ai-sdlc-approvals-sandbox: Approvals And Sandbox

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-approvals-sandbox
```
Primary audience: Dev
Supporting audience: QA
Audience tags: Dev, QA
SDLC stage: Sandbox escalation decision
Purpose: Decide, request, and report sandbox escalation for AI SDLC commands only when the sandbox blocks a required action or the task explicitly requires approved external access.
Output: Sandbox escalation decision record with prefix_rule guidance and residual risk

0.1 Required Inputs

Exact command that failed or requires external access.
Task reason for the command.
Sandbox error, expected restriction, and risk profile.

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/approval_plan.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 approval requests before asking for escalation or recording an approval decision.

Quick flow:

python3 skills/ai-sdlc-approvals-sandbox/scripts/approval_plan.py --quick-flow --command "<command>" --justification "<user-facing question?>"

Full flow:

python3 skills/ai-sdlc-approvals-sandbox/scripts/approval_plan.py --full-flow --command "<command>" --justification "<user-facing question?>" --prefix-rule "<safe reusable prefix>"

Use
```
--prefix-rule
```
only when proposing a reusable non-destructive approval prefix; omit it for destructive or one-off commands.

Purpose

Decide, request, and report sandbox escalation for AI SDLC commands only when the sandbox blocks a required action or the task explicitly requires approved external access.

Inputs

Collect the exact command that failed or must run outside the sandbox.
Collect the task reason that makes the command necessary.
Collect the sandbox error or expected restriction: filesystem, network, listener, GUI, external service, or destructive action.
Collect whether the command is destructive, secret-bearing, shell-heavy, or reusable.
Collect an intended narrow
```
prefix_rule
```
only when repeated approval is safe.

Steps

Run normal reads, workspace writes, local tests, and formatting in the default sandbox first.
Classify the command into one segment per shell operator when the command contains pipes, separators, logical operators, or subshells.
Request escalation only when a required command is blocked by sandbox restrictions or explicitly needs approved external access.
Do not request escalation to bypass SDD, skip validation, avoid fixing a local setup issue, or run unrelated broad commands.

Validate the approval plan before asking when time permits:

bash

python3 skills/ai-sdlc-approvals-sandbox/scripts/approval_plan.py \
  --command 'go test ./internal/service/...' \
  --justification 'Allow running focused service tests with the required sandbox permissions?' \
  --prefix-rule 'go test ./internal/service/...'

Phrase
```
justification
```
as a short user-facing question.
Provide
```
prefix_rule
```
only for narrow, reusable, non-destructive command classes.
Rerun the command only after approval is granted.
Report denied or partial approval and continue with the best safe fallback.

Output Spec

Return this decision record when escalation is requested, denied, or skipped:

text

Sandbox decision:
- Command: exact command
- Required for: task-specific reason
- Sandbox issue: filesystem | network | listener | GUI | external service | destructive | none
- Escalation: requested | not requested | denied | granted
- Prefix rule: proposed rule | none and why
- Result: passed | failed | skipped | blocked
- Residual risk: none | concrete limitation

Quality gate:

Pass when escalation is narrow, justified by the task, and avoids broad reusable approval for dangerous commands.
Fail when the request uses vague justification, proposes broad interpreter or shell prefixes, includes secrets, or escalates unrelated work.

Examples

Valid request:

json

{
  "sandbox_permissions": "require_escalated",
  "justification": "Allow running Go tests with a writable external cache for this package?",
  "prefix_rule": ["go", "test", "./internal/service/..."]
}

Invalid counter-example:

json

{
  "sandbox_permissions": "require_escalated",
  "justification": "Need permissions.",
  "prefix_rule": ["python3"]
}

Reject this because the justification is vague and the prefix allows arbitrary scripts.

Edge Cases

Skip reusable
```
prefix_rule
```
for destructive commands such as
```
rm
```
,
```
git reset
```
, force push, or data deletion.
Skip reusable
```
prefix_rule
```
for heredocs, redirection, wildcards, command substitution, environment-heavy one-liners, or shell wrappers.
Warn immediately and avoid reusing commands when a command contains credentials, bearer tokens, private keys, webhook secrets, or production-only values.
Treat a missing dependency error as a setup issue first, not an escalation reason, unless the dependency download is required and network is blocked.
Report partial approval when one command segment is approved but another remains blocked.

Scope Boundary

Do not decide which validation commands are required; use
```
$ai-sdlc-validation
```
.
Do not approve destructive commands on the user's behalf.
Do not weaken developer SDD, review, or validation requirements because sandbox permissions are inconvenient.
Do not replace the active runtime’s higher-priority sandbox and approval policies.

ai-sdlc-approvals-sandbox

NPX Install

Tags

SKILL.md Content

ai-sdlc-approvals-sandbox: Approvals And Sandbox

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