intent-driven-development
Turn ambiguous or high-impact product and engineering changes into scoped, verifiable acceptance criteria before or alongside implementation. Use when a user asks to clarify a feature, define acceptance criteria, de-risk a security/data/migration/integration change, prepare implementation requirements for another agent, or make a complex request testable. Do not trigger for trivial edits, straightforward fixes, active debugging, code review, or implementation requests whose acceptance conditions are already clear unless the user explicitly invokes this skill.
What this skill does
# Intent-Driven Development
Produce useful acceptance criteria without turning specification into ceremony. Inspect
available context first, expose genuine ambiguity, and choose verification methods that fit
the work and its risk.
## When to Activate
- User asks to clarify a feature, define acceptance criteria, or de-risk a change before implementation
- Request touches security, authentication, persistent data, migrations, external APIs, or compliance
- User wants to prepare a handoff artifact for another agent or team
- Request is ambiguous enough that the expected outcome is not yet observable or testable
- User explicitly invokes this skill with `/intent-driven-development`
Do not activate for trivial edits, straightforward one-line fixes, active debugging sessions,
code review requests, or implementation requests whose acceptance conditions are already clear.
## How It Works
1. **Inspect context first** — reads the repository, docs, schemas, and test infrastructure for technical facts before asking any question, while treating product/business constraints as something only the user or a product artifact can supply
2. **Choose depth** — selects Quick Capture (3-7 criteria, low/moderate risk) or Full Acceptance Brief (security, data, migration, cross-system changes) based on the risk profile
3. **Ask minimally** — only asks questions whose answers cannot be inferred and that materially change scope or behavior
4. **Write observable criteria** — each AC-NNN describes a starting condition, trigger, expected outcome, prohibited side effect, verification method, and priority; no vague words like "correctly" or "securely" without evidence
5. **Proceed or hand off** — for clear requests with no blocking risks, records criteria and continues; for risky changes, presents blockers and waits for confirmation
6. **Handle revision** — if an AC fails mid-implementation due to architectural constraints, marks it `[revised]`, updates scope or verification method, increments the revision number, and re-presents only the changed criteria
## Examples
**Quick Capture — "Add CSV export to the dashboard"**
```
Goal: Authenticated users can download dashboard data as a CSV file.
In scope: Export of currently filtered rows; filename includes date.
Out of scope: Scheduled exports, email delivery, Excel format.
Assumptions: Max row count is under 10k; no PII in exported fields.
AC-001: Export generates file with correct headers
- Scenario: authenticated user, at least one data row visible
- Action: click "Export CSV"
- Expected: browser downloads file with columns [id, name, created_at]
- Must not: expose internal fields or rows belonging to other users
- Verification: automated integration test + manual schema spot-check
- Priority: Required
```
**Full Acceptance Brief trigger — "Migrate user auth to OAuth"**
Auth change + external dependency + existing session data → Full Brief with Risk Review table,
blocking decisions on session invalidation strategy, and explicit rollback AC.
**Existing spec review — user pastes a PRD**
Skill reviews it for missing scope boundaries, unverifiable requirements ("the system shall be fast"),
and silent assumptions, then returns corrected or supplemental criteria without restarting discovery.
## Operating Rules
1. Inspect the available repository, documentation, issue, design, and test context before
asking for technical facts that can be discovered locally.
2. Do not infer product or business constraints from code. Business rules, compliance and
regulatory obligations, contractual SLAs, pricing, data-retention policy, prioritization,
and target users cannot be read from a repository. Treat them as unknown until the user
supplies them or an authoritative product artifact (PRD, contract, policy document) states
them. Record them as assumptions flagged for confirmation, never as discovered facts. The
repository tells you how the system behaves today, not what the business requires it to do.
3. Ask only questions whose answers are required and cannot be safely inferred. Group short,
related questions when that saves unnecessary turns.
4. Do not block implementation by default. When the user has asked to implement a sufficiently
clear change, record key assumptions and acceptance criteria briefly, then proceed or hand
them to the implementation workflow.
5. Require explicit user confirmation before proceeding only when an unresolved decision could
create material security exposure, data loss, irreversible migration, contractual/API
breakage, meaningful cost, or destructive external action.
6. Do not write an acceptance document into a repository, alter project files, create a branch,
commit, or invoke another skill unless the user requests it or the active repository
workflow explicitly requires it.
7. Treat automated tests as evidence, not truth. Prefer automation when reliable and
proportionate; allow manual UX, accessibility, security, legal, or operational verification
where automation cannot establish the outcome.
8. Never include real secrets, credentials, tokens, private keys, personal data, or sensitive
production payloads in acceptance criteria, fixtures, examples, or saved artifacts. Use
redacted or synthetic values.
9. Do not run destructive tests, migrations, security probes, load tests, paid external calls,
or operations against production/live data without explicit authorization and an identified
safe environment.
10. When an acceptance criterion cannot be satisfied due to an architectural, platform, or
external constraint discovered during implementation, do not silently drop or workaround it.
Update the affected criterion (mark it `[revised]`, state the constraint, and adjust scope or
verification method), increment the revision number, and re-present only the changed criteria
to the user before continuing. Require explicit confirmation only if the revision changes a
blocking decision or materially reduces safety or correctness guarantees.
## Choose The Depth
Use the smallest useful output.
### Quick Capture
Use for a clear but non-trivial change with low or moderate risk. Produce:
- Goal
- In scope / out of scope
- Assumptions
- 3-7 acceptance criteria with verification methods
- Blocking questions, if any
Do not delay implementation for approval unless a blocking risk from the operating rules
exists or the user specifically asked for a specification first.
### Full Acceptance Brief
Use for ambiguous, cross-system, security-sensitive, data-changing, migration, compliance,
or high-cost changes, or when the user requests a handoff artifact. Produce the full template
below and request confirmation for unresolved blocking decisions before risky implementation.
### Existing Specification Review
When the user already supplied a PRD, issue, plan, or acceptance criteria:
1. Review it instead of restarting discovery.
2. Identify missing scope boundaries, unsafe assumptions, contradictions, and unverifiable
requirements.
3. Return corrected or supplemental criteria.
## Workflow
### 1. Establish Goal And Risk
Extract or ask for:
- The observable outcome for the user or system.
- The actors affected.
- The main failure consequence.
- Risk dimensions that actually apply: security/privacy, persistent data, compatibility/API,
migration, external dependencies, cost, concurrency, performance, usability/accessibility.
Avoid asking generic questions about irrelevant risks.
### 2. Discover Context
When local or connected artifacts are available, inspect only what is needed:
- Existing behavior and directly related files or interfaces.
- Repository conventions, product docs, API contracts, data schemas, or migration history.
- Existing verification infrastructure and realistic commands.
- External dependencies and whether they are testable in isolation.
Record discovered facts separately from user-provided assumptions. If context caRelated in AI Agents
skill-development
IncludedComprehensive meta-skill for creating, managing, validating, auditing, and distributing Claude Code skills and slash commands (unified in v2.1.3+). Provides skill templates, creation workflows, validation patterns, audit checklists, naming conventions, YAML frontmatter guidance, progressive disclosure examples, and best practices lookup. Use when creating new skills, validating existing skills, auditing skill quality, understanding skill architecture, needing skill templates, learning about YAML frontmatter requirements, progressive disclosure patterns, tool restrictions (allowed-tools), skill composition, skill naming conventions, troubleshooting skill activation issues, creating custom slash commands, configuring command frontmatter, using command arguments ($ARGUMENTS, $1, $2), bash execution in commands, file references in commands, command namespacing, plugin commands, MCP slash commands, Skill tool configuration, or deciding between skills vs slash commands. Delegates to docs-management skill for official documentation.
reprompter
IncludedTransform messy prompts into well-structured, effective prompts — single or multi-agent. Use when: "reprompt", "reprompt this", "clean up this prompt", "structure my prompt", rough text needing XML tags and best practices, "reprompter teams", "repromptception", "run with quality", "smart run", "smart agents", multi-agent tasks, audits, parallel work, anything going to agent teams. Don't use when: simple Q&A, pure chat, immediate execution-only tasks. See "Don't Use When" section for details. Outputs: Structured XML/Markdown prompt, quality score (before/after), optional team brief + per-agent sub-prompts, agent team output files. Success criteria: Single mode quality score ≥ 7/10; Repromptception per-agent prompt quality score 8+/10; all required sections present, actionable and specific.
adaptive-compaction
IncludedAdaptive add-on policy and recovery layer that decides WHEN to compact, prune, snapshot, or fork -- replacing fixed-percent auto-compaction across Claude Code, Codex, and MCP-capable hosts. Trigger on auto-compact timing or damage: "when should I compact", "is it safe to compact now or start a fresh session", "auto-compact fires too early/mid-task", "switching to an unrelated task but the window still has space", "context rot", "answers get worse the longer the session runs", "the agent forgot the plan or my decisions after it summarized", "add a layer on top that manages context without changing the agent", raising autoCompactWindow to give the policy room, or installing/tuning a cross-tool compaction policy or PreCompact hook -- even when "compaction" is never said but the problem is context-window pressure or post-summarization memory loss. Do NOT use to summarize a conversation, build RAG, write a summarization prompt (decides WHEN not HOW), or answer max-context-length trivia.
agent-skill-creator
IncludedCreate cross-platform agent skills from workflow descriptions. Activates when users ask to create an agent, automate a repetitive workflow, create a custom skill, or need advanced agent creation. Triggers on phrases like create agent for, automate workflow, create skill for, every day I have to, daily I need to, turn process into agent, need to automate, create a cross-platform skill, validate this skill, export this skill, migrate this skill. Supports single skills, multi-agent suites, transcript processing, template-based creation, interactive configuration, cross-platform export, and spec validation.
llm-wiki
IncludedUse when building or maintaining a persistent personal knowledge base (second brain) in Obsidian where an LLM incrementally ingests sources, updates entity/concept pages, maintains cross-references, and keeps a synthesis current. Triggers include "second brain", "Obsidian wiki", "personal knowledge management", "ingest this paper/article/book", "build a research wiki", "compound knowledge", "Memex", or whenever the user wants knowledge to accumulate across sessions instead of being re-derived by RAG on every query.
skill-master
IncludedAgent Skills authoring, evaluation, and optimization. Create, edit, validate, benchmark, and improve skills following the agentskills.io specification. Use when designing SKILL.md files, structuring skill folders (references, scripts, assets), ingesting external documentation into skills, running trigger evals, benchmarking skill quality, optimizing descriptions, or performing blind A/B comparisons. Keywords: agentskills.io, SKILL.md, skill authoring, eval, benchmark, trigger optimization.