
Write Adr
- 44 installs
- 74 repo stars
- Updated July 21, 2026
- existential-birds/beagle
Helps with ai & agent building tasks.
About
write-adr is a Claude Code skill for ai & agent building. It helps solo builders move faster with AI-assisted development.
- write-adr
- AI & Agent Building
- AI-coding skill
Write Adr by the numbers
- 44 all-time installs (skills.sh)
- Ranked #7,851 of 16,546 AI & Agent Building skills by installs in the Skillselion catalog
- Data as of Jul 28, 2026 (Skillselion catalog sync)
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| Installs | 44 |
|---|---|
| repo stars | ★ 74 |
| Last updated | July 21, 2026 |
| Repository | existential-birds/beagle ↗ |
What it does
Helps with ai & agent building tasks.
Files
Write ADR
Generate Architecture Decision Records (ADRs) from decisions made during the current session.
Workflow Overview
1. Context - Gather repository context and existing ADRs 2. Extract - Analyze conversation for decisions using a subagent 3. Confirm - Present decisions to user for selection 4. Write - Generate ADRs in parallel using subagents 5. Report - Summarize created files and status 6. Verify - Validate generated ADRs against Definition of Done
Gates (objective pass conditions)
Advance only when the pass condition holds. These are checkable without “I verified internally.”
| After | Pass condition |
|---|---|
| Step 2 (extract) | The subagent response is valid JSON with a top-level decisions array (empty is OK). Each non-empty item has id, title, and at least one of context, decision, alternatives, or rationale present as a non-empty string or non-empty array. If parsing fails or the shape is wrong, re-run extraction or fix the payload before Step 3. |
| Step 4 (pre-allocate) | From repo root, python plugins/beagle-analysis/skills/adr-writing/scripts/next_adr_number.py --count N prints exactly `N` lines (one number per line). You have a written mapping (in the reply draft or notes) from each selected decision to one of those lines before launching any ADR writer unit. |
| Step 5 (report) | Every file path in the summary table is copied from a subagent completion output (not invented). Optionally spot-check: test -f <path> for each path before marking success. |
| Step 6 (verify) | For each ADR path, opening the file shows line 1 is `---`, frontmatter parses as YAML, status and date are present, and the body meets the Step 6 bullets below (alternatives count, Good/Bad consequences). |
Step 1: Gather Context
# Get current branch and recent commits
git branch --show-current
git log --oneline -5
# Check for existing ADRs
ls docs/adrs/ 2>/dev/null || echo "No ADR directory found"
# Count existing ADRs for numbering
find docs/adrs -name "*.md" 2>/dev/null | wc -lThis context helps the ADR writer:
- Reference related commits in the ADR
- Avoid duplicate ADRs for already-documented decisions
- Determine correct sequence numbering
Step 2: Extract Decisions
Analyze the current conversation for architectural decisions. If the agent supports subagents, dispatch this as a single extraction subagent; otherwise run the same extraction inline — identical output. Use this brief:
Load the **adr-decision-extraction** skill ([../adr-decision-extraction/SKILL.md](../adr-decision-extraction/SKILL.md)).
Analyze the conversation for decisions that warrant ADRs:
- Technology choices, architecture patterns, design trade-offs
- Rejected alternatives, significant implementation approaches
Return JSON:
{
"decisions": [
{
"id": 1,
"title": "Use PostgreSQL for primary datastore",
"context": "Brief context about why this came up",
"decision": "What was decided",
"alternatives": ["What was considered but rejected"],
"rationale": "Why this choice was made"
}
]
}If the subagent returns an empty decisions array, skip to Step 5 with message: "No architectural decisions detected in this session."
Gate: Meet the Step 2 row in Gates (objective pass conditions) before Step 3.
Step 3: Confirm with User
Display all extracted decisions with full details, then ask user to select:
## Detected Decisions
### 1. Use PostgreSQL for primary datastore
**Confidence:** high
**Problem:** Need ACID transactions for financial records
**Decision:** PostgreSQL for user data storage
**Alternatives discussed:**
- MongoDB
- SQLite
**Rationale:** ACID compliance, team familiarity, mature ecosystem
**Source:** Discussion about database selection in planning phase
---
### 2. Implement event sourcing for audit trail
**Confidence:** medium
**Problem:** Compliance requires complete audit history
**Decision:** Event sourcing pattern for state changes
**Alternatives discussed:**
- Database triggers
- Application-level logging
**Rationale:** Immutable audit trail, temporal queries, debugging capability
**Source:** Compliance requirements discussion
---
## Selection
Which decisions should I write ADRs for?
- Enter numbers (e.g., "1,2" or "1-2"), "all", or "none" to skipImportant: Always display the full decision details (problem, decision, alternatives, rationale) from the extraction output BEFORE asking for selection. Do not truncate to just title and context.
Parse user response:
"all"- Process all decisions"none"or empty - Skip with message "No ADRs will be created.""1,2"or"1-2"- Process specified decisions
Step 4: Write ADRs (Parallel)
Pre-allocate ADR numbers before launching subagents to prevent numbering conflicts:
# Pre-allocate numbers for all confirmed decisions (from repository root)
# Example: If user selected 3 decisions
python plugins/beagle-analysis/skills/adr-writing/scripts/next_adr_number.py --count 3
# Output:
# 0003
# 0004
# 0005Assign each pre-allocated number to its corresponding decision before launching subagents.
Gate: Meet the Step 4 row in Gates (objective pass conditions) before launching the first ADR writer unit.
For each confirmed decision, run an ADR writer unit with its pre-assigned number. If the agent supports subagents, dispatch one writer per decision in parallel; otherwise write each ADR sequentially — identical output. Use this brief:
Load the **adr-writing** skill ([../adr-writing/SKILL.md](../adr-writing/SKILL.md)).
Write an ADR for this decision:{decision JSON}
**IMPORTANT: Use this pre-assigned ADR number: {assigned_number}**
Instructions:
1. Explore codebase for additional context
2. Write MADR-formatted ADR to docs/adrs/
3. Use the pre-assigned number {assigned_number} - DO NOT call next_adr_number.py
4. Filename format: {assigned_number}-slugified-title.md
5. Return created file pathCritical: Pass the pre-allocated number to each writer unit. Writers must NOT call next_adr_number.py themselves - this causes duplicate numbers when running in parallel.
Wait for all writer units to complete before proceeding.
Step 5: Report Results
Gate: Meet the Step 5 row in Gates (objective pass conditions) when building the summary (paths from subagent outputs; optional test -f).
Collect outputs from all subagents and present summary:
## ADR Generation Complete
| File | Decision | Status |
|------|----------|--------|
| docs/adrs/0003-use-postgresql.md | Use PostgreSQL for primary datastore | Draft |
### Next Steps
- Review generated ADRs for accuracy
- Update status from "proposed" to "accepted" when finalized
### Gaps Requiring Investigation
- [List any decisions where subagent noted missing context]If no decisions were processed:
No ADRs were created. Run this command again after making architectural decisions.Step 6: Verify Generated ADRs
For each created ADR, validate against Definition of Done:
## Verification Checklist
| ADR | E | C | A | D | R | Status |
|-----|---|---|---|---|---|--------|
| 0003-use-postgresql.md | ✓ | ✓ | ✓ | ⚠ | ✗ | Incomplete |
Legend: E=Evidence, C=Criteria, A=Agreement, D=Documentation, R=RealizationGate: Meet the Step 6 row in Gates (objective pass conditions) for every created ADR.
Verification steps: 1. Open each generated ADR file 2. Confirm filename follows NNNN-slugified-title.md pattern 3. Verify YAML frontmatter exists at file start:
- File MUST begin with
--- - Contains
status: draft(or valid status) - Contains
date: YYYY-MM-DD(actual date) - Ends with
---before title - If frontmatter is missing, add it immediately
4. Review for [INVESTIGATE] prompts - these need follow-up 5. Verify at least 2 alternatives are documented 6. Confirm consequences section has both Good and Bad items
If gaps exist:
- Keep status as
draftuntil gaps are resolved - Use
[INVESTIGATE]prompts to guide follow-up session - Schedule review with stakeholders before changing to
accepted
Output Location
ADRs are written to docs/adrs/ (same convention as adr-writing). If no ADR directory exists, create it with an initial 0000-use-madr.md template record.
MADR Format Reference
---
status: draft
date: YYYY-MM-DD
---
# {TITLE}
## Context and Problem Statement
{What is the issue motivating this decision?}
## Decision Drivers
* {driver 1}
* {driver 2}
## Decision Outcome
Chosen option: "{option}", because {reason}.
### Consequences
* Good, because {positive}
* Bad, because {negative}