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Optimizing a pipeline

Skill gustavo-meilus/superpipelines/plugins/superpipelines/skills/optimizing-a-pipeline

Optimize an existing named Superpipelines workflow for topology, model tiers, cost, latency, and reliability.From its SKILL.md

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npx -y skills add gustavo-meilus/superpipelines --skill optimizing-a-pipeline

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SKILL.md

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Optimizing a Pipeline — On-Demand Optimization Workflow

<overview> Top-level orchestrator for optimizing an existing pipeline. A read-only `pipeline-optimizer` analyst surveys the selected bundle across four axes (topology structure, model-tier cost, past-run signals, protocol/prompt quality); a discovery session (4D → brainstorm → grill) converges the findings into an `optimization_plan` with the user; the approved plan is batch-applied atomically through the existing mutation and `change-models` engines, gated by a `pipeline-auditor` DELTA pass and proven by a mandatory full audit. Orchestration lives only here (`SUB_AGENT_SPAWNING: FALSE`); the optimizer never mutates (render-inline, #33). </overview> <glossary> <term name="Opportunity">A single proposed improvement rendered by the optimizer, carrying an axis, impact, affected steps, and a suggested engine.</term> <term name="optimization_plan">The reconciled set of accepted/rejected/modified opportunities returned by `sk-pipeline-grilling MODE=optimization`.</term> <term name="Batch apply">Staging ALL approved changes in one `edit-{ts}/` and promoting them all-or-nothing — a multi-change optimization is one semantic change.</term> <term name="Snapshot">A pre-mutation copy of the bundle in `edit-{ts}/backup/` plus a git checkpoint, used for rollback.</term> </glossary>

Workflow Phases

<protocol> ### PHASE 0 — SELECTION - Reuse the `running-a-pipeline` Phase 0 multi-scope discovery pattern: call `sk-pipeline-paths.ENUMERATE_ALL_SCOPE_ROOTS(workspace)`, merge every `<root>/superpipelines/registry.json`, annotate each entry with `source_tier` and `scope`. - Present the pipelines; capture the selection `{ROOT, P, pattern, source_tier}`. - Load `sk-platform-dispatch` → `DETECT()` → `platform_profile` (cache once; same probe/fallback rules as `running-a-pipeline` Phase 0.25). Emit every `platform_profile.degradation_warnings` entry. - IF `$ARGUMENTS` named a pipeline, pre-select it; still confirm before proceeding.

PHASE 0.5 — NO-ACTIVE-RUN SOFT GATE

  • Scan <ROOT>/superpipelines/temp/{P}/* for run directories; read each pipeline-state.json top-level status.
  • IF any run is running or escalated (non-terminal):
    • AskUserQuestion: (a) discard those run states and proceed (delete the non-terminal run dirs), or (b) abort and let the user finish/handle them manually.
  • <HARD-GATE>NEVER stage or mutate the bundle while a running/escalated run exists unless the user explicitly chose discard. escalated/failed run dirs are never deleted silently.</HARD-GATE>

PHASE 1 — SURVEY

  • Dispatch the read-only pipeline-optimizer via profile-driven dispatch — the SAME platform_profile.capabilities.dispatch_mechanism branching used by creating-a-pipeline Phase 4 (native_taskTask(); native_subagent / model_driven → platform-native; inline → Tier 2 inline loop). Hand it absolute paths (resolved via sk-pipeline-paths) to topology.json, the bundle agents/ dir, the temp/{P}/*/pipeline-state.json history, any run-telemetry.jsonl, and the platform_profile.
  • The optimizer renders an opportunity report as terminal output and NEVER writes a file.
  • <HARD-GATE>Persistence is the orchestrator's job (#33): write the rendered report to <ROOT>/superpipelines/temp/{P}/optimize-{ts}/findings.md (ensure the dir exists first).</HARD-GATE>
  • IF the optimizer returns DONE_WITH_CONCERNS (telemetry-blind axes), surface the advisory on enabling the opt-in telemetry hook (CLAUDE_CODE_ENHANCED_TELEMETRY_BETA=1 + register subagent-telemetry). NEVER auto-edit settings.
  • IF no opportunities were found: report that and exit cleanly — nothing to optimize.

PHASE 2 — DISCOVERY

  • sk-4d-method — frame what "better" means for this pipeline (cost? latency? reliability? clarity?). Produce the hardened constraints.
  • superpipelines:brainstorming — divergent exploration of alternative redesigns and their trade-offs against the findings.
  • sk-pipeline-grilling GRILL(MODE=optimization, findings, hardened) — convergent: walk each opportunity one at a time, capturing accept/reject/modify + rationale. Returns the optimization_plan.
  • <HARD-GATE>The grilling reconciliation gate must close with ZERO unresolved opportunities before Phase 3.</HARD-GATE>

PHASE 3 — PLAN GATE (single human approval)

  • Present the optimization_plan concretely: the chosen opportunities, the resulting topology diff (steps merged/split/parallelized/removed), the model-tier diff, and the predicted effect against the hardened success criteria.
  • ONE AskUserQuestion approval for the whole plan (plan-gate + batch-apply: the changes interact and must be approved/audited/promoted together).
  • <HARD-GATE>No staging, snapshot, or mutation before this approval returns yes. Rejection ends the workflow with the findings preserved.</HARD-GATE>

PHASE 4 — BATCH APPLY (atomic)

  • Snapshot: copy the bundle to <ROOT>/superpipelines/temp/{P}/edit-{ts}/backup/ AND create a git checkpoint commit. This is the rollback source.
  • Stage ALL changes in one edit-{ts}/ (never promote partials):
    • Topology changes (merge/split/parallelize/reorder/remove) route through the existing mutation engines — updating-a-pipeline-step / adding-a-pipeline-step / deleting-a-pipeline-step (architect STEP-* modes), staging into the shared edit-{ts}/.
    • Model-tier changes route through change-models Mode C (per-agent model_tier: override) — recommend a tier direction only; concrete model IDs stay in profile JSON (DEPENDENCY_INVERSION).
    • Advisory-only (Axis-4) opportunities are NOT auto-applied; surface them for manual follow-up.
  • DELTA audit: run ONE combined pipeline-auditor DELTA pass over the whole staged delta.
  • <HARD-GATE>SEV-0/1 == 0 is required to promote. Any SEV-0/1 → roll back from the snapshot, restore the git checkpoint, and surface the findings. Do NOT promote a partial set.</HARD-GATE>
  • Promote all-or-nothing: on a clean DELTA audit, promote the entire edit-{ts}/ atomically.
  • Stamp: bump plugin_version on topology.json, the registry.json entry, and every touched agent; set topology.metadata.optimization = { ts, opportunities_applied: [...], baseline_ref } (baseline_ref = the git checkpoint).

PHASE 5 — POST-APPLY PROOF

  • <HARD-GATE>MANDATORY full pipeline-auditor pass over the promoted bundle. Any SEV-0/1 → auto-rollback from the snapshot + git checkpoint.</HARD-GATE>
  • Graph-integrity check: every depends_on resolves to an existing step; no orphan edges; no unreachable non-entry step; I/O contracts chain (each consumed input is produced upstream). Any failure → auto-rollback.
  • Persist the auditor report per the commands/audit-steps.md REPORTING contract (orchestrator owns persistence; ensure audit/ exists; write audit/latest.md; update registry.json last_audit).
  • Offer an optional live smoke-run via running-a-pipeline (not mandatory — PARITY_TESTING: MANUAL_PHASE1).
  • Write the durable provenance report to <ROOT>/superpipelines/pipelines/{P}/optimization-report-{ts}.md (opportunities applied/rejected, diffs, audit verdict, baseline_ref). </protocol>
<invariants> - No mutation under a live run — Phase 0.5 soft gate with explicit discard-or-abort; `escalated`/`failed` runs are never deleted silently. - Snapshot (`edit-{ts}/backup/`) + git checkpoint precede any production write. - All-or-nothing promotion; roll back from the snapshot on any DELTA-audit, full-audit, graph-integrity, or promotion failure. - SEV-0/1 == 0 gates BOTH the DELTA (pre-promote) and full (post-promote) audits. - `plugin_version` is re-stamped on `topology.json`, the registry entry, and every touched agent on promotion. - The optimizer is read-only and renders inline; the orchestrator persists (#33). Orchestration is top-level only (`SUB_AGENT_SPAWNING: FALSE`). - Isolation-correctness and frontmatter-compliance are delegated to `pipeline-auditor` — never re-checked here (`DEPENDENCY_INVERSION`). - No concrete model IDs in this body; model-tier changes name a tier direction only and route through `change-models` Mode C. - One plan gate (Phase 3); batch-apply is one semantic change. </invariants>

Red Flags — STOP

  • "The optimizer can write findings.md itself to save a step." → STOP. Read-only render-inline; the orchestrator persists (#33).
  • "Promote the topology changes now; apply the tier changes after." → STOP. Batch-apply is all-or-nothing; partial promotion leaves the bundle in an unaudited interleaved state.
  • "The DELTA audit found a SEV-1, but it's minor — promote anyway." → STOP. SEV-0/1 == 0 gates promotion. Roll back.
  • "A run is escalated, but optimizing won't touch it." → STOP. Phase 0.5 gate: mutating the definition under a live run corrupts resume. Discard explicitly or abort.
  • "Skip the snapshot — the git checkpoint is enough." → STOP. Both are required; the snapshot is the staging-local rollback source, the checkpoint the version baseline.
  • "Re-check the isolation defect while surveying." → STOP. That is pipeline-auditor's job (DEPENDENCY_INVERSION); the optimizer delegates.
  • "Apply the advisory (Axis-4) quality fixes automatically." → STOP. Advisory-only opportunities are surfaced for manual decision, never auto-applied.

Rationalization Table

<rationalization_table>

ExcuseReality
"One plan gate is too slow — approve each change inline."The changes interact; piecemeal approval can promote a half-coherent topology. One plan, one gate, one atomic promote.
"The optimizer already audited the bundle."It did not — it surveys opportunities. Compliance/isolation is the auditor's DELTA + full passes (DEPENDENCY_INVERSION).
"Skip the post-apply full audit; the DELTA passed."The DELTA only saw the changed delta. The full pass + graph-integrity prove the whole bundle still chains.
"No telemetry, so skip past-run analysis silently."Degrade and SAY SO — surface the opt-in hook advisory so the next run can ground cost/latency signals.
"Down-tier this step to fast and name the model."Name a tier direction only; route through change-models Mode C. Concrete IDs live in profile JSON.
"Rolling back is wasteful after staging so much."A failed audit/graph check means the staged set is unsafe. Rollback is the contract, not a failure of effort.
</rationalization_table>

Reference Files

  • agents/pipeline-optimizer.md + skills/pipeline-optimizer-protocol/SKILL.md — the read-only survey worker.
  • skills/pipeline-optimizer-references/references/opportunity-taxonomy.md — opportunity classes + heuristics.
  • sk-pipeline-grilling/SKILL.mdMODE=optimization reconciliation (returns optimization_plan).
  • sk-4d-method/SKILL.md · superpipelines:brainstorming — the discovery session.
  • updating-a-pipeline-step · adding-a-pipeline-step · deleting-a-pipeline-step — topology mutation engines (edit-{ts}/ staging).
  • change-models/SKILL.md — Mode C per-agent model_tier: override.
  • pipeline-auditor + commands/audit-steps.md — DELTA + full audit and report persistence.
  • running-a-pipeline/SKILL.md — Phase 0 discovery pattern reused here; optional Phase 5 smoke-run.
  • sk-pipeline-paths/SKILL.md — scope-root and path resolution.

What ships with it

Read from the repository

Just SKILL.md. No reference files, no scripts.

Gives 0 of the 12 instructions most performance cost skills give in ~2.8k tokens

Counted across 797 of the 1,117 authors here whose files we hold, read 2026-09-06

  • Check for product marketing context firstin 46 of 797, across 20 files
  • Measure before optimizingin 31 of 797, across 25 files
  • Profile first to identify the actual bottleneckin 23 of 797, across 22 files
  • Verify your robots.txt allows AI crawlersin 21 of 797, across 12 files
  • Import directly and avoid barrel filesin 19 of 797, across 15 files
  • Spawn all runs in the same turnin 18 of 797, across 11 files
  • Write a draft of the skillin 17 of 797, across 10 files
  • Understand the user's intentin 17 of 797, across 10 files
  • Use React.cache for per-request deduplicationin 16 of 797, across 11 files
  • Profile before optimizingin 16 of 797, across 14 files
  • Include specific numbers with sourcesin 15 of 797, across 8 files
  • Add lazy loading to below-fold imagesin 15 of 797, across 10 files

Said here and by no other author read

  • Scan roots and capture pipeline selection
  • Check for active runs before mutating
  • Dispatch the read-only pipeline-optimizer
  • Persist the rendered optimizer report
  • Run 4D method, brainstorming, and grilling
  • Present optimization plan for user approval

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.

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