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AC tech-contract-negotiation

Negotiation frameworks for technology services agreements, B2B contracts, and professional services deals. Use when reviewing or negotiating tech contracts.

borghei/Claude-Skills Agent Skills author: borghei 6 files · 2 scripts body ≈ 2 779 tokens Open the sourcegithub.com↗ analyzed 8 h ago

Negotiation frameworks for technology services agreements, B2B contracts, and professional services deals.

As a process C 59/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureSales and CRMtype 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
96/100
safety, quality, tests
Safety 60%
98
Quality 40%
92
Run on models
none yet
Process rating
C
59/100
Has gaps
Inputs and preconditions w 11
0
When it triggers w 12
20
Running it twice w 4
30
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 · 2

    ✓ No critical or high findings

    Medium and low: 2
    • low Risky intent intent-offensive-security references/regulatory_leverage.md:69
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      | Business continuity testing | Threat-led penetration testing requirements | Art. 26-27 |
    • low Risky intent intent-offensive-security references/regulatory_leverage.md:80
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      | Penetration testing | Annual TLPT; share results under NDA | Client-directed pen testing of provider infrastructure is excessive |

    Files scanned: 6. 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 59/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 4 mutating operations with no state check
    • 40Result and completion. Does not say what the result is
    • 60Tools and files. Uses tools (bash) that frontmatter does not declare
    • 85Steps. 44 steps, 1 vague phrases
    • 100Failures and branches. 1 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2779 tokens
    • 100Progress reporting. Reports progress
    • low 11 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)
    • +3Output format is not stated: the model decides each time
    • +2Single-language instructions
    • +3Description length 156: enough signal without eating the budget
    • +4Structure: 19 headings
    • +3Step-by-step instructions: 44 items
    • +4Has examples (4 code blocks)
    • +4Reference files are cited in the instructions (3 of 3)
    • +3All 2 scripts are documented
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 92.