Source profileQuality 89/100Review permissions

tikalk/adlc-team-skills/skills/levelup/levelup-clarify/SKILL.md

levelup-clarify

Review, accept, reject, or defer Context Directive Records (CDRs) discovered by levelup-init or proposed by levelup-specify. Interactive one-CDR-at-a-time workflow.

Source repository stars
97
Declared platforms
0
Static risk flags
2
Last source update
2026-08-05
Source checked
2026-08-05

Decision brief

What it does—and where it fits

Review, accept, reject, or defer Context Directive Records (CDRs) discovered by levelup-init or proposed by levelup-specify. Interactive one-CDR-at-a-time workflow.

Best for

  • After /levelup-init: Validate brownfield discoveries
  • After /levelup-specify: Review proposed feature learnings
  • After /team-repair found conflicts: Resolve conflict CDRs created by repair

Not for

  • Tasks that require unconfirmed production actions or broad system permissions.
  • Environments where the pinned source and install steps cannot be inspected.

Compatibility matrix

Platform support, with evidence labels

PlatformStatusEvidenceWhat to check
CodexNot declaredNo explicit evidencePortability before use
Claude CodeNot declaredNo explicit evidencePortability before use
CursorNot declaredNo explicit evidencePortability before use
Gemini CLINot declaredNo explicit evidencePortability before use
Open the compatibility checker

Installation

Inspect first. Install second.

The source command is displayed only when detected. A safe inspection prompt is always available so your agent can explain every action before execution.

Source-detected install commandSource
npx skills add https://github.com/tikalk/adlc-team-skills --skill "skills/levelup/levelup-clarify"
Safe inspection promptEditorial

Inspect the Agent Skill "levelup-clarify" from https://github.com/tikalk/adlc-team-skills/blob/a6ea2fd3d9cf46c5cba9ff384e1099ce62481b8b/skills/levelup/levelup-clarify/SKILL.md at commit a6ea2fd3d9cf46c5cba9ff384e1099ce62481b8b. List every install step, command, network request, credential, file read/write, external action, and rollback step. Explain whether it fits my task. Do not install or execute anything until I approve.

Workflow

What the source asks the agent to do

  1. 01

    Process

    You MUST consider the user input before proceeding (if not empty).

    "CDR-001 CDR-003" — Focus on specific CDRs"rules" — Clarify only rule-type CDRs"all" — Clarify all pending CDRs
  2. 02

    Phase 1: Load Pending CDRs

    Read all {REPOROOT}/.adlc/drafts/cdr/CDR-.md files and filter:

    Include: Status: Discovered or Status: ProposedSkip: Status: Accepted, Rejected, DeprecatedREPOROOT — walk up from cwd to find .adlc/, or git rev-parse --show-toplevel.
  3. 03

    Phase 2: Pre-Validation

    For each pending CDR, check required sections:

    ContextDecisionEvidence
  4. 04

    Phase 2a: Evals Regression Gate

    Default: ON. Use --no-evals-gate to disable.

    Every case has a concrete evidence reference (session description or code file:line)Pass/fail is binary (no ambiguous cases)Eval CDR references its paired directive CDR
  5. 05

    Phase 3: Gap Identification

    Review the “Phase 3: Gap Identification” section in the pinned source before continuing.

    Review and apply the “Phase 3: Gap Identification” source section.

Permission review

Static risk signals and limitations

Runs scripts

medium · line 91

The documentation asks the agent to run terminal commands or scripts.

Run setup script:

Writes files

medium · line 302

The documentation asks the agent to create, modify, or delete local files.

After EACH CDR interaction, immediately update the file. Do not batch at the end.

Writes files

medium · line 353

The documentation asks the agent to create, modify, or delete local files.

Update CDR file after each decision

Evidence record

Why each signal appears

EvidenceSourceComputedTestedEditorial
SignalValueEvidence typeMeaning
Quality score89/100ComputedDocumentation, specificity, maintenance, and trust rules
Repository stars97SourceRepository attention, not individual Skill quality
Compatibility0 platformsSourceDeclared in the catalog source record
Usage guideautomated source guideEditorialGenerated or reviewed according to the visible evidence level

Pinned source

Provenance and original SKILL.md

Repository
tikalk/adlc-team-skills
Skill path
skills/levelup/levelup-clarify/SKILL.md
Commit
a6ea2fd3d9cf46c5cba9ff384e1099ce62481b8b
License
MIT
Collected
2026-08-05
Default branch
main
View the original SKILL.md

levelup-clarify

What this skill does

Review pending CDRs (status Discovered or Proposed) and decide their fate: Accepted, Rejected, or Deferred.

This is the quality gate for all contributions to team-ai-directives:

  • Validate that patterns are team-wide (not project-specific)
  • Check for duplicates against existing team-ai-directives
  • Ensure CDRs have clear context, decision, and evidence
  • Update CDR statuses in {REPO_ROOT}/.adlc/drafts/cdr/CDR-{NNN}.md
  • Regenerate {REPO_ROOT}/.adlc/drafts/cdr/cdr.md index

This is an interactive command. Present exactly one CDR per interaction and wait for user input.

When to use

  • After /levelup-init: Validate brownfield discoveries
  • After /levelup-specify: Review proposed feature learnings
  • After /team-repair found conflicts: Resolve conflict CDRs created by repair
  • Periodic review: Clean up stale pending CDRs

When NOT to use

  • No pending CDRs: If no CDRs have status Discovered/Proposed, there is nothing to clarify
  • Direct editing: Do not use this skill to bypass the review workflow
  • Routine health checks: Use /team-repair for team AI directives maintenance

Process

User Input

$ARGUMENTS

You MUST consider the user input before proceeding (if not empty).

Examples of User Input:

  • "CDR-001 CDR-003" — Focus on specific CDRs
  • "rules" — Clarify only rule-type CDRs
  • "all" — Clarify all pending CDRs
  • Empty input: Clarify all CDRs with status "Discovered" or "Proposed"

Flags

  • --all: Clarify all pending CDRs (same as empty input)
  • --type TYPE: Filter by context type (rules, personas, examples, skills, constitution, evals)
  • --limit N: Limit to N clarifications per session (default: 5)
  • --no-evals-gate: Disable the evals regression gate (default: gate is ON)

Role & Context

You are acting as a Context Validator reviewing discovered patterns. Your role involves:

  • Validating that patterns are still relevant
  • Clarifying scope (team-wide vs project-specific)
  • Checking against existing team-ai-directives for overlap
  • Refining CDR content through targeted questions

CDR Quality Checklist

Each CDR should have:

  • Clear context explaining the pattern
  • Explicit decision statement
  • Evidence from codebase or feature
  • Target module path well-formed
  • Status is accurate
  • No conflicts with existing directives
  • Team-wide applicability

Outline

  1. Load Pending CDRs (Phase 1): Parse CDR files with status Discovered/Proposed
  2. Pre-Validation (Phase 2): Skip CDRs missing required sections
  3. Evals Regression Gate (Phase 2a): Run existing goldensets before accepting (default ON)
  4. Gap Identification (Phase 3): List clarification needs
  5. Sequential Clarification (Phase 4): One CDR per interaction
  6. Update CDRs (Phase 5): Write status and clarification metadata after each decision
  7. Regenerate Index (Phase 6): Update cdr.md
  8. Summary (Phase 7): Present results

Execution Steps

Phase 1: Load Pending CDRs

Run setup script:

scripts/bash/setup-levelup-clarify.sh

Read all {REPO_ROOT}/.adlc/drafts/cdr/CDR-*.md files and filter:

  • Include: ### Status: **Discovered** or ### Status: **Proposed**
  • Skip: ### Status: **Accepted**, **Rejected**, **Deprecated**

If the setup script is unavailable or fails, resolve manually:

  1. REPO_ROOT — walk up from cwd to find .adlc/, or git rev-parse --show-toplevel.
  2. CDR_DRAFTS_DIRREPO_ROOT/.adlc/drafts/cdr
  3. PENDING_COUNTgrep -l '^### Status: \*\*(Discovered|Proposed)\*\*' CDR_DRAFTS_DIR/CDR-*.md | wc -l

If user specified specific CDR IDs or types, filter accordingly.

If no pending CDRs:

No pending CDRs found.
Run /levelup-init or /levelup-specify to create CDRs first.

Phase 2: Pre-Validation

For each pending CDR, check required sections:

  • ### Context
  • ### Decision
  • ### Evidence
  • ### Target Module

Skip invalid CDRs and report:

## Skipped CDRs

| CDR | Issue | Action |
|---|---|---|
| CDR-XXX | Missing Evidence | Add evidence and re-run clarify |

Phase 2a: Evals Regression Gate

Default: ON. Use --no-evals-gate to disable.

When reviewing an eval CDR or a directive CDR that has a paired eval, run the existing goldensets in team-ai-directives/evals/ to check if accepting this CDR would break existing directive compliance tests.

For eval CDRs: Validate eval quality:

  • Every case has a concrete evidence reference (session description or code file:line)
  • Pass/fail is binary (no ambiguous cases)
  • Eval CDR references its paired directive CDR
  • Fail cases have a correction (what should have been done)

For directive CDRs with paired evals: Run regression check:

  1. Read existing goldensets from {TEAM_AI_DIRECTIVES}/evals/
  2. If no existing goldensets → skip gate (no regression possible)
  3. If goldensets exist for the same directive area:
    • Run the existing goldenset cases against the agent via LLM calls
    • If accepting this CDR would modify the directive, check if existing eval cases would still pass
    • If existing evals would fail with the new directive → mark CDR as Blocked (Evals)

Blocked CDRs remain in pending status with a note:

### Clarification

- **Date**: [YYYY-MM-DD]
- **Action**: Blocked (Evals)
- **Reason**: Accepting this CDR would break N existing goldenset cases in evals/{directive-id}/

Report:

## Evals Regression Gate

| CDR | Gate Result | Details |
|---|---|---|
| CDR-001 | PASS | No existing goldensets in scope |
| CDR-002 | BLOCKED | 3 existing cases would fail with modified rule |

Phase 3: Gap Identification

Generate a gap report:

## CDR Clarification Report

| CDR | Title | Gap Type | Severity |
|---|---|---|---|
| CDR-001 | [Title] | Missing scope | HIGH |
| CDR-002 | [Title] | Duplicate check | MEDIUM |

Gap types:

  • Missing scope: Team-wide vs project-specific unclear
  • Unclear validity: Pattern status unknown
  • Duplicate check needed: May overlap existing directives
  • Content incomplete: Missing context/decision/evidence
  • Target module unclear: Module path needs clarification

Phase 4: Sequential Clarification

CRITICAL: Present exactly ONE CDR per interaction. Do NOT:

  • Present multiple CDRs together
  • Auto-select actions
  • Proceed without explicit user input
  • Ask more than one question at a time

Session limit: Default 5 CDRs per session. User can say "done" to exit early.

For each CDR:

## CDR-{ID}: {Title}

**Context Type**: {type}
**Target Module**: {target}
**Current Status**: {status}

### Current Content

**Context**:
{context}

**Decision**:
{decision}

**Evidence**:
{evidence}

### Choose Action

| Option | Action |
|---|---|
| A | **Accept** — Approve for implementation |
| B | **Reject** — Decline with reason |
| C | **Defer** — Skip for now, keep pending |
| D | **Accept all remaining** — Accept this CDR and all pending CDRs without further review |

Reply with your choice (A/B/C/D).

Wait for user input before proceeding.

Action A: Accept

Update the CDR's status line from ### Status: **Proposed** (or **Discovered**) to ### Status: **Accepted**. Add clarification metadata:

### Clarification

- **Date**: [YYYY-MM-DD]
- **Action**: Accepted
- **Rationale**: [summary of discussion]

Action D: Accept All Remaining

Update the current CDR as Accepted (same as Action A). Then iterate through all remaining pending CDRs, mark each as ### Status: **Accepted**, and add clarification metadata:

### Clarification

- **Date**: [YYYY-MM-DD]
- **Action**: Accepted (bulk)
- **Rationale**: Bulk-accepted with user approval during clarify session

Skip the per-CDR presentation for remaining CDRs. Proceed directly to Phase 6 (Regenerate Index) and Phase 7 (Summary).

Action B: Reject

Ask for reason:

### Decision: Reject

| Option | Reason |
|---|---|
| A | Project-specific |
| B | Duplicate of existing directive |
| C | Deprecated/outdated pattern |
| D | Low value |

Reply with your choice.

Update the CDR's status line to ### Status: **Rejected** with reason.

Action C: Defer

Keep status as-is. Add note:

### Clarification

- **Date**: [YYYY-MM-DD]
- **Action**: Deferred
- **Reason**: [need more context / waiting on team / low priority]

Phase 5: Update CDR Files

After EACH CDR interaction, immediately update the file. Do not batch at the end.

Phase 6: Regenerate Index

Regenerate {REPO_ROOT}/.adlc/drafts/cdr/cdr.md by listing all CDR-*.md files and building a markdown table. For each CDR, extract the single-line fields (### Target Module:, ### Context Type:, ### Status:, ### Date:, ### Descriptor:) and build the index table. See /levelup-specify Phase 5 for the full format.

Phase 7: Summary

## LevelUp Clarify Summary

**CDRs Reviewed**: N
**Accepted**: N
**Rejected**: N
**Deferred**: N

### Accepted (Ready for Implementation)

| CDR | Target Module | Type |
|---|---|---|
| CDR-001 | rules/python/error-handling | Rule |

### Rejected

| CDR | Reason |
|---|---|
| CDR-003 | Project-specific |

### Deferred

| CDR | Title |
|---|---|
| CDR-004 | [Title] |

### Next Steps

1. **Accepted**: Run `/levelup-publish`
2. **Deferred**: Will appear in next clarify session
3. **Remaining**: Run `/levelup-clarify` again to continue

Key Rules

One-at-a-Time

  • Present exactly ONE CDR per response
  • Ask exactly ONE question per response
  • Wait for user input before proceeding

Immediate Writes

  • Update CDR file after each decision
  • Regenerate index after session ends

No Auto-Approval

  • Never accept or reject without explicit user choice
  • Do not assume user preference

Session Limits

  • Default limit: 5 CDRs per session
  • Honor --limit N if provided
  • User can say "done" to exit early

Workflow Guidance & Transitions

After /levelup-clarify

If any CDRs were Accepted, handoff to /levelup-publish:

{
  "command": "clarify",
  "accepted": ["CDR-001", "CDR-002"],
  "rejected": ["CDR-003"],
  "deferred": ["CDR-004"]
}

Complete Clarify Flow

[Pending CDRs exist]
    ↓
/levelup-clarify
    ↓
[One CDR at a time] → Accept / Reject / Defer
    ↓
[Run /levelup-publish] → Compile accepted CDRs

Next Steps

After accepting CDRs, run /levelup-publish to compile them into a team-ai-directives PR.

Verification

  • All reviewed CDR files updated with new status and clarification metadata.
  • cdr.md index regenerated.
  • Accepted CDRs are ready for /levelup-publish.
  • No CDRs were auto-accepted or auto-rejected without user input.

Context

$ARGUMENTS

Alternatives

Compare before choosing

Computed 10023,835

alirezarezvani/claude-skills

app-store-optimization

App Store Optimization (ASO) toolkit for researching keywords, analyzing competitor rankings, generating metadata suggestions, and improving app visibility on Apple App Store and Google Play Store. Use when the user asks about ASO, app store rankings, app metadata, app titles and descriptions, app store listings, app visibility, or mobile app marketing on iOS or Android. Supports keyword research and scoring, competitor keyword analysis, metadata optimization, A/B test planning, launch checklist

Computed 10014,533

prowler-cloud/prowler

postgresql-indexing

PostgreSQL indexing best practices for Prowler: index design, partial indexes, partitioned table indexing, EXPLAIN ANALYZE validation, concurrent operations, monitoring, and maintenance. Trigger: When creating or modifying PostgreSQL indexes, analyzing query performance with EXPLAIN, debugging slow queries, reviewing index usage statistics, reindexing, dropping indexes, or working with partitioned table indexes. Also trigger when discussing index strategies, partial indexes, or index maintenance

Computed 10014,251

wanshuiyin/Auto-claude-code-research-in-sleep

citation-audit

Use it for operations and research tasks; the detail page covers purpose, installation, and practical steps.

Computed 9832,671

K-Dense-AI/scientific-agent-skills

dask

Distributed computing for larger-than-RAM pandas/NumPy workflows. Use when you need to scale existing pandas/NumPy code beyond memory or across clusters. Best for parallel file processing, distributed ML, integration with existing pandas code. For out-of-core analytics on single machine use vaex; for in-memory speed use polars.