feature-dev
This skill should be used when the user asks to "implement a feature", "add a new feature", "develop feature", "refactor module", "upgrade API", "migrate database", or invokes "/feature". Also triggers on complex multi-file bugfixes, architectural changes, or when user mentions "feature workflow". Provides a 5-phase systematic approach: Discovery -> Exploration -> Documentation -> Implementation -> Review.
What this skill does
# Feature Development Workflow
> Systematic feature development: Discovery → Exploration → Documentation → Implementation → Review
## Overview
A comprehensive 5-phase workflow for feature development that emphasizes:
- **Documentation-first**: Spec documents before code
- **Progressive context**: Fork context prevents main session pollution
- **Progress persistence**: Resume interrupted work seamlessly
- **Multi-model agents**: Right model for each task (Haiku/Sonnet/Opus)
## Hard Rules
- Speak with user in **Simplified Chinese**, write all artifacts in **English**
- Do not skip decision gates without explicit user confirmation
- Keep diffs minimal; avoid unrelated refactors
- Update progress.md after each phase transition
- Run verification before marking tasks complete
## Workflow Overview
```
Phase 1: Discovery -> Phase 2: Exploration -> Phase 3: Documentation
(clarify & decide) (understand codebase) (contracts & specs)
| | |
v v v
[GATE: confirm] [parallel agents] [GATE: approve]
|
v
Phase 3c: Readiness Gate (NEW - 95% check)
(structural + semantic scoring)
|
v
Phase 4: Implementation <- Phase 3b: Tasks
(incremental, verified) (breakdown)
|
v
Phase 5: Review & PR
```
## File Structure
All artifacts live under `.works/spec/{feature-name}/` (gitignored):
```
.works/spec/{feature-name}/
├── progress.md # Progress tracking (YAML frontmatter + markdown)
├── README.md # Index + core decisions
├── contracts.md # Data contracts (input/output/API)
├── tasks.md # Task breakdown with status
├── PR.md # PR description template
├── qa.md # Q&A log (Phase 3c question rounds)
├── score.json # Readiness scoring results (Track A)
├── semantic-check.json # Semantic check results (Track B)
├── verification.log # Test/build output history
├── assumptions.md # Assumptions pack (if question budget exhausted)
└── spec.lock # Spec locked marker (created when gate passes)
```
Initialize with: `bash ${CLAUDE_PLUGIN_ROOT}/scripts/init.sh {feature-name}`
---
## Phase 1: Discovery
**Goal**: Transform vague requirements into actionable decisions
**Agents**: `product-thinker` (Opus) for deep product analysis
**Actions**:
1. Initialize workspace with `init.sh {feature-name}`
2. Create progress.md with initial state
3. Ask clarifying questions using numbered format (Q1, Q2...)
4. Record decisions in README.md
**GATE**: Confirm all decisions with user before proceeding.
---
## Phase 2: Codebase Exploration
**Goal**: Understand relevant existing code and patterns
**Agents**: `codebase-explorer` (Haiku, fast) - launch 2-3 in parallel
**Actions**:
1. Launch parallel agents targeting:
- Similar features and implementation patterns
- Architecture and abstractions
- UI patterns and state management
2. Read all key files identified by agents
3. Present comprehensive findings summary
4. Update progress.md
5. if the `ast-grep` tool is exsiting, please use it. here is the Command line usage example :
```bash
## ast-grep has following form.
ast-grep --pattern 'var code = $PATTERN' --rewrite 'let code = new $PATTERN' --lang ts
## Example : Rewrite code in null coalescing operator
ast-grep -p '$A && $A()' -l ts -r '$A?.()'
## Example : Rewrite Zodios
ast-grep -p 'new Zodios($URL, $CONF as const,)' -l ts -r 'new Zodios($URL, $CONF)' -i
```
---
## Phase 3: Documentation & Tasks
**Goal**: Create spec documents and task breakdown
**Agents**: `architect` (Sonnet) for implementation blueprint
### Sub-phase 3a: Documentation
1. Write contracts.md with field definitions, types, examples
2. Update README.md with decisions summary
**GATE**: User approves spec documents
### Sub-phase 3b: Task Breakdown
1. Launch `architect` agent for implementation design
2. Split feature into atomic, verifiable tasks in tasks.md
3. Each task: Goal, Size (S/M/L), Files, Acceptance criteria
**GATE**: User confirms task list
---
## Phase 3c: Readiness Gate (95% Confidence)
**Goal**: Verify specification completeness before implementation
**CRITICAL**: Do not proceed to implementation until this gate passes.
### Dual-Track Scoring
The readiness gate uses two complementary checks:
1. **Track A: Structural Scoring** (Deterministic)
- Checks for required sections, type definitions, test commands
- Scores 0-100 based on concrete rubric
- Fast, consistent, no API calls needed
2. **Track B: Semantic Sufficiency** (LLM-based)
- Checks for ambiguity, hidden assumptions, logical gaps
- Requires evidence citations from spec
- Uses semantic-checker agent (in-session model)
### Execution Steps
**1. Run structural scoring**:
```bash
bun run ${CLAUDE_PLUGIN_ROOT}/scripts/score-spec.ts \
--feature {feature-name} \
--threshold 95
```
Result written to `.works/spec/{feature}/score.json`
**2. Run semantic check**:
- Use Task tool to launch `semantic-checker` agent
- Provide README.md, contracts.md, tasks.md as context
- Write the JSON output to `.works/spec/{feature}/semantic-check.json`
**3. Evaluate gate condition**:
- `readiness_score >= 95` AND `semantic_ok == true`
- If **PASS**: Create `spec.lock` file, update progress, proceed to Phase 4
- If **FAIL**: Go to step 4
**4. Information Gathering (if gate fails)**:
a. Generate blocking questions list:
- Combine gaps from `score.json` + `semantic-check.json`
- Filter to blocking items only
- Max 3-5 questions per round
b. Check question budget:
- If `question_round < question_budget` (default: 2):
- Use AskUserQuestion to ask targeted questions
- Record Q&A in `qa.md`
- Update `question_round` in progress.md
- Re-run readiness gate (steps 1-3)
- If budget exhausted: Go to step 5
**5. Assumptions Pack (budget exhausted)**:
Write to `.works/spec/{feature}/assumptions.md`:
```markdown
## Unresolved Gaps
[List blocking gaps from score.json + semantic-check.json]
## Proposed Assumptions
- Gap: [description]
Assumption: [what we'll assume]
Risk: [what could go wrong]
## Acceptance
[ ] User accepts assumptions (allows Phase 4)
[ ] User rejects (requires more info or cancels feature)
```
Use AskUserQuestion with 2 options:
- "Accept assumptions and proceed (Recommended if risks are acceptable)"
- "Provide more information (will ask targeted questions)"
**If accepted**: Mark `assumptions_accepted: true`, create `spec.lock`, proceed to Phase 4
**If rejected**: Output Stuck Report and mark `status: blocked`
**6. Stuck Report format** (if assumptions rejected):
```markdown
# Feature Blocked: {feature-name}
## Blocking Gaps
[List from score.json + semantic-check.json]
## Information Needed
[Specific questions that must be answered]
## Attempts Made
- Question rounds: {question_round}/{question_budget}
- Assumptions offered: Yes
- User response: Rejected assumptions
## Recommendation
Please provide answers to "Information Needed" section, then:
1. Update contracts.md and/or tasks.md with missing info
2. Re-run readiness gate: bun run scripts/score-spec.ts --feature {feature-name}
```
### Gate Pass Criteria
- **Track A**: `readiness_score >= confidence_threshold` (default 95)
- **Track B**: `semantic_ok == true`
- **Combined**: Both must pass
### Post-Gate Actions
When gate passes:
1. Create `.works/spec/{feature}/spec.lock` (empty file, timRelated in Backend & APIs
jfrog
IncludedInteract with the JFrog Platform via the JFrog CLI and REST/GraphQL APIs. Use this skill when the user wants to manage Artifactory repositories, upload or download artifacts, manage builds, configure permissions, manage users and groups, work with access tokens, configure JFrog CLI servers, search artifacts, manage properties, set up replication, manage JFrog Projects, run security audits or scans, look up CVE details, query exposures scan results from JFrog Advanced Security, manage release bundles and lifecycle operations, aggregate or export platform data, or perform any JFrog Platform administration task. Also use when the user mentions jf, jfrog, artifactory, xray, distribution, evidence, apptrust, onemodel, graphql, workers, mission control, curation, advanced security, exposures, or any JFrog product name.
cupynumeric-migration-readiness
IncludedPre-migration readiness assessor for porting NumPy to cuPyNumeric. Use BEFORE substantial porting work begins when the user asks whether code will scale on GPU, whether they should migrate to cuPyNumeric, which NumPy patterns transfer cleanly, what must be refactored before porting, or mentions pre-port assessment, scaling analysis, or refactor planning. Inspect the user's source code, look up NumPy usage, cross-reference the cuPyNumeric API support manifest, and distinguish distributed-scaling-friendly patterns from blockers such as unsupported APIs, scalar synchronization, host round-trips, Python/object-heavy control flow, shape/data-dependent branching, and in-place mutation hazards. Produce a verdict of READY, LIGHT REFACTOR, SIGNIFICANT REFACTOR, or NOT RECOMMENDED, with concrete refactor pointers.
alibabacloud-data-agent-skill
IncludedInvoke Alibaba Cloud Apsara Data Agent for Analytics via CLI to perform natural language-driven data analysis on enterprise databases. Data Agent for Analytics is an intelligent data analysis agent developed by Alibaba Cloud Database team for enterprise users. It automatically completes requirement analysis, data understanding, analysis insights, and report generation based on natural language descriptions. This tool supports: discovering data resources (instances/databases/tables) managed in DMS, initiating query or deep analysis sessions, real-time progress tracking, and retrieving analysis conclusions and generated reports. Use this Skill when users need to query databases, analyze data trends, generate data reports, ask questions in natural language, or mention "Data Agent", "data analysis", "database query", "SQL analysis", "data insights".
token-optimizer
IncludedReduce OpenClaw token usage and API costs through smart model routing, heartbeat optimization, budget tracking, and native 2026.2.15 features (session pruning, bootstrap size limits, cache TTL alignment). Use when token costs are high, API rate limits are being hit, or hosting multiple agents at scale. The 4 executable scripts (context_optimizer, model_router, heartbeat_optimizer, token_tracker) are local-only — no network requests, no subprocess calls, no system modifications. Reference files (PROVIDERS.md, config-patches.json) document optional multi-provider strategies that require external API keys and network access if you choose to use them. See SECURITY.md for full breakdown.
resend-cli
IncludedUse this skill when the task is specifically about operating Resend from an AI agent, terminal session, or CI job via the official resend CLI: installing/authenticating the CLI, sending/listing/updating/cancelling emails, batch sends, domains and DNS, webhooks and local listeners, inbound receiving, contacts, topics, segments, broadcasts, templates, API keys, profiles, or debugging Resend CLI/API failures. Trigger on mentions of Resend CLI, `resend`, `resend doctor`, `resend emails send`, `resend domains`, `resend webhooks listen`, `resend emails receiving`, or agent-friendly terminal automation.
alibabacloud-odps-maxframe-coding
IncludedUse this skill for MaxFrame SDK development and documentation navigation on Alibaba Cloud MaxCompute (ODPS). Helps answer MaxFrame API, concept, official example, and supported pandas API questions; create data processing programs; read/write MaxCompute tables; debug jobs (remote or local); and build custom DPE runtime images. Trigger when users mention MaxFrame, MaxCompute with MaxFrame, ODPS table processing, DPE runtime, MaxFrame docs/examples, DataFrame/Tensor operations, or GPU runtime setup. Works for both English and Chinese queries about Alibaba Cloud data processing with MaxFrame.