ai-sdlc-conventional-commit: Conventional Commit Message

Internal AI SDLC skill, not client-facing by default. Every rule below is important to follow. None of it can be skipped. Before producing the final artifact, confirm required inputs, target audience, missing facts, output format, and constraints when they are unclear. Do not invent missing information. Ask concise clarification questions when required inputs are absent.

0. Skill Card

Skill name:
```
ai-sdlc-conventional-commit
```
Primary audience: Dev
Supporting audience: PM, BA, QA
Audience tags: Dev, PM, BA, QA
SDLC stage: Commit message drafting
Purpose: Draft, validate, or repair an AI SDLC commit message that uses Conventional Commit syntax and includes SDD, business, implementation, testing, and validation traceability when the change is medium or large.
Output: Conventional Commit subject/body with traceability and validation summary

0.1 Required Inputs

Change type, scope, and implementation summary.
Spec, validation, and test evidence when applicable.
Breaking-change or migration details if any.

0.2 Clarification Rules

Ask concise questions before finalizing when role, artifact, requirements, scope, audience, or constraints are unclear.
If optional information is missing, mark it as
```
TBD
```
,
```
Not provided
```
, or
```
Assumption
```
instead of inventing it.
Separate confirmed facts from assumptions and open questions.
Do not proceed to downstream synthesis when a required upstream artifact or decision is missing.

0.2.1 Flow Mode Flags

Support two explicit execution flags:
```
--quick-flow
```
and
```
--full-flow
```
.
If both flags are supplied,
```
--full-flow
```
takes precedence because it is the stricter mode.
```
--quick-flow
```
: move fast, make high-quality progress with available context, avoid clarification questions unless continuing would create material product, security, compliance, data-loss, or irreversible implementation risk.
In
```
--quick-flow
```
, use documented assumptions, recommended defaults, existing repository patterns, and the nearest available artifact evidence; record important assumptions and decisions in
```
decision-log.md
```
.
In
```
--quick-flow
```
, run only focused checks that are directly relevant, cheap, and likely to catch regressions for the requested work; report any skipped broader checks as residual risk.
```
--full-flow
```
: ask concise clarification questions when inputs, scope, ownership, acceptance criteria, or decisions are unclear; do not silently assume material requirements.
In
```
--full-flow
```
, verify upstream and downstream artifacts, decision-log entries, traceability links, acceptance criteria, and validation evidence before finalizing.
In
```
--full-flow
```
, run or recommend the skill-appropriate gates, reviews, scripts, and validation commands needed for end-to-end confidence; document any blocked verification explicitly.
When neither flag is supplied, follow the skill default rules and choose the least risky behavior for the request size and domain.

0.3 Output Rules

Keep output structured with headings and bullets.
Make findings, gaps, risks, and blockers explicit.
Tie recommendations to evidence from the provided artifact, repository,
```
specs-refiniment/<feature-name>/<file.md>
```
workspace, or user context.
Include role ownership when the output creates follow-up work for BA, QA, Dev, PM, or Delivery.
Return progress, completion, validation, and handoff summaries directly in the Codex response.

Before the final response, emit the

ai-sdlc-handoff/v1

contract with

result

blockers

next_required

, and

next_optional

; every action includes

reason

command

, and

expected_artifact

Do not create
```
summary.txt
```
,
```
*-summary.txt
```
, or another standalone summary file unless the user explicitly requests one.
Keep durable writes limited to the canonical lifecycle artifacts, decision log, human-readable index, and
```
_ai_sdlc
```
machine files.
Let shared helpers migrate legacy paths on the next write; never overwrite or manually merge divergent legacy and canonical files.

0.4 Artifact Routing

Maintain a feature decision log whenever this skill records, resolves, changes, or depends on a product, delivery, QA, security, validation, branching, implementation, or rollout decision.
For PM, BA, QA, Delivery, discovery, planning, refinement, and readiness work, write decisions to
```
specs-refiniment/<feature-name>/decision-log.md
```
.
For developer implementation SDD work, write decisions to
```
specs/<feature-name>/decision-log.md
```
.
Each decision-log entry must include date, decision, context or evidence, options considered when relevant, owner, status, and links to affected artifacts, tasks, tests, or validation evidence.

Use this exact decision-log structure:

markdown

# Decision Log

| ID | Date | Status | Owner | Decision | Context/Evidence | Options Considered | Affected Artifacts | Validation/Trace Links |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| DEC-001 | YYYY-MM-DD | proposed / accepted / superseded / rejected | role or name | concise decision | source facts, artifact links, or evidence | option A; option B; recommended default | affected docs, tasks, code, tests, or rollout notes | requirement IDs, test IDs, validation commands, PRs, commits, or tickets |

Use
```
specs/
```
only for developer implementation SDD packages and repo-governance artifacts.
Do not place PM, BA, QA, Delivery, discovery, planning, refinement, or readiness outputs in
```
specs/
```
; those belong at
```
specs-refiniment/<feature-name>/<file.md>
```
.
When consuming
```
specs-refiniment/<feature-name>/<file.md>
```
, treat it as upstream refinement context and create or update
```
specs/
```
only when implementation work is explicitly in scope.

0.5 Feature State Machine

Maintain feature lifecycle state in TOON at

specs-refiniment/<feature-name>/_ai_sdlc/state.toon

for refinement work and

specs/<feature-name>/_ai_sdlc/state.toon

for implementation work.

Before executing this skill for a feature, check the state machine with

python3 skills/_shared/state_machine.py check --feature <feature-name> --skill <this-skill-name> --workspace <refinement|implementation> --quick-flow|--full-flow

When this skill starts durable work, mark it in progress with
```
begin
```
; when the skill's required artifact or review is complete, mark it done with
```
complete
```
and include
```
--artifacts <path>
```
plus
```
--decision-ref DEC-###
```
when a decision was involved.
In
```
--full-flow
```
, do not proceed when predecessor stages are incomplete, another lifecycle skill is active, or the state file reports a blocker.
In
```
--quick-flow
```
, a predecessor skip is allowed only when continuing is low risk and the command includes
```
--assumption "..."
```
or
```
--decision-ref DEC-###
```
; record the same assumption or decision in
```
decision-log.md
```
.

Use

python3 skills/_shared/state_machine.py status --feature <feature-name> --workspace <refinement|implementation> --format toon

to emit compact LLM-readable state before choosing the next skill.

The state machine is feature-scoped: do not reuse a
```
state.toon
```
across unrelated feature folders.

0.6 Artifact Metadata And Metatags

Every Markdown artifact generated or updated by this skill must start with an
```
artifact_metadata
```
YAML frontmatter block before the first visible heading.

Use schema

ai-sdlc-artifact-metadata/v1

and keep these fields current:

feature

artifact

path

workspace

skill

flow_mode

state_file

decision_log

status

owner

created_at

updated_at

trace_ids

related_artifacts

validation

, and

metatags

```
metatags
```
must include at minimum
```
ai-sdlc
```
, the workspace (
```
refinement
```
or
```
implementation
```
), this skill name, the artifact type or filename stem, and a lifecycle/status tag such as
```
draft
```
,
```
review
```
,
```
approved
```
, or
```
validated
```
.
When
```
--quick-flow
```
is active, set
```
flow_mode: quick
```
, keep assumptions visible in the body, and add tags for major defaults or unresolved risk only when they help retrieval.
When
```
--full-flow
```
is active, set
```
flow_mode: full
```
, keep blockers and validation evidence reflected in
```
status
```
,
```
validation
```
,
```
trace_ids
```
, and
```
related_artifacts
```
.
Update metadata whenever the artifact path, status, owner, trace links, validation evidence, related artifacts, or decision references change.
Metadata is an index for routing, retrieval, and traceability; it does not replace the artifact body,
```
decision-log.md
```
, or
```
state.toon
```
.

0.7 Specs Index

Before searching across feature folders, inspect the compact LLM index first:
```
specs-refiniment/_ai_sdlc/specs-index.toon
```
for refinement work or
```
specs/_ai_sdlc/specs-index.toon
```
for implementation work.
Use the human-readable index at
```
specs-refiniment/specs-index.md
```
or
```
specs/specs-index.md
```
when reporting feature coverage, artifact inventory, or handoff status to people.

After this skill creates or materially updates an artifact, refresh the matching workspace index with

python3 skills/_shared/ai_sdlc_specs_index.py --workspace <refinement|implementation> --quick-flow|--full-flow

In
```
--quick-flow
```
, rely on
```
specs-index.toon
```
to choose the smallest relevant artifact set before opening files.
In
```
--full-flow
```
, verify the updated artifact appears in both
```
specs-index.toon
```
and
```
specs-index.md
```
before final handoff.
The specs index summarizes artifact metadata and state; it does not replace reading the selected source artifacts when details, approvals, or validation evidence matter.

References

Use
```
scripts/validate_commit_msg.py
```
when deterministic validation, planning, or formatting is required by the workflow; pass the same
```
--quick-flow
```
or
```
--full-flow
```
flag that was supplied to the skill when supported.

Script Usage

Validate commit messages before presenting or using them.

Quick flow:

python3 skills/ai-sdlc-conventional-commit/scripts/validate_commit_msg.py --quick-flow --message "<type(scope): subject>"

Full flow:

python3 skills/ai-sdlc-conventional-commit/scripts/validate_commit_msg.py --full-flow path/to/commit-message.txt

Use
```
--require-traceability
```
when medium or large work must include
```
Spec:
```
and
```
Validation:
```
metadata even outside full flow.

Purpose

Draft, validate, or repair an AI SDLC commit message that uses Conventional Commit syntax and includes SDD, business, implementation, testing, and validation traceability when the change is medium or large.

Inputs

Collect the intended change type:
```
feat
```
,
```
fix
```
,
```
docs
```
,
```
test
```
,
```
refactor
```
,
```
chore
```
,
```
ci
```
,
```
build
```
,
```
perf
```
, or
```
revert
```
.
Collect the optional scope when it adds useful precision, for example
```
api
```
,
```
bitgo
```
,
```
sdd
```
,
```
skills
```
, or
```
docs
```
.
Collect the active spec folder for medium or large work, for example
```
specs/177-skill-instruction-upgrade
```
.
Collect the implementation summary, reviewer-visible test path, and exact validation commands with outcomes.
Collect breaking-change details when behavior, schema, API contract, migration requirements, or compatibility changes are not backward compatible.

Steps

Write the subject as

type(scope): imperative summary

type: imperative summary

Keep the subject under 72 characters unless a longer subject prevents ambiguity.
Use a lowercase type and lowercase kebab-case scope.

Use an imperative summary, for example

fix bitgo wallet routing

, not

fixed bitgo wallet routing

Add
```
Spec: specs/NNN-feature-name
```
for medium or large work.

Add

Business context

Implementation details

Mermaid diagram

How to test

, and

Validation

sections for medium or large work.

Add
```
BREAKING CHANGE:
```
when the change requires a migration, client update, data backfill, or operator action.

Validate the message before committing:

bash

python3 skills/ai-sdlc-conventional-commit/scripts/validate_commit_msg.py path/to/message.txt --require-traceability

Fix every validator error before using the message.

Output Spec

Return a complete commit message, not a paragraph about the message:

text

type(scope): imperative summary


Spec: specs/NNN-feature-name

Business context:
One or two sentences explaining why the change matters to product, operations, risk, clients, QA, or delivery governance.

Implementation details:
- Concrete code, contract, doc, workflow, provider, schema, or validation changes.
- Important compatibility, rollout, or failure-mode decisions.

Mermaid diagram:
```mermaid
flowchart LR
    Actor["Actor or trigger"] --> Change["Changed AI SDLC path"]
    Change --> Outcome["Business-visible outcome"]
```

How to test:
1. Reviewer-visible happy path or documentation path.
2. Important permission, failure, boundary, regression, or governance path.

Validation:
- command -> outcome

Quality gate:

Pass when the subject is Conventional Commit compliant, traceability is present when required, validation commands are exact, and every required body section contains concrete project-specific content.
Fail when the message uses placeholders, omits required traceability, hides failed validation, or describes implementation in vague terms such as "updated stuff" or "improved docs".

Examples

Valid medium-change message:

text

docs(skill): upgrade repo-local skill instructions


Spec: specs/177-skill-instruction-upgrade

Business context:
This makes AI skill usage deterministic for future AI SDLC work and reduces reviewer effort caused by vague skill outputs.

Implementation details:
- Rewrote every repo-local skill with Purpose, Inputs, Steps, Output spec, Examples, Edge cases, and Scope boundary.
- Added a skill index that maps each workflow phase to the correct skill.

Mermaid diagram:
```mermaid
flowchart LR
    Request["Skill audit"] --> Rewrite["Normalized SKILL.md files"]
    Rewrite --> Output["Consistent AI assistant behavior"]
```

How to test:
1. Read a cold skill and confirm it contains a complete execution contract.
2. Run skill and spec validators for the updated files.

Validation:
- PYTHONPYCACHEPREFIX=/tmp/ai-sdlc-harness-pycache python3 -m py_compile skills/ai-sdlc-validation/scripts/validation_plan.py -> passed
- git diff --check -> passed

Invalid counter-example:

text

updates

Made skills better.

Reject this because the subject is not Conventional Commit syntax, traceability is absent, and validation evidence is missing.

Edge Cases

Include
```
BREAKING CHANGE:
```
even for documentation-only commits when the documented workflow intentionally retires a previously required control.
Use
```
revert: ...
```
only when the commit actually reverts a previous commit; include the reverted hash in the body.
Stop and fix the message when the validator fails; do not commit with an invalid message.
State failed or skipped validation honestly in the
```
Validation
```
section with the residual risk.

Scope Boundary

Do not stage files or create commits; use
```
$ai-sdlc-commit-prep
```
for staging and commit execution.
Do not invent validation results; use
```
$ai-sdlc-validation
```
to choose and run checks.
Do not use this skill to summarize a diff unless the output is a commit message.

ai-sdlc-conventional-commit

NPX Install

Tags

SKILL.md Content

ai-sdlc-conventional-commit: Conventional Commit Message

0. Skill Card

0.1 Required Inputs

0.2 Clarification Rules

0.2.1 Flow Mode Flags

0.3 Output Rules

0.4 Artifact Routing

0.5 Feature State Machine

0.6 Artifact Metadata And Metatags

0.7 Specs Index

References

Script Usage

Purpose

Inputs

Steps

Output Spec

Examples

Edge Cases

Scope Boundary