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Agent skill

Presales Technical Consulting

by rongxinzy in rongxinzy/RongxinAI

Plan and execute enterprise technical discovery, outcome-led demos, POCs, solution architectures, competitive responses, and evaluation closeout.

AGPL-3.0Auto-check passedDevelopment

Install Presales Technical Consulting

skills CLI
$ npx skills add rongxinzy/RongxinAI --skill presales-technical-consulting -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install rongxinzy/RongxinAI presales-technical-consulting --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/rongxinzy/RongxinAI.git skills-src && mkdir -p .claude/skills && cp -r skills-src/SKILLs/zhiyuan-expert-manager/presets/presales-technical-consultant/skills/presales-technical-consulting .claude/skills/presales-technical-consulting && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
presales-technical-consulting
GitHub stars
154
Token cost
~1.1k tokens
SKILL.md length
549 words
Files
1
Skills in repo
94
Repo updated
First seen
Licence
AGPL-3.0

At a glance

Plan and execute enterprise technical discovery, outcome-led demos, POCs, solution architectures, competitive responses, and evaluation closeout.

  • Works in 5 steps: Establish the current state and the… → Define the desired future state and… → Identify who experiences the problem and… → …
  • A sales opportunity needs technical qualification
  • SKILL.md covers Intake, Discovery, Outcome-led demo and POC plan, plus 3 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Presales Technical Consulting is an agent skill from rongxinzy/RongxinAI. Plan and execute enterprise technical discovery, outcome-led demos, POCs, solution architectures, competitive responses, and evaluation closeout. Use when a sales opportunity needs technical qualification or evidence for a customer decision.

Its SKILL.md is about 1.1k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Development, covering Software architecture. The repository describes itself as: An all-in-one local AI Agent workspace with a fully self-developed stack. The licence is AGPL-3.0.

When your agent uses it

  • A sales opportunity needs technical qualification
  • Evidence for a customer decision

Example prompts

  • “/presales-technical-consulting”

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

  1. Establish the current state and the cost, risk, or delay it creates.
  2. Define the desired future state and measurable outcome.
  3. Identify who experiences the problem and who decides whether it is solved.
  4. Translate needs into decision criteria and ranked success metrics.
  5. Map each criterion to evidence: documentation, demo, reference architecture, benchmark, or POC result.

What it can do on your machine

Read from SKILL.md and the folder at commit 9c64865. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Presales Technical Consulting loads about 1.1k tokens when it runs. Until then it costs about 68 tokens; SKILL.md has 549 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~68
When it runs · the whole SKILL.md, loaded when a task matches
~1.1k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from rongxinzy/RongxinAI at commit 9c64865, republished under its AGPL-3.0 licence (© rongxinzy). 549 words, ~1,150 tokens.

Download SKILL.mdSave it as .claude/skills/presales-technical-consulting/SKILL.md (or your agent's skills folder).
name
presales-technical-consulting
description
Plan and execute enterprise technical discovery, outcome-led demos, POCs, solution architectures, competitive responses, and evaluation closeout. Use when a sales opportunity needs technical qualification or evidence for a customer decision.

Presales Technical Consulting

Use this skill to turn an enterprise opportunity into a bounded, evidence-led technical evaluation. Do not invent customer facts, product capabilities, benchmarks, integrations, certifications, or competitor claims.

Intake

Capture the minimum context before recommending an activity:

  • customer, industry, business objective, current workflow, and quantified impact;
  • stakeholders, technical evaluators, economic buyer, approver, and blockers;
  • current architecture, data, security, integration, deployment, and operational constraints;
  • decision criteria, alternatives, timeline, budget status, and required evidence;
  • known facts, customer statements, assumptions, and open questions as separate fields.

If a missing answer changes scope or feasibility, ask for it. Otherwise state the assumption and continue.

Discovery

Structure discovery around outcomes rather than a feature checklist:

  1. Establish the current state and the cost, risk, or delay it creates.
  2. Define the desired future state and measurable outcome.
  3. Identify who experiences the problem and who decides whether it is solved.
  4. Translate needs into decision criteria and ranked success metrics.
  5. Map each criterion to evidence: documentation, demo, reference architecture, benchmark, or POC result.

Produce a discovery matrix with: objective, current state, impact, requirement, priority, metric, evidence, owner, status, and open question.

Outcome-led demo

Build demos in this order:

  1. Restate the customer's scenario and target result.
  2. Show the shortest end-to-end path that proves the result.
  3. Explain only the implementation details needed by the audience.
  4. Tie each scene to a decision criterion and observable evidence.
  5. Close with confirmed fit, gaps, questions, and the next decision.

For every scene specify audience, setup, sample data, talk track, action, expected output, business relevance, fallback, duration, and owner. Never run a generic feature tour.

POC plan

A POC must contain:

  • one-sentence hypothesis and business context;
  • in-scope and explicitly out-of-scope items;
  • baseline, metric, target, measurement method, and evidence owner;
  • data, environment, access, security, integration, and staffing prerequisites;
  • milestones, daily/weekly checkpoints, issue path, and change control;
  • success, partial success, failure, stop, and exit criteria;
  • final readout date and decision gate.

Keep the POC as small as possible while still testing the risky assumptions. A POC is not production implementation.

Show full SKILL.md (205 more words)Show less

Solution architecture

Describe:

  • actors, systems, trust boundaries, and ownership;
  • request, event, and data flows;
  • APIs, protocols, schemas, rate limits, retries, and failure handling;
  • identity, authorization, secrets, encryption, retention, residency, and audit;
  • deployment topology, capacity assumptions, observability, support, and recovery;
  • confirmed capabilities, conditional fit, gaps, workarounds, dependencies, and open decisions.

Trace every architecture choice to a customer requirement or operational constraint.

Competitive and objection response

Use FIA for each claim:

  • Fact: a sourced, date-stamped, verifiable statement;
  • Impact: why it matters for this customer's criterion or risk;
  • Act: a fair question, demonstration, or validation step.

For objections: acknowledge, clarify the underlying concern, classify it as fact/risk/misunderstanding/commercial condition, answer with evidence, and agree on a validation action. Do not disparage competitors or present stale claims as current.

Evaluation notes and closeout

Maintain one decision record throughout the evaluation:

  • executive outcome and current recommendation;
  • decision criteria with weight, status, evidence, and source;
  • requirement-to-capability traceability;
  • activity log and customer feedback;
  • risks, gaps, mitigations, owners, and due dates;
  • commercial or legal dependencies clearly separated from technical fit;
  • next decision, participants, date, and required inputs.

Use web-search for current external evidence. Use docx, xlsx, pptx, or pdf only after reading the corresponding skill from <available_skills>, and verify the generated artifact before delivery.

© rongxinzy, AGPL-3.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in SKILLs/zhiyuan-expert-manager/presets/presales-technical-consultant/skills/presales-technical-consulting of rongxinzy/RongxinAI.

Open the folder on GitHubat commit 9c64865

Compare with similar skills

Presales Technical Consulting next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Presales Technical Consulting compared with similar skills
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Presales Technical Consulting this skillrongxinzy/RongxinAI154—~1.1kAutomated safety check: PassAGPL-3.0
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Electron Multi-Process ArchitectureiOfficeAI/AionUi33k1 repos~1.8kAutomated safety check: PassApache-2.0
Backend Code Reviewlanggenius/dify158k—~676Automated safety check: PassCustom licence
Dark Architecture Diagram BuilderCocoon-AI/architecture-diagram-generator7.4k1 repos~2.1kAutomated safety check: PassMIT
SVG Diagram GeneratorJimLiu/baoyu-skills27k1 repos~3.1kAutomated safety check: PassMIT

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Categories

Questions about Presales Technical Consulting

What does Presales Technical Consulting do?

Plan and execute enterprise technical discovery, outcome-led demos, POCs, solution architectures, competitive responses, and evaluation closeout. Presales Technical Consulting is an agent skill from rongxinzy/RongxinAI. Plan and execute enterprise technical discovery, outcome-led demos, POCs, solution architectures, competitive responses, and evaluation closeout.

When should I use Presales Technical Consulting?

Presales Technical Consulting fits situations like: A sales opportunity needs technical qualification; evidence for a customer decision.

How do I install Presales Technical Consulting in Claude Code?

Run `npx skills add rongxinzy/RongxinAI --skill presales-technical-consulting -a claude-code`. Or copy the skill folder (SKILLs/zhiyuan-expert-manager/presets/presales-technical-consultant/skills/presales-technical-consulting in rongxinzy/RongxinAI) into .claude/skills/presales-technical-consulting in your project. Claude Code loads it when a task matches its description.

How do I install Presales Technical Consulting in Codex?

Run `npx skills add rongxinzy/RongxinAI --skill presales-technical-consulting -a codex`. Or copy the skill folder (SKILLs/zhiyuan-expert-manager/presets/presales-technical-consultant/skills/presales-technical-consulting in rongxinzy/RongxinAI) into .agents/skills/presales-technical-consulting in your project. Codex loads it when a task matches its description.

Can I use Presales Technical Consulting in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add rongxinzy/RongxinAI --skill presales-technical-consulting -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/presales-technical-consulting, .gemini/skills/presales-technical-consulting, .github/skills/presales-technical-consulting and .opencode/skills/presales-technical-consulting in your project.

What does Presales Technical Consulting need to run?

SKILL.md names no scripts, command-line tools or credentials: Presales Technical Consulting is instructions for the agent only.

Does Presales Technical Consulting access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Presales Technical Consulting safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Presales Technical Consulting use?

Presales Technical Consulting is published under the AGPL-3.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Presales Technical Consulting use?

About 1.1k tokens (SKILL.md is roughly 4.6k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Presales Technical Consulting?

Skills that share tags, products or a category with Presales Technical Consulting: Archify Diagrams (tt-a1i/archify, 82k stars), Electron Multi-Process Architecture (iOfficeAI/AionUi, 33k stars), Backend Code Review (langgenius/dify, 158k stars) and Dark Architecture Diagram Builder (Cocoon-AI/architecture-diagram-generator, 7.4k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Presales Technical Consulting?

rongxinzy (a GitHub organization) maintains it in rongxinzy/RongxinAI, which has 154 GitHub stars. The repository holds 94 skills in this directory. The repository was last updated on October 10, 2026.

Source: rongxinzy/RongxinAI on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.