Skill: completetech/agentic-case-study-skill (version 1.0.3)

--- name: agentic-case-study-skill description: >- Create CompleteTech LLC case studies, testimonials, and proof assets for completed agentic development engagements, including intake questionnaires, outcome interview guides, anonymized and named-client case studies, before/after workflow summaries, implementation stories, technical notes, risk/control summaries, approval-gate summaries, evaluation results, testimonial requests/drafts, proof libraries, sales one-pagers, website stories, LinkedIn posts, nurture emails, referral blurbs, portfolio entries, pitches, award submissions, press releases, quote approvals, and anonymization checks. Use after delivery when Codex needs to package verified client-approved outcomes without exposing confidential details or inventing proof. version: 1.0.3 metadata: openclaw: skillKey: agentic-case-study-skill homepage: https://github.com/CompleteTech-LLC/agentic-case-study-skill requires: bins: - python3 install: - kind: uv package: reportlab==4.5.1 - kind: uv package: pypdfium2==5.8.0 - kind: uv package: pillow==12.2.0 - kind: uv package: pyyaml==6.0.3 --- # Agentic Case Study Skill ## Purpose Create case studies, testimonials, and proof assets after CompleteTech LLC agentic development delivery, using only verified and client-approved facts. ## System Boundary This skill owns approved outcome packaging and reusable proof after delivery. Use `agentic-delivery-skill` for raw delivery evidence, `agentic-customer-success-skill` for relationship and approval routing, and `agentic-email-skill` only for messages that request approval or share the proof. Do not turn internal notes, unapproved metrics, private client details, or speculative outcomes into public proof. ## Core Workflow 1. Identify the proof need: intake, interview, internal brief, anonymized/public case study, sales asset, social post, quote, referral, award, press, or approval checklist. 2. Gather verified facts: client approval status, workflow, before state, implementation, controls, evaluation evidence, qualitative observations, measured outcomes, quotes, confidentiality constraints, and allowed attribution. 3. Use `references/use-case-decision-table.md` to choose the right proof artifact. 4. Use `references/proof-positioning.md` for CompleteTech LLC proof language, evidence rules, and anonymization guardrails. 5. Use `references/proof-catalog.md` for the near-exhaustive proof asset library. 6. Distinguish measured outcomes from qualitative observations. Do not fabricate ROI, savings, approvals, quotes, metrics, regulated-use assurances, legal claims, or client facts. ## Proof Asset Selection Guide - Starting proof collection: use `case-study-intake-questionnaire`. - Interviewing client stakeholders: use `client-outcome-interview-guide`. - Internal sales enablement: use `internal-case-study-brief`. - Confidential client or sensitive details: use `anonymized-case-study`. - Client approved public attribution: use `public-named-client-case-study`. - Show process change: use `before-after-workflow-summary`. - Tell delivery narrative: use `implementation-story`. - Technical buyer proof: use `technical-implementation-note`. - Governance proof: use `risk-control-summary`. - Approval workflow proof: use `human-approval-gate-summary`. - Evaluation evidence: use `evaluation-results-summary`. - Ask for testimonial: use `testimonial-request`. - Draft testimonial for approval: use `testimonial-draft`. - Build reusable proof snippets: use `proof-point-library`. - Sales handout: use `sales-one-pager`. - Website page: use `website-case-study`. - Social post: use `linkedin-post`. - Nurture campaign: use `email-nurture-story`. - Referral ask: use `referral-enablement-blurb`. - Portfolio listing: use `portfolio-entry`. - Speaking or podcast outreach: use `speaker-podcast-pitch`. - Award entry: use `award-submission-draft`. - Press announcement: use `press-release-draft`. - Quote approval: use [REDACTED_SECRET]. - Confidentiality review: use [REDACTED_SECRET]. - Short proof for proposal/email reuse: use `micro-proof-snippet`. - Internal proof QA: use `proof-approval-status-tracker`. When several artifacts fit, choose the safest asset that matches approval status. If client approval is unknown, use internal or anonymized artifacts and run the approval/anonymization checklist before public use. ## Quality Rules - Use only verified, client-approved facts. - Separate measured outcomes from qualitative observations. - Preserve confidential details and anonymize when required. - Keep the CompleteTech LLC frame: bounded workflow implementation, human approval gates, evaluation, monitoring, documentation, support, and handoff. - Do not imply regulated-use approval, legal conclusions, or guaranteed outcomes. - Use `TBD`, "not approved for public use", or "client approval required" where facts or permissions are missing. ## Resource Guide - `references/proof-positioning.md`: load for evidence rules, brand language, and anonymization boundaries. - `references/use-case-decision-table.md`: load when choosing a proof artifact. - `references/proof-lifecycle.md`: load for flow from outcome collection through public proof approval. - `references/proof-catalog.md`: load for the near-exhaustive proof asset templates. - `references/template-index.json`: machine-readable template metadata used by the renderer. - `scripts/render_proof.py`: list proof assets or render a draft with placeholders. ## Runtime Permissions This skill needs local filesystem access only for its documented renderer workflow: - Reads bundled templates, references, examples, `assets/logo.png`, and user-provided Markdown or variable inputs. - Writes only to the user-selected `--out`, `--png`, `--markdown-out`, or default `output/` artifact paths. - Runs local Python entry points `scripts/render_proof.py` and `scripts/render_pdf.py`. - Does not require network access, credential access, persistence, privilege escalation, or destructive file operations. ## Renderer ```bash python3 scripts/render_proof.py --list python3 scripts/render_proof.py --stage case_study --list python3 scripts/render_proof.py --template anonymized-case-study --var workflow="support triage" ``` Rendered artifacts are drafts. Replace placeholders with verified facts and confirm approval status before publishing or sending externally. ## Rendering to a Branded PDF 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: ```bash pip install -r requirements.txt python3 scripts/render_proof.py --template public-named-client-case-study \ --out artifact.pdf --png artifact.png \ --title "Customer Support Email Triage Agent" --doc-type "CLIENT CASE STUDY" \ --subtitle "Northwind Trading Co. × CompleteTech LLC" --meta "CASE NO.=CASE-2026-007" --meta "DATE=2026-07-01" \ --var client_name="Client Name" --var workflow="support triage" ``` - `--no-pdf` emits Markdown only (the original behavior); `--no-cover` drops the cover page. - Already drafted the Markdown yourself? Render it directly: `python3 scripts/render_pdf.py --markdown artifact.md --out artifact.pdf --logo assets/logo.png --title "..."`. - 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. ## Network Boundary This skill is local-only. It does not include outbound network helpers, callbacks, or any helper that posts case-study run metadata to an external service.

path: agents/openai.yaml
content: interface:
  display_name: "Agentic Case Study Skill"
  short_description: "Create agentic proof assets"
  default_prompt: "Use $agentic-case-study-skill to choose and draft the right case study, testimonial, or proof asset for a completed agentic development engagement."

policy:
  allow_implicit_invocation: false


path: BRAND_ASSETS.md
content: # Brand Assets

CompleteTech LLC names, logos, seals, and brand assets included in this repository are not licensed under the MIT License.

They remain trademarks, service marks, or brand assets of CompleteTech LLC and may not be reused in a way that suggests endorsement, affiliation, sponsorship, or ownership without written permission.

The logo is included so this repository can identify itself as part of the CompleteTech LLC agentic services skill library.


path: examples/README.md
content: # Examples

This directory is reserved for runnable example inputs, overrides, or notes for this skill.

Rendered demonstration artifacts live under `assets/examples/` so README previews and packaged outputs stay close to the visual assets they reference.


path: pyproject.toml
content: [tool.ruff]
line-length = 120
target-version = "py312"
exclude = [
  ".git",
  ".venv",
  "venv",
  "env",
  "ENV",
  "output",
  "preview",
]

[tool.ruff.lint]
select = ["E4", "E7", "E9", "F"]


path: requirements.txt
content: # Branded PDF artifact rendering (scripts/render_pdf.py)
reportlab==4.5.1
pyyaml==6.0.3
# Optional: only needed to also emit a PNG preview montage (scripts/render_pdf.py --png)
pypdfium2==5.8.0
pillow==12.2.0


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)