# System Deep Loop

> Routes research, review, AI Council, and improvement modes through registry-selected packets.

- **Type:** Skill
- **Install:** `agentstack add skill-michelkerkmeester-skilled-harness-spec-driven-agent-loops-system-deep-loop`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [MichelKerkmeester](https://agentstack.voostack.com/s/michelkerkmeester)
- **Installs:** 0
- **Category:** [Agent Skills](https://agentstack.voostack.com/c/agent-skills)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [MichelKerkmeester](https://github.com/MichelKerkmeester)
- **Source:** https://github.com/MichelKerkmeester/skilled-harness__spec-driven-agent-loops/tree/main/.opencode/skills/system-deep-loop

## Install

```sh
agentstack add skill-michelkerkmeester-skilled-harness-spec-driven-agent-loops-system-deep-loop
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# System Deep Loop

One skill, four active workflow families, one nested runtime layer. `system-deep-loop` is the public, advisor-routable home for active deep-loop personas; `runtime/` is the frozen, MCP-free infrastructure layer it consumes (formerly the separate `deep-loop-runtime` skill, merged into this hub 2026-07-08). This hub holds NO per-mode convergence, state, or synthesis logic — each active mode keeps its own contract in its packet, and the hub only routes by `workflowMode` through `mode-registry.json`.

Use `@context` for one-shot retrieval, `/deep:research` for iterative investigation with a bounded context snapshot, `/deep:review` for iterative audit with a bounded review snapshot, or `/speckit:plan` for implementation planning.

---

## 1. WHEN TO USE

Use this skill (through the hub) for any active deep-loop workflow. Invoke it as `Skill(system-deep-loop)` (optionally with a mode hint such as `research: `); the hub classifies the request, resolves a `workflowMode`, and loads the matching nested mode packet. Active `/deep:*` commands and native agent types remain as complementary surfaces over the same packets.

| Mode | Use it for | Packet | Command | Agent |
|------|-----------|--------|---------|-------|
| **research** | Outward, web + code iterative investigation → `research/research.md` | `system-deep-loop/deep-research/` | `/deep:research` | `deep-research` |
| **review** | Iterative review loop → P0/P1/P2 findings + verdict | `system-deep-loop/deep-review/` | `/deep:review` | `deep-review` |
| **ai-council** | Multi-seat planning deliberation → `ai-council/**` artifacts | `system-deep-loop/deep-ai-council/` | `/deep:ai-council` | `ai-council` |
| **improvement** (3 lanes) | Evaluator-first improvement: `agent-improvement`, `model-benchmark`, `skill-benchmark` | `system-deep-loop/deep-improvement/` | `/deep:agent-improvement` · `/deep:model-benchmark` · `/deep:skill-benchmark` | `deep-improvement` |

### When NOT to Use
- A single quick read/edit (no loop) — use the relevant code or doc skill directly.
- Backend/runtime support (executor, coverage-graph, scoring, fan-out) — that is `runtime/`, consumed here, not invoked as a user workflow (formerly the separate deep-loop-runtime skill).

---

## 2. SMART ROUTING

Routing is **registry-driven** (invokable-hub, Option E) in two stages. Stage 1 (hub → mode): `mode-registry.json` is the single source of truth; the hub reads it and does not re-derive the mapping. When invoked as `Skill(system-deep-loop[, ": "])`, the hub classifies the request to a `workflowMode`, resolves it through the registry, and loads `registry[mode].packet`. The advisor routes any deep-loop query to the single identity `system-deep-loop`; the hub then picks the mode. Stage 2 (mode → leaves): the root `ROUTER.md` maps the request's deep-loop intent to the exact packet-local leaf resources that mode loads. The two layers stay separate: the hub never emits leaf paths, and the surface router never re-decides the mode. The `/deep:*` commands and native agent types remain as complementary surfaces — they reach the same packets through static routers/agent definitions — and the hub holds NO per-mode logic.

> **Compiled routing (default-on, flag-gated, additive).** Resolve the mode via the compiled router contract first:
> ```bash
> node .opencode/bin/compiled-route.cjs --hub system-deep-loop --prompt ""
> ```
> Follow the returned decision — `route` (use its `targets`), `clarify`/`defer` (disambiguate), `reject` (refuse). On a `{"servingAuthority":"legacy"}` sentinel or any error, use the routing below. The front door self-gates on serving-authority. Compiled routing is now the default for `system-deep-loop`; set `SPECKIT_COMPILED_ROUTING=0` to force legacy routing fleet-wide — the explicit kill-switch.

### Surface Router — per-mode leaf sets

Stage 2 of routing lives in `ROUTER.md` at the hub root, next to `SKILL.md` and `README.md`. It defines the per-mode leaf-intent model (the research loop protocol/state/convergence leaves, the review loop protocol/completion/convergence leaves, the ai-council loop protocol/scoring/output-schema leaves, the agent-improvement candidate/score/promotion-gate leaves, and the model- and skill-benchmark evaluator leaves), the machine-readable `DEFAULT_RESOURCE` / `INTENT_SIGNALS` / `RESOURCE_MAP` block that the deterministic router-replay and benchmarks parse, and the how-to-read rules (dominant intent → one leaf set; near-tied intents → deduped union; no keyword match → hub `defer`, never a silent default to a mode). Every `RESOURCE_MAP` path is packet-qualified and converts to the canonical `(workflowMode, leafResourceId)` pair at the one contract boundary.

`ROUTER.md` stays a separate document on purpose: the router-replay contract resolves the hub's mode from `hub-router.json` and reads the leaf sets from the surface document — the machine block must not move into `SKILL.md` (the replay would treat it as the hub's own router and lose the mode projection) or into `hub-router.json` (schema handoff-ambiguity rule).

This hub is an intent/registry router, not a keyed resource-discovery router: at runtime it omits `discover_markdown_resources` and only guards registry-selected packet loads, never selecting hub-level `references//` or `assets//` resources by runtime key. It does carry a second-layer surface router at the root `ROUTER.md` — a benchmark/replay artifact (mirroring sk-prompt/sk-code) that maps a resolved mode's intent to packet-qualified child leaves so router-replay can emit canonical `{workflowMode, leafResourceId}` typed pairs against `leaf-manifest.json`. That surface layer never re-decides the mode and is not a runtime hub discovery surface; the hub still routes by `workflowMode` through `mode-registry.json` alone.

### Note on the frontmatter `allowed-tools` grant

The hub's own routing logic is read-only (classify, guard a path, load a packet). The frontmatter `allowed-tools` list is nonetheless broad because, per the two-axis hub canon contract, a hub's tool grant MUST equal the exact union of every registered mode's `toolSurface.allowed` in `mode-registry.json` — not the tool set the hub's own logic uses. This is a hard invariant enforced by `parent-skill-check.cjs` (check 3j: "hub allowed-tools equals the union of mode tool surfaces"); narrowing the frontmatter grant to only what routing-only logic needs would fail that check and break every mode whose `toolSurface` isn't a subset of the narrowed list. Treat the breadth of `allowed-tools` as evidence of the child modes' combined needs, not of mutating logic living in the hub.

### The three-tier discriminator
- **`workflowMode`** — the public active mode key: `research`, `review`, `ai-council`, and the three improvement lanes `agent-improvement`, `model-benchmark`, `skill-benchmark`.
- **`runtimeLoopType`** — the graph-backed convergence key consumed by `runtime/scripts/convergence.cjs` (validated against active `research|review|council`). **Explicit `null` for custom and improvement backends; never inferred from `workflowMode`.** Note `ai-council` maps to `runtimeLoopType: council`.
- **`backendKind`** — which backend runs the mode: `runtime-loop-type` (research/review/ai-council) or `improvement-host` (`deep-improvement/scripts/shared/loop-host.cjs --mode`).

### Routing rule
```
UNKNOWN_FALLBACK_CHECKLIST = [
    "Confirm whether this is research, review, ai-council, or one improvement lane (agent-improvement, model-benchmark, skill-benchmark) work",
    "Confirm the target artifact: research.md, a review verdict, ai-council deliberation artifacts, or an improvement candidate",
    "Confirm the matching /deep:* command or agent type when one is already known",
    "Confirm the backend expectations: runtimeLoopType (research/review/council) or the improvement-host lane",
]

classify the request to a workflowMode (dominant deep-loop intent; mode hint like "research: ..." overrides)
guard mode-registry.json inside SKILL_ROOT and read it as data
if classifier confidence is low or no mode dominates:
  → return UNKNOWN_FALLBACK with disambiguation_checklist = UNKNOWN_FALLBACK_CHECKLIST
else:
  → resolve workflowMode from the hint / classified intent (or the /deep:* command / advisor alias)
  → find registry[mode]; if missing, return UNKNOWN_FALLBACK instead of loading a guessed path
  → guard registry[mode].packet/SKILL.md inside SKILL_ROOT and load it only if the packet directory and SKILL.md both exist
       e.g. registry["research"].packet → system-deep-loop/deep-research/SKILL.md
       (the 3 improvement modes all share the system-deep-loop/deep-improvement/ packet)
  → if registry[mode].runtimeLoopType !== null: backend = convergence.cjs --loop-type 
     else: backend = improvement loop-host (--mode), per backendKind
```

Router-driven loads MUST use `_guard_in_skill(relative_path)` before `load()`, reject paths that escape this skill or do not end in `.md`, and check `if packet_base.exists()` plus `if packet_skill.exists()` before loading. The fallback must name the unresolved `workflowMode` when known, avoid loading any guessed packet, and ask the operator to provide one of the registered modes or the matching `/deep:*` command.

Intent classification favors the single dominant active deep-loop mode; a mode hint (`research: ...`, `review: ...`, `ai-council: ...`, or an improvement lane) overrides the classifier. The legacy advisor projection maps stay hardcoded and drift-guarded against the registry, and the command files remain static routers with hardcoded asset/mode routing; neither resolves from `mode-registry.json` at runtime, but both stay equal to its projection.

Per-mode behavior is **not flattened**: each active packet keeps its own convergence math, state shape, artifacts, and tool-permission guards (research has WebFetch; review/ai-council are code/inward-only; improvement is the only direct mutation family). Exactly one `graph-metadata.json` — this hub's — is preserved, so the advisor discovers exactly one skill identity regardless of which surface (hub `Skill()`, `/deep:*` command, or agent) reaches a mode.

---

## 3. HOW IT WORKS

### Layout
```
system-deep-loop/
  SKILL.md               # this routing hub (no per-mode logic)
  ROUTER.md              # stage-two surface router (deep-loop intent -> leaf sets)
  mode-registry.json     # the three-tier discriminator + advisorRouting (single source of truth)
  graph-metadata.json    # the ONE advisor identity for the whole skill
  deep-research/   deep-review/   deep-ai-council/   deep-improvement/   # active mode packets
  shared/synthesis/      # workflows-shared synthesis (e.g. emitResourceMap)
```

Each active mode packet keeps its own `SKILL.md`, `references/`, `scripts/`, `assets/`, `feature-catalog/`, or `manual-testing-playbook/` as applicable, with internal paths repointed and **no per-packet `graph-metadata.json`** — only this hub carries one, so the advisor discovers exactly one skill. The `deep-ai-council` packet folder follows the standard `folder == packetSkillName` convention (`deep-ai-council`); its legacy public surfaces (the `/deep:ai-council` command and the `ai-council` agent) intentionally keep the shorter `ai-council` key, so always resolve the packet path through `mode-registry.json` rather than hardcoding it.

### Backend
All modes consume `runtime/` (frozen, MCP-free): executor config, prompt-pack, validation, atomic state, coverage-graph, Bayesian scoring, fan-out, the council primitives, and the promoted plumbing (capability resolver, artifact-root, loop-lock CLI, lifecycle taxonomy). The runtime never gains an `improvement` loopType — improvement stays host-driven.

---

## 4. RULES

### ✅ ALWAYS
- **ALWAYS** resolve a mode through `mode-registry.json` (read the `packet` key; never hardcode a router mapping or packet path in the hub).
- **ALWAYS** keep advisor projection maps hardcoded and drift-guarded against the registry; command mode routing is still hardcoded in the command files and does not resolve through `mode-registry.json`.
- **ALWAYS** keep each mode's convergence/state/artifact contract in its packet — the hub stays logic-free.
- **ALWAYS** keep exactly one `graph-metadata.json` (this hub's) so the advisor sees one skill identity, whether a mode is reached via `Skill(system-deep-loop)`, a `/deep:*` command, or an agent.
- **ALWAYS** keep the root `ROUTER.md` `RESOURCE_MAP` in sync with `leaf-manifest.json` — the leaf sets dual-read to canonical typed pairs at the one contract boundary; the three improvement lanes stay distinct `(workflowMode, leafResourceId)` pairs even though they share the `deep-improvement` packet.
- **ALWAYS** keep `Skill(system-deep-loop)` hub routing, the `/deep:*` commands, and the agent types as complementary surfaces over the same packets — never let one surface fork per-mode logic out of its packet.

### ⛔ NEVER
- **NEVER** add an `improvement` `loopType` to `runtime/convergence.cjs` (improvement is host-driven; `runtimeLoopType` stays `null`).
- **NEVER** infer `runtimeLoopType` from `workflowMode` — read it from the registry (explicit `null` is load-bearing).
- **NEVER** let a read-only mode (research/review/ai-council) reach the improvement mutation scripts (`promote-candidate.cjs`/`rollback-candidate.cjs`).
- **NEVER** add a `graph-metadata.json` or a discoverable skill marker inside a mode packet or `shared/`.

### ⚠️ ESCALATE IF
- A new mode is needed beyond the six registered — extend `mode-registry.json` and open a packet, do not bolt logic onto the hub.
- A change would require the runtime to gain MCP tools or an improvement loopType — that contradicts the architecture; escalate.

---

## 5. REFERENCES

- Backend: `.opencode/skills/system-deep-loop/runtime/` (frozen, consumed by every mode; nested infrastructure, not a separate skill).
- Mode packets: `deep-research/SKILL.md`, `deep-review/SKILL.md`, `deep-ai-council/SKILL.md`, `deep-improvement/SKILL.md` (per-mode detail).
- Commands: the active `/deep:*` commands under `.opencode/commands/deep/` (complementary surface).
- Registry: `mode-registry.json` (the routing contract — the authoritative `packet` paths).
- Surface router: `ROUTER.md` (deep-loop intent to packet-local leaf sets).

---

## 6. SUCCESS CRITERIA

- The hub resolves one primary active `workflowMode` for the request through `mode-registry.json` (improvement folds to the right lane via the registry, never by array order).
- The selected mode packet owns the detailed convergence/state/artifact workflow; the hub stayed routing-only.
- `Skill(system-deep-loop[, hint])` reaches a mode, and the `/deep:*` commands and agent types still reach the same packets.
- Exactly one `graph-metadata.json` exists for the whole skill; no packet carries its own.

---

## 7. INTEGRATION POINTS

### Modes
- `research` — outward web + code iterative investigation (`research/research.md`).
- `review` — iterative review loop, P0/P1/P2 findings + verdict.
- `ai-council` — multi-seat planning deliberation (`ai-council/**` artifacts).
- `improvement` (3 lanes) — evaluator-first agent/model/skill improvement.

### Surfaces and Consumers
- `Skill(system-deep-loop)` is the invokable hub; active `/deep:*` commands and the agent types (`deep-research`, `deep-review`, `ai-council`, `deep-improvement`) are complementary surfaces over the same packets.
- `runtime/` is the frozen, MCP-free backend every mode consumes (nested infrastructure, not a separate skill).
- `/speckit:plan` consumes `@context` packages plus research/review outputs; spec-folder docs consume research/review output.

---

## 8. RELATED RESOURCES

- Pattern: `.opencode/skills/sk-doc/sk-create-skill/references/parent-skill/parent-skills-nested-packets.md` (parent-skill hub + nested packets, the one-graph-metadata invariant).
- Sibling example: `.opencode/skills/sk-prompt/` (the same invokable-hub + `mode-registry.json` Option E pattern).
- Registry: `mode-re

…

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [MichelKerkmeester](https://github.com/MichelKerkmeester)
- **Source:** [MichelKerkmeester/skilled-harness__spec-driven-agent-loops](https://github.com/MichelKerkmeester/skilled-harness__spec-driven-agent-loops)
- **License:** MIT

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-michelkerkmeester-skilled-harness-spec-driven-agent-loops-system-deep-loop
- Seller: https://agentstack.voostack.com/s/michelkerkmeester
- Browse the marketplace: https://agentstack.voostack.com/browse

---
Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.