By ehlyzov
End-to-end product shaping, scenarios, implementation planning, hardening, validation, and stakeholder PDF packaging.
Use this prompt in Phase 0 discovery for API and contract analysis.
Use this prompt in Phase 0 discovery for runtime, verification, and operational constraints.
Use this prompt in Phase 0 discovery for user-facing surfaces.
Use this prompt in Phase 4 to generate H-tasks.
Use this prompt in Phase 8 after T/H-plan execution.
Uses power tools
Uses Bash, Write, or Edit tools
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Verify ownership to unlock analytics, metadata editing, and a verified badge. GitHub access is read-only (username + org membership).
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Verify ownership to unlock analytics, metadata editing, and a verified badge. GitHub access is read-only (username + org membership).
Sign in to claimBased on adoption, maintenance, documentation, and repository signals. Not a security audit or endorsement.
Language: English | Русский
This repository contains ready-to-install Codex skills: self-contained folders
with SKILL.md, agent prompt files, references, scripts, and tests.
product-workflowEnd-to-end product shaping: discovery, decision log, PRD, user scenarios, current-scenario baseline, feature impact analysis, implementation plan, hardening plan, independent verification, editorial pass, and stakeholder-facing PDF.
Use it for product or feature descriptions, roadmap validation, scenario design,
and implementation planning. For existing products, the baseline mode captures
current scenarios as current-scenario-baseline.md, scenario-cards.md, and
scenario-graph.dot; new increments then declare affected scenarios through
pre-scan and impact artifacts before implementation planning. By default, the
PDF includes only the product problem, scenarios, chosen solution, and
independent verdict; T/H plans remain engineering artifacts.
flowchart TD
U["User vision"] --> D["Phase 0: discovery agents"]
D --> DL["Decision log: proposed / approved / delegated"]
DL --> H{"Human approval?"}
H -->|approved| S["Scenarios + overview"]
H -->|needs choice| U
S --> B["Baseline: scenario cards + DOT graph"]
B --> I["Increment pre-scan + impact"]
I --> C["Scenario critic loop"]
C --> P["Implementation plan with Product artifacts"]
P --> HP["Hardening plan"]
HP --> V["Independent artifact verifier"]
V -->|blockers| C
V -->|approved verdict| E["Editorial style pass"]
E --> PDF["Product PDF"]
P --> IV["Post-implementation verifier"]
HP --> IV
Contents:
SKILL.md — main workflow and gates.agents/ — discovery, critic, verifier, and style-editor prompts.references/ — templates for scenarios, baseline, scenario cards,
increment pre-scan/impact, decision log, plans, and PDF.scripts/verify_artifacts.py — structural checks for scenarios,
baseline/pre-scan/impact artifacts, plans, hardening plans, and validation
gate.scripts/build_pdf.sh — PDF assembly with mandatory independent validation.evals/ — expected-behavior eval set.service-knowledge-contourMinimal knowledge contour for one service repository: startup docs, canonical
SERVICE_MAP.md / VERIFY.md, knowledge-gap registry, generated overlays,
audit, promotion, and pruning.
Use it when a service needs a stable operating knowledge layer for humans and agents, onboarding docs are missing or fragmented, or topology, entrypoints, verification commands, integrations, or risk zones changed.
flowchart TD
R["Repository reality"] --> B["bootstrap"]
B --> C["Canonical core"]
C --> G["Generated layer"]
G --> A["audit / strict audit"]
A -->|trigger fired| H{"Human approval needed?"}
H -->|yes| P["promote / repair candidate"]
H -->|no| G
P --> C
C --> V["Independent contour verifier"]
A --> PR["PR / CI evidence"]
Contents:
SKILL.md — service knowledge contour workflow and rules.agents/contour-verifier.md — independent semantic contour verification.bin/ — bootstrap, refresh, audit, promote, and prune shell scripts.examples/ — GitHub Actions and PR template examples.tests/ — bootstrap/audit contract tests.Install a skill by pointing Codex at the repository folder URL.
Ask Codex:
Install the skill from https://github.com/ehlyzov/skills/tree/main/product-workflow
or:
Install the skill from https://github.com/ehlyzov/skills/tree/main/service-knowledge-contour
Manual install:
mkdir -p ~/.codex/skills
cp -R product-workflow ~/.codex/skills/
cp -R service-knowledge-contour ~/.codex/skills/
Update an installed copy:
rm -rf ~/.codex/skills/product-workflow ~/.codex/skills/service-knowledge-contour
cp -R product-workflow service-knowledge-contour ~/.codex/skills/
Verify installation:
test -f ~/.codex/skills/product-workflow/SKILL.md
test -f ~/.codex/skills/service-knowledge-contour/SKILL.md
Add this repository as a Claude Code plugin marketplace:
/plugin marketplace add ehlyzov/skills
or, with the full Git URL:
/plugin marketplace add https://github.com/ehlyzov/skills.git
Then install one or both plugins from the marketplace:
/plugin install product-workflow@knowledge-contour-skills
/plugin install service-knowledge-contour@knowledge-contour-skills
The marketplace file is stored at .claude-plugin/marketplace.json, which is
the path Claude Code expects when adding a GitHub repository as a plugin
marketplace.
Running scripts is separate from installing a skill.
For product-workflow, run scripts from the skill folder or by absolute path to
the installed skill:
npx claudepluginhub ehlyzov/skills --plugin product-workflowMaintain a minimal service knowledge contour with canonical docs, generated overlays, audits, promotion, and pruning.
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