agentsclimarketplace

Optimizing a pipeline

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

Loop Engineering for AI coding agents, with real review boundaries. Your AI reviewer cannot edit code. Structurally.

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

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

  • 5 stars5 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

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

SKILL.md

11.4 KB, as published. Nobody here has run it

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.

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.