ai-sdlc-qa: QA Planning And Evidence

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-qa
```
Primary audience: QA
Supporting audience: BA, Dev, PM
Audience tags: QA, BA, Dev, PM
SDLC stage: QA planning and refinement
Purpose: Produce QA acceptance, regression, manual-check, and signoff evidence for AI SDLC changes and place QA refinement artifacts under
```
specs-refiniment/<feature-name>/<file.md>
```
when writing files.
Output: QA acceptance plan, regression targets, manual checks, validation evidence, and residual risks

0.1 Required Inputs

Requirements, stories, delivery spec,
```
specs-refiniment/<feature-name>/<file.md>
```
QA context, or changed implementation context.
Acceptance criteria, changed files, or release-sensitive behavior.
Validation output if already run.

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,
```
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 |

When writing or updating files, place PM, BA, QA, Delivery, discovery, planning, refinement, and readiness artifacts at
```
specs-refiniment/<feature-name>/<file.md>
```
.
Use the path pattern
```
specs-refiniment/<feature-name>/<file.md>
```
; choose a stable feature slug when known, otherwise use
```
tbd-<short-topic>
```
for
```
<feature-name>
```
.
Do not write this skill's output into
```
specs/
```
; that folder is reserved for developer implementation SDD artifacts.
If the user explicitly asks to convert a refined artifact into developer implementation work, hand off to
```
$ai-sdlc-sdd
```
.

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.

0.8 Complete Refinement Cascade

Trigger the complete cascade only when the user explicitly asks for a full, complete, or end-to-end spec refinement or asks for every refinement artifact. A normal
```
--full-flow
```
call for one skill remains single-stage.
Before the first durable write, run
```
python3 skills/_shared/refinement_status.py --feature <feature-name> --gate full --format toon
```
and start with the earliest reported
```
next_skill
```
, including stages earlier than this skill.
Execute the existing refinement skills in lifecycle order with
```
--full-flow
```
: discovery, PRFAQ, delivery-package gap review, requirements readiness, goal/capability mapping, backlog gap review, backlog decomposition, story decomposition, release slicing, BA context, delivery spec, QA plan, QA gap review, test strategy, test cases, test suite, QA readiness, and delivery handoff.
Produce all 18 canonical Markdown artifacts.
```
release-slicing.md
```
is mandatory for a complete cascade; when release slicing is not applicable, write an explicit evidence-backed N/A artifact and complete the stage instead of skipping it.
After every stage, finalize its artifact, record required decisions, mark the stage
```
done
```
, and refresh the refinement indexes before selecting the next skill.
Do not declare the cascade complete until
```
python3 skills/_shared/refinement_status.py --feature <feature-name> --gate full --format markdown
```
exits successfully with
```
18/18
```
. If it fails, continue with the reported next skill or return the concrete blocker and remaining inventory in Codex.
Surface checkpoint and final summaries in Codex only; never persist a cascade summary as a text file.

References

Use
```
scripts/qa_plan_scaffold.py
```
when deterministic scaffolding, planning, or formatting is useful for this workflow; pass the same
```
--quick-flow
```
or
```
--full-flow
```
flag that was supplied to the skill.
Read
```
references/qa-plan-template.md
```
when the task needs the detailed structure, checklist, or examples for this skill.

Script Usage

In default and full flow, always run this skill's primary analysis script with
```
--format toon --budget-tokens 24000
```
before drafting; explicit inputs are priority evidence but do not replace the rest of the feature package.
Read this skill's reference file before writing sections. Use its detailed tables and quality bar, not only the compact scaffold headings.
Run the primary script with
```
--emit-template
```
in the active flow mode to obtain the exact shared context headings and required stage table columns before section writes.
Make every default/full artifact self-contained by completing all ten shared feature-context sections plus the stage-specific profile sections. Quick flow may use the compact stage-only draft.
Follow every
```
next_reads
```
entry before finalization and list every consumed source in
```
Source Coverage
```
; do not claim whole-feature context from a partial source set.
Keep the final artifact within
```
--max-artifact-tokens 24000
```
; condense repetition instead of dropping feature dimensions or source traceability.
Run
```
scripts/qa_plan_scaffold.py
```
before drafting or updating this skill's artifact when inputs are longer than a few bullets, when traceability matters, or when a flow flag is supplied. For agent analysis, pass
```
--format toon
```
, read
```
anchors
```
first, and open only
```
next_reads
```
; without that flag the script keeps its human-readable Markdown output.

Quick flow analysis:

python3 skills/ai-sdlc-qa/scripts/qa_plan_scaffold.py --feature <feature-name> --quick-flow <input.md>...

Full flow analysis:

python3 skills/ai-sdlc-qa/scripts/qa_plan_scaffold.py --feature <feature-name> --full-flow <input.md>...

To write content, pass one canonical heading with
```
--section "<section>"
```
; provide only that section body on stdin, without H1, H2, frontmatter, or a temporary content file.
Repeat
```
--section
```
for each required section, then run the same script with
```
--finalize
```
to validate the artifact and refresh metadata and specs indexes.
The AI must not write or directly edit the routed Markdown artifact; the script owns scaffold creation, section placement, and durable file writes.
Use
```
--decision-row
```
with one nine-cell Markdown table row on stdin when a decision-log entry is required.

Legacy

--emit-template

--emit-decision-log-entry

, and

--write

remain available for compatibility.

Use
```
--quick-flow
```
for first-pass synthesis with assumptions; use
```
--full-flow
```
before readiness, handoff, signoff, or any decision-sensitive output.

Purpose

Produce QA acceptance, regression, manual-check, and signoff evidence for AI SDLC changes. Return the QA plan as an internal refinement artifact and place it under

specs-refiniment/<feature-name>/<file.md>

when writing files.

Inputs

Read the relevant requirements, stories, delivery spec, existing
```
specs-refiniment/<feature-name>/<file.md>
```
QA notes, test cases, and release context.
Collect the changed files or diff when QA is based on an implementation.
Collect validation output from
```
$ai-sdlc-validation
```
when checks have already run.
Collect release context, user roles, provider names, asset symbols, endpoints, or UI surfaces affected by the change.
Read
```
references/qa-plan-template.md
```
when the QA plan needs reusable acceptance or regression wording.

Steps

Define the change boundary in one sentence.
Rank QA risks by user impact, money/asset impact, security impact, provider impact, and regression likelihood.
Write acceptance scenarios with actor, setup, action, expected result, evidence type, and risk.
Write regression targets for existing behavior that must remain stable.
Separate automated validation from manual or exploratory checks.
Record exact validation commands and outcomes when already run.
Mark unrun checks as planned or skipped with a concrete reason and residual risk.
Write or update the QA artifact under
```
specs-refiniment/<feature-name>/<file.md>
```
when file output is requested.
Use
```
$ai-sdlc-test-cases
```
when the missing artifact is scenario-to-test automation design.
Use
```
$ai-sdlc-validation
```
when commands need to be selected or executed.

Output Spec

Use this format:

text

QA plan:
- Change boundary: one sentence.

Acceptance scenarios:
- QA-001:
  Actor: role or system.
  Setup: required state.
  Action: user/API/system action.
  Expected result: observable result.
  Evidence: automated test | manual check | not yet covered.
  Risk: high | medium | low and reason.

Regression targets:
- Existing behavior and why it is at risk.

Validation evidence:
- command -> passed | failed | skipped: reason.

Manual checks:
- Check, environment, expected result, and owner if known.

Signoff:
- Ready | blocked | partial, with reason.

Quality gate:

Pass when every acceptance scenario has actor, setup, action, expected result, evidence, and risk.
Fail when a scenario says only "verify it works", when skipped validation has no reason, or when the plan claims a pass without executed evidence.

Examples

Valid QA scenario:

text

- QA-001:
  Actor: operations user.
  Setup: organization has BitGo configured with one enterprise wallet.
  Action: open the custodian setup endpoint.
  Expected result: response includes the BitGo enterprise ID and wallet display label without exposing secrets.
  Evidence: transport test plus manual API response review.
  Risk: high because incorrect wallet scope can block settlement operations.

Invalid counter-example:

text

Acceptance scenarios:
- Test the endpoint.

Reject this because it lacks setup, action, expected result, evidence, and risk.

Edge Cases

State
```
blocked
```
when no environment, fixture, credentials, or sample payload exists for a manual check.
Do not mark manual QA as passed from code inspection alone.
Mark flaky or nondeterministic evidence as partial and name the unstable dependency.
Use redacted examples for provider payloads; never include secrets or production-only values.
Keep QA signoff separate from developer validation when acceptance requires human workflow review.

Scope Boundary

Do not implement automated tests; use
```
$ai-sdlc-test-cases
```
to design tests and normal coding workflow to implement them.
Do not select broad test suites by default; use
```
$ai-sdlc-validation
```
.
Do not approve product scope; use
```
$ai-sdlc-ba
```
for unresolved business decisions.
Do not claim release readiness when validation, manual checks, or signoff are incomplete.

ai-sdlc-qa

NPX Install

Tags

SKILL.md Content

ai-sdlc-qa: QA Planning And Evidence

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

0.8 Complete Refinement Cascade

References

Script Usage

Purpose

Inputs

Steps

Output Spec

Examples

Edge Cases

Scope Boundary