Borrowing it
Nothing to install: this file belongs to shep-ai/shep. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/shep-ai/shep/main/.claude/skills/shep-kit-new-feature/SKILL.mdgit clone --depth 1 https://github.com/shep-ai/shepWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/shep-ai/shep/shep-kit-new-feature)<a href="https://agentmods.dev/skills/shep-ai/shep/shep-kit-new-feature"><img src="https://agentmods.dev/badge/skills/shep-ai/shep/shep-kit-new-feature/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/shep-ai/shep/shep-kit-new-feature"><img src="https://agentmods.dev/badge/skills/shep-ai/shep/shep-kit-new-feature.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00071 | $0.01330 |
| Opus 5 | $0.00036 | $0.00665 |
| Sonnet 5 | $0.00014 | $0.00266 |
| Haiku 4.5 | $0.00007 | $0.00133 |
Grade A, and why
shep-kit:new-feature scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 172 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Create New Feature Specification
Start spec-driven development by creating a feature branch and specification directory.
Full workflow guide: docs/development/spec-driven-workflow.md
Phase Lifecycle
Requirements → Research → Planning → Implementation → Complete
↓ ↓ ↓ ↓ ↓
spec.yaml research.yaml plan.yaml tasks.yaml all files
↓ ↓ ↓ ↓ ↓
spec.md research.md plan.md tasks.md (auto-generated)
CRITICAL: Each phase MUST update the Phase status field before proceeding.
IMPORTANT: Edit YAML files, not Markdown.
Workflow
1. Gather Minimal Input
Ask the user for:
- Feature name (kebab-case, e.g.,
user-authentication) - One-liner description (brief summary)
2. Create Branch
# Determine next number
NEXT_NUM=$(ls -d specs/[0-9][0-9][0-9]-* 2>/dev/null | wc -l | xargs printf "%03d" $(($ + 1)))
# If no specs exist, use 001
[ -z "$NEXT_NUM" ] && NEXT_NUM="001"
# Create branch from main
git checkout main && git pull
git checkout -b "feat/${NEXT_NUM}-${FEATURE_NAME}"
3. Run Init Script
Execute the scaffolding script:
.claude/skills/shep-kit-new-feature/scripts/init-feature.sh <NNN> <feature-name>
This creates specs/NNN-feature-name/ with all template files using a YAML-first approach:
- YAML source files:
spec.yaml,research.yaml,plan.yaml,tasks.yaml(source of truth) - Markdown files:
spec.md,research.md,plan.md,tasks.md(auto-generated from YAML) - Status tracking:
feature.yaml(implementation status, unchanged)
4. Analyze Context
Before filling the spec, analyze:
- Existing specs: Read
specs/*/spec.yaml(orspecs/*/spec.md) to understand feature landscape and discover dependencies - Codebase: Identify affected areas, patterns, existing implementations
- Cross-reference: Infer dependencies, impact areas, size estimate
What ships with it
8 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 172 lines · 71 tokens per session scan A 6b969b0824f0
shep-kit:new-feature is a skill published in the GitHub repository shep-ai/shep (251 stars, last pushed 4d ago), licensed MIT. It adds 71 tokens to every session and 1,330 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
parallel-feature-development
Coordinate parallel feature development with file ownership strategies, conflict avoidance rules, and integration patterns for multi-agent implementation. Use this skill when decomposing a large feature into independent work streams, when two or more agents need to implement different layers of the same system…
nft-standards
Implement NFT standards (ERC-721, ERC-1155) with proper metadata handling, minting strategies, and marketplace integration. Use when creating NFT contracts, building NFT marketplaces, or implementing digital asset systems.
cost-optimization
Optimize cloud costs across AWS, Azure, GCP, and OCI through resource rightsizing, tagging strategies, reserved instances, and spending analysis. Use when reducing cloud expenses, analyzing infrastructure costs, or implementing cost governance policies.
istio-traffic-management
Configure Istio traffic management including routing, load balancing, circuit breakers, and canary deployments. Use when implementing service mesh traffic policies, progressive delivery, or resilience patterns.
postgresql-table-design
Use this skill when designing or reviewing a PostgreSQL-specific schema. Covers best-practices, data types, indexing, constraints, performance patterns, and advanced features.
spark-training-gotchas
Preflight and diagnose the ten known failure modes for ML training on NVIDIA DGX Spark. Use when a training run on DGX Spark fails to start, OOMs below the 128GB limit, slows down mid-run, or before any multi-hour training job on GB10.