-entropy-token references/clauses/environmental-business-charter.md:50
    High-entropy token-like string (may be an id, hash or a credential)
    3\. Publicly set a Net Zero Target \[approved by the [Science Based Targets initiative](https://sciencebasedtargets.org/)\]\[and sign up to the [Race to Zero](https://racetozero.unfccc.int/system/race

Files scanned: 80. 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 49/100

  • 0Result and completion. Does not say what the result is
  • 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. 5 mutating operations with no state check
  • 40Consistency. Frontmatter name (climate-aligned-contracts-felix-cohen) differs from the folder (climate-aligned-contracts-the-chancery-lane-project)
  • 55Failures and branches. 1 branches
  • 85Steps. 41 steps, 2 vague phrases
  • 100Tools and files. No external tools needed
  • 100Execution cost. Instruction body is 1245 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)
  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +3Description length 591: enough signal without eating the budget
  • +4Structure: 14 headings
  • +3Step-by-step instructions: 41 items
  • +4Has examples (0 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.