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doc-chg-autopilot

Drive a Change Management record end-to-end with minimal prompts - detect the change, classify the level, draft the CHG, assess cross-layer cascade, and prep the entry gate. Use to author or batch CHG

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About

Imported from vladm3105/aidoc-flow-framework (platforms/claude-code-plugin/skills/doc-chg-autopilot/SKILL.md). Install upstream with npx skills add vladm3105/aidoc-flow-framework --skill doc-chg-autopilot. Copyright stays with the author.

doc-chg-autopilot

Purpose

Automated CHG pipeline. From a change request — a diff, an incident, a prompt, or an upstream artifact edit — it detects the change, classifies the level, drafts a complete CHG record, traces the cross-layer cascade, registers it, and prepares the entry gate for approval — for one change or a batch.

CHG is NOT a lifecycle layer in the BRD..IPLAN sense: no template chain, no readiness score. The pipeline closes on gate approval, not a numeric score. For team-mode dispatch purposes the CHG layer is addressed as 09_CHG (the layer-id slot occupied by the change-management overlay).

This autopilot deliberately carries no ## Model precheck step. The per-layer model recommendation (MODEL-PRECHECK-ROLLOUT) applies to the eight lifecycle-layer autopilots (BRD..IPLAN); CHG is a governance overlay with no model.per_layer.CHG entry, so there is nothing to precheck. The conformance suite (tests/conformance/platforms/test_model_precheck.py) asserts the precheck on exactly those eight — CHG's absence is intentional, not a gap.

Skill Dependencies

Skill Role
../doc-chg/SKILL.md CHG structure, classification, and routing rules
../doc-chg-audit/SKILL.md gate-readiness check (pass/fail + fix list)
../doc-chg-fixer/SKILL.md applies fixes from the audit report
../gate-check/SKILL.md runs the formal gate (C3/Emergency) + approval form
../review-team/SKILL.md team-mode dispatcher (parallel persona subagent fan-out)

Input Contract

Accepts: a target CHG-NN/path; a diff or list of edited artifacts; an incident reference; or a free-text change description. Optional: max fix iterations (default 3), batch list. With no explicit input, treat the request as a change description and infer the source.

Smart Document Detection

For each target, check whether the CHG already exists in the change registry (CHG-00_index.md):

  • Missing → generate mode (draft a new CHG record).
  • Exists → review & fix mode (audit, then fix if it fails gate-readiness).

Infer the change source (upstream/midstream/design/execution/external/ feedback) and level (C1/C2/C3/Emergency) from the changed artifacts and the trigger.

Workflow

MANDATORY — DO THIS FIRST. Your first and only action when review_mode: team (the framework default) is to invoke the saga driver via the Bash tool. You MUST NOT dispatch Task subagents directly, MUST NOT call doc-chg/doc-chg-audit/doc-chg-fixer directly via SlashCommand or otherwise, and MUST NOT generate the CHG in-session. The driver is the sole orchestration mechanism for the create-review-revise loop. Bypassing it produces a CHG without a saga.json journal — the same failure mode the BRD/PRD/EARS slim-downs closed out at their respective layers.

Saga-driven generation loop (review_mode: team)

Step 1 — Invoke the driver. Period. The harness sets PREV_OUTPUT, ARTIFACT_ID, ARTIFACT_PATH env vars before invoking this SKILL. Your VERY FIRST tool call MUST be the Bash tool, running exactly:

python3 "${CLAUDE_PLUGIN_ROOT}/tools/saga_driver.py" \
  --layer 09_CHG \
  --artifact-path "${ARTIFACT_PATH}" \
  --allow-skip-permissions

--allow-skip-permissions lets the phases the driver dispatches write files without a permission prompt — unattended autopilot requires it. Drop the flag to run the same loop with Claude Code's normal prompts on.

Use a generous timeout (≥1800s). Do not pre-analyze the input. Do not read the seed. Do not classify level/source. The driver and its dispatched subprocesses (/aidoc-flow:doc-chg for draft, /aidoc-flow:doc-chg-audit for review, /aidoc-flow:doc-chg-fixer for fixer) handle all of that. The driver enforces the state machine preemptively per ${CLAUDE_PLUGIN_ROOT}/framework/governance/REVIEW_SAGA.md; this SKILL's job is to invoke it and report.

Step 2 — After the driver returns, report. Read .aidoc/review/09_CHG/${ARTIFACT_ID}/saga.json. Final status MUST be one of CLOSED (PASS, gate_ready: true), ESCALATED (terminal FAIL), or PARTIAL_TIMEOUT (soft-deadline; resumable). Print the status, the gate_ready boolean from verdict.json if present, and a 1-line summary.

Step 3 — Index update (only on CLOSED). Add a row to CHG-00_index.md referencing the new CHG.

Step 4 — Gate prep (only on CLOSED). C1 commits directly; C2 routes to peer review; C3/Emergency hand off to ../gate-check/SKILL.md to run the formal/post-hoc gate and complete GATE_APPROVAL_FORM. For Emergency, schedule the post-mortem (${CLAUDE_PLUGIN_ROOT}/framework/governance/chg/templates/POST_MORTEM-TEMPLATE.md) within 48h of the fix deploy.

That is the entire workflow in team mode. If you find yourself doing anything else here — drafting prose, dispatching Task subagents, invoking other slash commands — STOP, recognize that you are bypassing the driver, and invoke the Bash command above instead.

Linear Pipeline (review_mode: single_pass)

Unchanged legacy behaviour — used when the profile says so, when Task subagent dispatch is unavailable, or at write-time (on_author) where cost is the primary concern.

  1. Input analysis — classify the input (diff / incident / prompt / upstream edit), locate the touched artifacts, and decide generate vs review-and-fix.
  2. Type & scope — classify the change level and source per ../doc-chg/SKILL.md; select the entry gate (GATE-01/03/06/08/CODE, or GATE-SPEC for a framework/-spec change — set semver_impact, ≥C2); reserve the next CHG-NN (or CHG-EMG-YYYYMMDD-HHMM for Emergency).
  3. Generation — populate ${CLAUDE_PLUGIN_ROOT}/framework/governance/chg/CHG-TEMPLATE.yaml: metadata, change_control, description (what/why/trigger), implementation, verification. Impact assessment is mandatory — trace the cascade along BRD→PRD→EARS→BDD→ADR→SPEC→TDD→IPLAN→Code (downstream for upstream/external, bubble-up for feedback, lateral for midstream) and list every affected artifact. Add rollback_plan (C2/C3), gate_approval (C3), emergency_change (Emergency).
  4. Validation — run ../doc-chg-audit/SKILL.md from scratch in single_pass mode.
  5. Audit ↔ fix cycle — while the audit FAILs and iterations < max: run ../doc-chg-fixer/SKILL.md in single_pass mode, then re-audit. On pass, update CHG-00_index.md; on exhausting iterations, flag for manual review.
  6. Gate prep — C1 commits directly; C2 routes to peer review; C3/Emergency hand off to ../gate-check/SKILL.md to run the formal/post-hoc gate and complete GATE_APPROVAL_FORM. For Emergency, schedule the post-mortem (${CLAUDE_PLUGIN_ROOT}/framework/governance/chg/templates/POST_MORTEM-TEMPLATE.md) within 48h of the fix deploy.

Execution Modes

  • Single — one CHG (generate or review-and-fix).
  • Batch — multiple changes, processed in chunks of 3 to bound context; generate upstream-source CHGs before the downstream ones that depend on them.
  • Dry-run — report the planned classification, source, entry gate, and cascade list without writing files.

Quality Gates

  • A CHG is not "done" until the audit passes (0 blocking findings) and the required approval is obtained: C1 self, C2 peer review, C3/Emergency gate via ../gate-check/SKILL.md. There is no numeric readiness score.
  • The change registry is updated only after a CHG passes the audit.
  • Fresh audit every cycle — no cached results.

Error Handling

Situation Action
Source ambiguous infer from touched layers; record the assumption in the CHG
Cascade incomplete (E003) re-trace the chain before drafting impact assessment
Entry gate mismatch (E002) re-route per the source table; correct entry_gate
Max iterations reached, still failing write reports, flag for manual review, continue batch
Emergency post-mortem overdue block sign-off; escalate; keep post_mortem_completed: false

Adaptation

Before applying defaults, read the project adaptation profile (.aidoc/profile.yaml) and apply it in both the generation and the internal audit/fix phases. Honor section_toggles, active_layers, audit_threshold (raise-only — stricter only), glossary, and review_mode (select team to invoke the saga driver per §Saga-driven generation loop; single_pass to run the linear pipeline). Ignore any unknown or out-of-surface key; absent a profile, use framework defaults (team at gates / single_pass at write-time). Authority: ${CLAUDE_PLUGIN_ROOT}/framework/governance/ADAPTATION.md.

Related Resources

  • Create: ../doc-chg/SKILL.md · Audit: ../doc-chg-audit/SKILL.md · Fix: ../doc-chg-fixer/SKILL.md · Gate: ../gate-check/SKILL.md
  • Authority: ${CLAUDE_PLUGIN_ROOT}/framework/governance/chg/CHG-TEMPLATE.yaml, ${CLAUDE_PLUGIN_ROOT}/framework/governance/chg/README.md, ${CLAUDE_PLUGIN_ROOT}/framework/governance/chg/CHG-00_index.TEMPLATE.md, ${CLAUDE_PLUGIN_ROOT}/framework/governance/chg/gates/, ${CLAUDE_PLUGIN_ROOT}/framework/governance/AUTHORING_STYLE.md

Use it

Copy one of these into your project. Installing also returns the manifest and these snippets.

yaml
targets:
  - https://api.opensmartroute.ai/api/v1/registry/vladm3105-aidoc-flow-framework-doc-chg-autopilot/manifest   # or paste the manifest below

Manifest

An Open Capability Manifest: the router reads it to know what this does, what it costs and when to pick it.

vladm3105-aidoc-flow-framework-doc-chg-autopilot.ocm.jsonjson
{
  "ocm": "1",
  "id": "vladm3105-aidoc-flow-framework-doc-chg-autopilot",
  "kind": "skill",
  "name": "doc-chg-autopilot",
  "description": "Drive a Change Management record end-to-end with minimal prompts - detect the change, classify the level, draft the CHG, assess cross-layer cascade, and prep the entry gate. Use to author or batch CHGs hands-off.",
  "publisher": "vladm3105",
  "version": "1.0.0",
  "capabilities": {
    "domains": [
      "general"
    ],
    "tags": [
      "skill-md",
      "sdd-workflow",
      "change-management",
      "automation-workflow",
      "skills-sh"
    ],
    "languages": [
      "en"
    ]
  },
  "quality_prior": 0.6,
  "examples": [
    "Drive a Change Management record end-to-end with minimal prompts - detect the change, classify the level, draft the CHG, assess cross-layer cascade, and prep the entry gate. Use to author or batch CHGs hands-off."
  ],
  "primary": false,
  "metadata": {
    "source": {
      "provider": "skills.sh",
      "repository": "https://github.com/vladm3105/aidoc-flow-framework",
      "path": "platforms/claude-code-plugin/skills/doc-chg-autopilot/SKILL.md",
      "ref": "HEAD",
      "url": "https://github.com/vladm3105/aidoc-flow-framework/blob/HEAD/platforms/claude-code-plugin/skills/doc-chg-autopilot/SKILL.md",
      "key": "vladm3105/aidoc-flow-framework/platforms/claude-code-plugin/skills/doc-chg-autopilot/SKILL.md"
    }
  },
  "instructions": "# doc-chg-autopilot\n\n## Purpose\n\nAutomated **CHG pipeline**. From a change request — a diff, an incident, a\nprompt, or an upstream artifact edit — it detects the change, classifies the\nlevel, drafts a complete CHG record, traces the cross-layer cascade, registers\nit, and prepares the entry gate for approval — for one change or a batch.\n\nCHG is **NOT a lifecycle layer** in the BRD..IPLAN sense: no template chain,\nno readiness score. The pipeline closes on **gate approval**, not a numeric\nscore. For team-mode dispatch purposes the CHG layer is addressed as\n**`09_CHG`** (the layer-id slot occupie",
  "cost": {
    "context_tokens": 2308
  }
}

Fetch it by URL: GET /api/v1/registry/vladm3105-aidoc-flow-framework-doc-chg-autopilot/manifest?version=1.0.0

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