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Add ClawHub publish readiness checks
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‎.clawhubignore‎

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# ClawHub accepts text-based skill bundles. Keep generated/binary assets in GitHub only.
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*.png
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*.jpg
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*.jpeg
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*.gif
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*.ico
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*.pdf
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*.docx
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*.ipynb
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*.ttf
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*.otf
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*.woff
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*.woff2
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preview/
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output/
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__pycache__/
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*.py[cod]
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*$py.class
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.mypy_cache/
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.pytest_cache/
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.ruff_cache/
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.venv/
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venv/
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env/
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ENV/
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.env
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.env.*
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!.env.example
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secrets.json
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*_secret.ini
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*_local.ini
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*.tmp
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.DS_Store
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Thumbs.db
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desktop.ini

‎CLAW_HUB_PUBLISHING.md‎

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# ClawHub Publishing
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This repository is prepared for ClawHub publishing as a text-based OpenClaw skill bundle.
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## Included In ClawHub
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The ClawHub bundle is intended to include text-based skill material only:
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- `SKILL.md`, `README.md`, `QUALITY.md`, and this file
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- `agents/openai.yaml`
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- `pyproject.toml`
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- `.github/workflows/quality.yml`
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- `scripts/*.py`
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- `references/` text files, schemas, catalogs, and examples
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- `examples/` text inputs such as `.ini` files or README placeholders
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- `assets/diagrams/*.mmd`
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- config examples and templates where they are text-based
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## Excluded From ClawHub
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`.clawhubignore` excludes binary and generated assets from the publish candidate, including PDFs, PNG previews, DOCX files, fonts, logos, `preview/`, `output/`, caches, virtual environments, local env files, and temporary files.
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Those files remain part of the GitHub repository for brand presentation, examples, local demos, and generated artifact previews. They are not part of the ClawHub text bundle.
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## License And Brand Boundary
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ClawHub publishes skills under MIT-0. The text/code bundle can be used under ClawHub's publishing terms, but CompleteTech LLC names, logos, seals, and other brand assets remain reserved. Publishing this text bundle does not grant a trademark or brand-asset license and does not relicense excluded binary brand assets.
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## Runtime Dependencies
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Runtime requirements are declared in `SKILL.md` under `metadata.openclaw`.
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- All Python-backed skills require `python3`.
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- PDF/generator skills declare `reportlab` where PDF rendering is used.
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- The contract skill declares `jinja2`.
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- `pyyaml` is declared so the included quality/audit validator can parse YAML metadata.
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- Optional PNG preview dependencies such as `pypdfium2` and `pillow` are GitHub/local-demo conveniences, not required for the ClawHub core workflow unless a user intentionally runs PNG preview generation.
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## Local Readiness Check
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Run before publishing:
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```bash
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python3 scripts/validate_quality.py
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```
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The validator checks lint, Python compilation, structured-file parsing, Mermaid rendering, smoke tests, Pyright where configured, and ClawHub bundle readiness. It does not publish to ClawHub.
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## Publishing
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Do not publish automatically. Use the ClawHub CLI only after explicit approval and an authenticated owner context, for example:
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```bash
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clawhub skill publish . --owner <owner> --version <semver>
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```

‎SKILL.md‎

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---
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name: agentic-discovery-skill
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description: >-
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Create CompleteTech LLC discovery and scoping artifacts for agentic development opportunities, including intake questionnaires, workflow discovery scripts, stakeholder interview guides, current/future-state workflow maps, systems/data readiness checklists, approval gate reviews, risk/excluded-use checks, success criteria, evaluation examples, pilot readiness scorecards, and proposal/SOW handoff briefs. Use before proposal, contract, invoice, or delivery work when Codex needs to gather verified facts for bounded agentic workflow services.
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version: 1.0.0
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metadata:
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openclaw:
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skillKey: agentic-discovery-skill
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homepage: https://github.com/CompleteTech-LLC/agentic-discovery-skill
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requires:
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bins:
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- python3
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---
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# Agentic Discovery Skill
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## Purpose
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Create discovery and scoping artifacts that turn an agentic development opportunity into verified facts for proposal/SOW, contract, invoice, and delivery work.
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## System Boundary
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This skill owns pre-sale fact-finding and scoping. Hand off to `agentic-proposal-skill` for buyer-facing scope and SOW documents, `agentic-security-review-skill` for formal security or launch risk review, `agentic-delivery-skill` after approval, and `agentic-customer-success-skill` for ongoing relationship management. Do not use discovery artifacts as final proposals, contracts, invoices, launch signoffs, or public proof.
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## Core Workflow
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1. Identify the discovery need: qualification, intake, stakeholder interview, workflow map, readiness check, risk review, success criteria, evaluation examples, prioritization, recap, or proposal handoff.
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2. Gather verified facts only: repeated workflow, inputs, systems, tools, retrieval sources, decision points, approval gates, risks, exclusions, logging, monitoring, documentation, support, and handoff needs.
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3. Use `references/use-case-decision-table.md` to choose the right artifact.
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4. Use `references/discovery-positioning.md` for CompleteTech LLC framing and guardrails.
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5. Use `references/discovery-catalog.md` for near-exhaustive artifact templates.
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6. Keep discovery practical and bounded. Do not fabricate client facts, proof, savings metrics, regulated-use assurances, legal claims, or implementation feasibility.
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## Artifact Selection Guide
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- First-pass lead fit: use `quick-qualification-checklist`.
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- New opportunity intake: use `client-intake-questionnaire`.
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- Live discovery call: use `workflow-discovery-script`.
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- Multiple roles or departments: use `stakeholder-interview-guide`.
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- Existing process documentation: use `current-state-workflow-map`.
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- Proposed reviewed-agent process: use `future-state-agentic-workflow-map`.
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- System, document, data, or access review: use `systems-and-data-access-checklist`.
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- API/tool feasibility: use `tool-api-readiness-checklist`.
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- Approval and escalation design: use `human-approval-gate-checklist`.
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- Regulated, sensitive, or risky workflow: use `risk-and-excluded-use-checklist`.
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- Buyer asks what success looks like: use `success-criteria-worksheet`.
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- Need test examples: use `evaluation-example-worksheet`.
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- Decide whether to propose a pilot: use `pilot-readiness-scorecard`.
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- After discovery call: use `discovery-recap-memo`.
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- Handoff into proposal/SOW: use `requirements-brief-for-proposal-sow-handoff`.
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- Missing facts or assumptions: use `assumptions-and-unknowns-log`.
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- Ownership unclear: use `stakeholder-responsibility-matrix`.
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- Dependencies unclear: use `implementation-dependency-checklist`.
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- Sensitive data or retention concerns: use `data-sensitivity-and-retention-worksheet`.
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- Several possible workflows: use `workflow-prioritization-matrix`.
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- Need logging/monitoring requirements: use `logging-and-monitoring-needs-worksheet`.
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- Need support/handoff expectations: use `support-and-handoff-discovery-worksheet`.
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- Need production-readiness check: use `pilot-to-production-readiness-checklist`.
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When several artifacts fit, choose the earliest artifact that resolves the biggest unknown. Do not draft proposal/SOW scope until the workflow, owners, approval points, success criteria, dependencies, and exclusions are clear enough to avoid vague scope.
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## Quality Rules
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- Frame discovery as bounded workflow scoping, not AI brainstorming.
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- Always identify human approval points for external communication, production changes, purchases, data export, and material business decisions.
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- Capture risks, excluded uses, data sensitivity, and assumptions explicitly.
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- Make outputs easy to hand to `agentic-proposal-skill`.
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- Use `TBD` or open questions for unknowns.
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## Resource Guide
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- `references/discovery-positioning.md`: load for CompleteTech LLC discovery language and boundaries.
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- `references/use-case-decision-table.md`: load when choosing an artifact.
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- `references/discovery-lifecycle.md`: load for flow from lead qualification through proposal handoff.
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- `references/discovery-catalog.md`: load for the near-exhaustive artifact library.
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- `references/template-index.json`: machine-readable template metadata used by the renderer.
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- `scripts/render_discovery.py`: list discovery artifacts or render a draft with placeholders.
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## Renderer
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```bash
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python3 scripts/render_discovery.py --list
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python3 scripts/render_discovery.py --stage readiness --list
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python3 scripts/render_discovery.py --template client-intake-questionnaire --var client_name=Acme --var workflow="support triage"
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```
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Rendered artifacts are drafts. Replace placeholders with verified client facts and refine questions for the meeting context.
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## Rendering to a Branded PDF
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Artifacts from this skill are delivered as branded CompleteTech LLC **PDF** documents, not raw Markdown. The renderer emits the PDF (and prints the Markdown) in **one command**, using the same reportlab branding engine as the contract skill:
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```bash
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pip install -r requirements.txt
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python3 scripts/render_discovery.py --template requirements-brief-for-proposal-sow-handoff \
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--out artifact.pdf --png artifact.png \
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--title "Requirements Brief — Proposal / SOW Handoff" --doc-type "DISCOVERY HANDOFF" \
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--subtitle "Prepared for <b>Northwind Trading Co.</b>" --meta "DOCUMENT NO.=DISC-2026-0117" --meta "DATE=2026-05-15" \
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--var client_name="Client Name" --var workflow="support triage"
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```
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- `--no-pdf` emits Markdown only (the original behavior); `--no-cover` drops the cover page.
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- Already drafted the Markdown yourself? Render it directly: `python3 scripts/render_pdf.py --markdown artifact.md --out artifact.pdf --logo assets/logo.png --title "..."`.
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- The PDF supports a Markdown subset: `#`/`##`/`###` headings, paragraphs, `-` bullets, tables, `>` callouts, `**bold**`, and `[PAGE_BREAK]`. PDF requires `reportlab`; the optional `--png` preview requires `pypdfium2` and `pillow`. See `assets/examples/` for a rendered example.
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---
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name: agentic-discovery-skill
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description: >-
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Create CompleteTech LLC discovery and scoping artifacts for agentic development opportunities, including intake questionnaires, workflow discovery scripts, stakeholder interview guides, current/future-state workflow maps, systems/data readiness checklists, approval gate reviews, risk/excluded-use checks, success criteria, evaluation examples, pilot readiness scorecards, and proposal/SOW handoff briefs. Use before proposal, contract, invoice, or delivery work when Codex needs to gather verified facts for bounded agentic workflow services.
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version: 1.0.0
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metadata:
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openclaw:
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skillKey: agentic-discovery-skill
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homepage: https://github.com/CompleteTech-LLC/agentic-discovery-skill
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requires:
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bins:
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- python3
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install:
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- kind: uv
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package: reportlab>=4.0
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- kind: uv
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package: pyyaml>=6.0
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---
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# Agentic Discovery Skill
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## Purpose
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Create discovery and scoping artifacts that turn an agentic development opportunity into verified facts for proposal/SOW, contract, invoice, and delivery work.
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## System Boundary
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This skill owns pre-sale fact-finding and scoping. Hand off to `agentic-proposal-skill` for buyer-facing scope and SOW documents, `agentic-security-review-skill` for formal security or launch risk review, `agentic-delivery-skill` after approval, and `agentic-customer-success-skill` for ongoing relationship management. Do not use discovery artifacts as final proposals, contracts, invoices, launch signoffs, or public proof.
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## Core Workflow
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1. Identify the discovery need: qualification, intake, stakeholder interview, workflow map, readiness check, risk review, success criteria, evaluation examples, prioritization, recap, or proposal handoff.
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2. Gather verified facts only: repeated workflow, inputs, systems, tools, retrieval sources, decision points, approval gates, risks, exclusions, logging, monitoring, documentation, support, and handoff needs.
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3. Use `references/use-case-decision-table.md` to choose the right artifact.
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4. Use `references/discovery-positioning.md` for CompleteTech LLC framing and guardrails.
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5. Use `references/discovery-catalog.md` for near-exhaustive artifact templates.
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6. Keep discovery practical and bounded. Do not fabricate client facts, proof, savings metrics, regulated-use assurances, legal claims, or implementation feasibility.
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## Artifact Selection Guide
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- First-pass lead fit: use `quick-qualification-checklist`.
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- New opportunity intake: use `client-intake-questionnaire`.
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- Live discovery call: use `workflow-discovery-script`.
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- Multiple roles or departments: use `stakeholder-interview-guide`.
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- Existing process documentation: use `current-state-workflow-map`.
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- Proposed reviewed-agent process: use `future-state-agentic-workflow-map`.
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- System, document, data, or access review: use `systems-and-data-access-checklist`.
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- API/tool feasibility: use `tool-api-readiness-checklist`.
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- Approval and escalation design: use `human-approval-gate-checklist`.
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- Regulated, sensitive, or risky workflow: use `risk-and-excluded-use-checklist`.
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- Buyer asks what success looks like: use `success-criteria-worksheet`.
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- Need test examples: use `evaluation-example-worksheet`.
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- Decide whether to propose a pilot: use `pilot-readiness-scorecard`.
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- After discovery call: use `discovery-recap-memo`.
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- Handoff into proposal/SOW: use `requirements-brief-for-proposal-sow-handoff`.
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- Missing facts or assumptions: use `assumptions-and-unknowns-log`.
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- Ownership unclear: use `stakeholder-responsibility-matrix`.
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- Dependencies unclear: use `implementation-dependency-checklist`.
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- Sensitive data or retention concerns: use `data-sensitivity-and-retention-worksheet`.
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- Several possible workflows: use `workflow-prioritization-matrix`.
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- Need logging/monitoring requirements: use `logging-and-monitoring-needs-worksheet`.
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- Need support/handoff expectations: use `support-and-handoff-discovery-worksheet`.
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- Need production-readiness check: use `pilot-to-production-readiness-checklist`.
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When several artifacts fit, choose the earliest artifact that resolves the biggest unknown. Do not draft proposal/SOW scope until the workflow, owners, approval points, success criteria, dependencies, and exclusions are clear enough to avoid vague scope.
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## Quality Rules
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- Frame discovery as bounded workflow scoping, not AI brainstorming.
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- Always identify human approval points for external communication, production changes, purchases, data export, and material business decisions.
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- Capture risks, excluded uses, data sensitivity, and assumptions explicitly.
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- Make outputs easy to hand to `agentic-proposal-skill`.
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- Use `TBD` or open questions for unknowns.
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## Resource Guide
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- `references/discovery-positioning.md`: load for CompleteTech LLC discovery language and boundaries.
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- `references/use-case-decision-table.md`: load when choosing an artifact.
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- `references/discovery-lifecycle.md`: load for flow from lead qualification through proposal handoff.
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- `references/discovery-catalog.md`: load for the near-exhaustive artifact library.
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- `references/template-index.json`: machine-readable template metadata used by the renderer.
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- `scripts/render_discovery.py`: list discovery artifacts or render a draft with placeholders.
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## Renderer
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```bash
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python3 scripts/render_discovery.py --list
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python3 scripts/render_discovery.py --stage readiness --list
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python3 scripts/render_discovery.py --template client-intake-questionnaire --var client_name=Acme --var workflow="support triage"
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```
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Rendered artifacts are drafts. Replace placeholders with verified client facts and refine questions for the meeting context.
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## Rendering to a Branded PDF
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Artifacts from this skill are delivered as branded CompleteTech LLC **PDF** documents, not raw Markdown. The renderer emits the PDF (and prints the Markdown) in **one command**, using the same reportlab branding engine as the contract skill:
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```bash
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pip install -r requirements.txt
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python3 scripts/render_discovery.py --template requirements-brief-for-proposal-sow-handoff \
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--out artifact.pdf --png artifact.png \
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--title "Requirements Brief — Proposal / SOW Handoff" --doc-type "DISCOVERY HANDOFF" \
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--subtitle "Prepared for <b>Northwind Trading Co.</b>" --meta "DOCUMENT NO.=DISC-2026-0117" --meta "DATE=2026-05-15" \
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--var client_name="Client Name" --var workflow="support triage"
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```
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- `--no-pdf` emits Markdown only (the original behavior); `--no-cover` drops the cover page.
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- Already drafted the Markdown yourself? Render it directly: `python3 scripts/render_pdf.py --markdown artifact.md --out artifact.pdf --logo assets/logo.png --title "..."`.
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- The PDF supports a Markdown subset: `#`/`##`/`###` headings, paragraphs, `-` bullets, tables, `>` callouts, `**bold**`, and `[PAGE_BREAK]`. PDF requires `reportlab`; the optional `--png` preview requires `pypdfium2` and `pillow`. See `assets/examples/` for a rendered example.

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