levelup-specify

What this skill does

Extract Context Directive Records (CDRs) from the current session after completing work.

This is the primary command for capturing learnings from completed work:

• Review the current session directly (the agent remembers what it did)
• Identify reusable patterns: rules, personas, examples, skills, and constitution amendments
• For each directive CDR, also extract a paired eval CDR with pass/fail cases from the session
• Link CDRs to concrete implementation evidence (files, commits, tests)
• Write CDRs to {REPO_ROOT}/.adlc/drafts/cdr/CDR-{NNN}.md with status Proposed
• Auto-generate {REPO_ROOT}/.adlc/drafts/cdr/cdr.md index

Key Difference from /levelup-init:

• /levelup-init = Discovers patterns from existing codebase (brownfield)
• /levelup-specify (this skill) = Extracts patterns from the current session (greenfield)

This skill focuses on session-level learnings — what reusable knowledge emerged from the work just completed.

When to use

• After completing work: Capture reusable patterns from the session
• Contributing back to team AI directives: Turn session work into reusable directives
• Before closing a branch: Extract team-wide learnings

When NOT to use

• Brownfield projects: Use /levelup-init to scan existing code
• Mining git history / issue-linked changes: Use /change-init to recover past decisions from commits + issue trackers
• Before work is done: Run this after completing the implementation
• Routine team AI directives validation: Use /team-repair for health checks

Process

User Input

$ARGUMENTS

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

Examples of User Input:

• "Focus on error handling patterns" — Extract CDRs related to error handling
• "Document the testing approach" — Focus on testing patterns
• "CDR-001" — Enrich an existing CDR with more session evidence
• "--focus skills" — Only propose skill-type CDRs
• Empty input: Extract all patterns from the session trace

Flags

• --focus AREA: Focus on specific context type
  • rules: Only propose rule CDRs
  • personas: Only propose persona CDRs
  • examples: Only propose example CDRs
  • constitution: Only propose constitution amendment CDRs
  • skills: Only propose skill CDRs
• --cdr-id ID: Enrich an existing CDR with session evidence instead of creating new ones

Role & Context

You are acting as a Context Extractor — identifying reusable patterns from the current session. Your role involves:

• Reading the current session (the agent directly observes what happened)
• Identifying patterns that would benefit other projects
• Creating CDRs for rules, personas, examples, constitution amendments, or skills
• For each directive CDR, extracting a paired eval CDR with pass/fail cases from the session
• Linking CDRs to implementation evidence (code, commits, tests)

Brownfield vs Greenfield

Scenario	Command	Input	Output
Brownfield (existing code)	/levelup-init	Codebase scan	Discovered CDRs
Greenfield (session complete)	/levelup-specify	Current session	Proposed CDRs

Outline

1. Environment Setup (Phase 0): Resolve paths
2. Review Session (Phase 1): Review the current session for patterns and evidence
3. Load Existing CDRs (Phase 2): Read pending CDRs for enrichment
4. Extract Patterns (Phase 3): Identify reusable patterns by context type + extract paired eval CDRs
5. Create/Enrich CDRs (Phase 4): Write CDR files with session evidence
6. Regenerate Index (Phase 5): Update cdr.md
7. Summary (Phase 6): Present extraction results

Execution Steps

Phase 0: Environment Setup

Run the setup script from the skill's base directory:

scripts/bash/setup-levelup-specify.sh

Parse JSON output for paths and next CDR number.

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

1. REPO_ROOT — walk up from cwd to find a directory containing .adlc/, or use git rev-parse --show-toplevel, or use pwd.
2. TEAM_AI_DIRECTIVES — check TEAM_AI_DIRECTIVES env var, then REPO_ROOT/.adlc/init-options.json → team_ai_directives field, then fallback to REPO_ROOT/team-ai-directives.
3. CDR_DRAFTS_DIR — REPO_ROOT/.adlc/drafts/cdr
4. NEXT_CDR — list CDR_DRAFTS_DIR/CDR-*.md, find the highest number, increment by 1, zero-pad to 3 digits (e.g., 001).

If TEAM_AI_DIRECTIVES is not configured:

Team AI directives repository not configured.
Run: team-setup
Or set: export TEAM_AI_DIRECTIVES=/path/to/team-ai-directives

Phase 1: Review Session

Review the current session to identify what happened. The agent directly observes the session — no trace file is needed.

1. What did the user ask for?
2. What did the agent do? (file changes, key decisions, approach)
3. What was the outcome?
4. What files were created/modified? (git diff --stat, git log --oneline -10)
5. What reusable patterns emerged?

Also collect implementation evidence:

# Files changed
git diff --stat 2>/dev/null

# Recent commits
git log --oneline -10 2>/dev/null

# New/untracked files
git status --short 2>/dev/null

Phase 2: Load Existing CDRs

Read {REPO_ROOT}/.adlc/drafts/cdr/CDR-*.md:

• If user provided --cdr-id: load that specific CDR for enrichment
• Otherwise: load all pending CDRs (status Discovered/Proposed) to avoid duplicates

Phase 3: Extract Patterns

For each context type, look for reusable patterns from the session and evidence:

Rules: Coding conventions, error handling, testing patterns, security practices Personas: Roles that emerged during implementation (e.g., "API consumer", "DevOps operator") Examples: Code patterns worth reusing Skills: Capabilities that could be packaged as agent skills (especially reusable workflows) Constitution Amendments: Cross-cutting principles discovered during the session

Skill-Type CDRs: Yes, /levelup-specify can propose skill-type CDRs. The actual SKILL.md is built later by /levelup-publish --skill <name>.

Eval CDRs: For each directive CDR extracted (rule, persona, example, constitution), also extract a paired eval CDR with binary pass/fail cases from the session:

• Pass cases: moments where the agent correctly followed the (implicit) pattern
• Fail cases: moments where the agent violated the pattern (often the trigger for the directive's creation)
• Adversarial cases: edge cases from the session context that the directive should handle

Each case is self-contained — it includes the scenario, input context, agent output, and why it passes/fails. The case does not depend on an external trace file; the evidence is inline.

Eval CDRs use ### Context Type: Eval and reference their paired directive CDR via ### Paired Directive CDR: CDR-NNN.

Phase 4: Create/Enrich CDRs

For each extracted pattern, create or enrich a CDR:

## CDR-NNN: [Title]

### Status: **Proposed**

### Date: [YYYY-MM-DD]

### Source: Session evidence via /levelup-specify

### Target Module: `context_modules/rules/[domain]/[file].md` or `skills/[skill-name]/`

### Context Type: Rule | Persona | Example | Skill | Constitution Amendment

### Descriptor: One-line "when to use" summary.

### Context
[What reusable pattern was identified]

### Evidence

**Session**: [brief session description]
**Branch**: [branch-name]

**Implementation Evidence**:
- [file/path]: [description]
- `{commit-sha}`: [commit message]

### Decision
[What should be contributed to team-ai-directives]

Eval CDR format (paired with a directive CDR):

## CDR-NNN: [Eval Title]

### Status: **Proposed**

### Date: [YYYY-MM-DD]

### Source: Session evidence via /levelup-specify

### Target Module: `evals/{directive-id}/goldset.md`

### Context Type: Eval

### Paired Directive CDR: CDR-NNN

### Descriptor: One-line "what directive this eval tests"

### Context
[What behavior is being tested and why]

### Pass Cases
1. **Source**: session "[brief description]"
   **Scenario**: [what was asked]
   **Input context**: [what context was loaded]
   **Agent output**: [what the agent did]
   **Why it passes**: [why this follows the directive]

### Fail Cases
1. **Source**: session "[brief description]"
   **Scenario**: [what was asked]
   **Input context**: [what context was loaded]
   **Agent output**: [what the agent did — the violation]
   **Why it fails**: [why this violates the directive]
   **Correction**: [what should have been done]

### Adversarial Cases
1. **Scenario**: [edge case]
   **Expected**: [correct behavior]

### Evidence
**Session**: [brief session description]
**Paired Directive**: CDR-NNN

When enriching an existing CDR, append to the ### Evidence section and update the enrichment timestamp.

Phase 5: 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 these single-line fields:

• ID — from filename (e.g., CDR-001)
• Target Module — from ### Target Module: line
• Type — from ### Context Type: line
• Status — from ### Status: line
• Created — from ### Date: line
• Descriptor — from ### Descriptor: line

Format:

# Context Directive Records (Drafts)

## CDR Index

| ID | Target Module | Type | Status | Created | Verified | Age | Descriptor |
|----|---------------|------|--------|---------|----------|-----|------------|
| CDR-001 | context_modules/rules/... | Rule | Proposed | 2026-07-21 | - | - | One-line summary |

**Stats**: N entries | Last Updated: YYYY-MM-DD

Phase 6: Summary

## LevelUp Specify Summary

**Session**: [brief description]
**Date**: [date]
**CDRs Created**: N
**CDRs Enriched**: N

### CDRs by Type

| Type | Count |
|---|---|
| Rules | N |
| Examples | N |
| Skills | N |
| Personas | N |
| Constitution Amendments | N |
| Evals | N |

### Handover

**Next**: Run `/levelup-clarify` to review the N proposed CDRs.

CDRs are in `Proposed` status and cannot be published until accepted.

Handoff context:

```json
{
  "source": "session",
  "command": "specify",
  "cdrs_created": ["CDR-001", "CDR-002"],
  "cdrs_enriched": [],
  "next": "clarify"
}

### Next Steps

1. Run `/levelup-clarify` to review proposed CDRs
2. For low-evidence CDRs, add implementation evidence first
3. Run `/levelup-publish` for accepted CDRs

Key Rules

Link Evidence, Don't Fabricate

• Every CDR must link to concrete session evidence
• Cite specific file paths, commit SHAs, or test cases
• Do not invent evidence

Avoid Duplicates

• Check existing CDRs before creating new ones
• If a similar pattern exists, enrich it instead of duplicating

Skill CDRs Are Records, Not Skills

• /levelup-specify creates CDRs with Context Type: Skill
• It does not create SKILL.md files directly
• Skill artifacts are built by /levelup-publish --skill <name>

Signal Gate Applies Later

• Create CDRs generously from session evidence
• The signal gate (team-wide, high-value, unique, evidence) is applied at /levelup-publish

Workflow Guidance & Transitions

After /levelup-specify

Required: Run /levelup-clarify to review proposed CDRs.

Complete CDR Lifecycle

[Agent session — work completed]
    ↓
/levelup-specify
    ↓
[Extract patterns + paired evals] → Write CDRs to .adlc/drafts/cdr/CDR-{NNN}.md (Proposed)
    ↓
[Run /levelup-clarify] → Review and accept/reject CDRs (evals gate runs existing goldensets)
    ↓
[Run /levelup-publish] → Compile accepted CDRs into team-ai-directives PR
    ↓
[Run /team-repair] → Re-index and validate team AI directives after merge

Next Steps

After /levelup-specify completes, run /levelup-clarify to review the proposed CDRs.

Verification

• CDRs written to {REPO_ROOT}/.adlc/drafts/cdr/CDR-{NNN}.md with status Proposed.
• Auto-generated cdr.md index exists in {REPO_ROOT}/.adlc/drafts/cdr/.
• Each CDR includes session implementation evidence.
• Eval CDRs are paired with their directive CDRs and contain self-contained pass/fail cases.
• Existing CDRs were enriched rather than duplicated when applicable.

Configuration

• TEAM_AI_DIRECTIVES — Path to the team AI directives (overrides .adlc/init-options.json).
• .adlc/init-options.json — Project-level config file with team_ai_directives field.
• Default fallback: team-ai-directives/ relative to project root.

12-Factor Alignment

Factor IX (Traceability) — extracts reusable patterns from session evidence for team-wide contribution.

Context

$ARGUMENTS