forge

---

name: forge description: "Autonomous development with experimental self-correction. Iterates through quality gates until antifragile or blocked." user-invocable: true allowed-tools: Read, Write, Edit, Bash, Grep, Glob, Agent, WebSearch, WebFetch disable-model-invocation: true kernel: kind: workflow version: 1 side_effects: writes_remote confirmation: on_side_effect

The forge metaphor is literal: HEAT — generate competing approaches, inject entropy HAMMER — iterate implementation against failing tests QUENCH — quality gates, adversarial review, convergence check TEMPER — experiment on output, discover emergent patterns, self-correct ANNEAL — if brittle, reheat and try a different crystalline structure

TEMPER is the evolution: the forge doesn't just build — it measures, hypothesizes, tests, and adapts. Every cycle produces data. Every data point refines the next cycle.

Run this overnight. Come back to shipped code + experimental evidence + emergent learnings.

<skill_load> always: skills/quality/SKILL.md, skills/build/reference/testing.md, skills/build/reference/git.md, skills/build/SKILL.md on_classify: bug: skills/debug/SKILL.md refactor: skills/build/reference/refactor.md on_domain: api: skills/architecture/reference/api.md, skills/architecture/reference/backend.md auth: skills/tearitapart/reference/security.md frontend: skills/frontend/SKILL.md backend: skills/architecture/reference/backend.md on_tier: 2+: skills/orchestration/SKILL.md </skill_load>

<on_start>

agentdb read-start
agentdb emit command "forge-start" "" '{"goal":"...","max_budget_usd":N.NN}'

Load ALL always-skills immediately. Load task/domain skills after classify.

Budget preflight (mandatory before any forge cycle):

1. Confirm max_budget_usd is set on the contract or the forge invocation.
2. If unset: AskUserQuestion — "Forge runs are autonomous loops. Set a cost ceiling (default: $5 tier 2 / $15 tier 3), or proceed unbounded?" Never proceed unbounded silently.
3. Cumulative cost tracked via agentdb emit each cycle. Hard stop at 100% of budget.
4. Hitting the cap = forge halts and reports. This is the circuit breaker working.

Why: one stuck retry at $0.40-0.60/query × 200 retries = $120 silently. The cap is the only mechanism that prevents this — there is no in-session signal that cost is runaway. See skills/orchestration/SKILL.md <max_budget_usd_invariant>. </on_start>

**RULE: Research without verification is theory fiction.** (LRN-F11)
Never generate approaches from research alone. Each candidate must have a verification path
(test, prototype, visual proof) defined upfront. If you can't verify it, don't propose it.

**RULE: Avoid specific multiplier claims.** (H104 graduated meta-rule)
Approaches that promise "3-5x improvement" or "always better" have a 71% refutation rate.
Frame as directional: "reduces X" not "reduces X by 80%." Measure after, not before.

1. Read agentdb context + _meta/research/ for prior work.
1b. Measure entropy: check agentdb learning count in domain, test coverage, recent failures.
    Low entropy → generate 1 approach (streamlined). High entropy → generate 3 approaches (full exploration).
2. Classify task: type, tier, domain.
3. Generate 2-3 candidate approaches (not variations — genuinely different strategies).
4. For each: files affected, tests needed, effort estimate, known risks, **verification method**.
5. For each: **hypothesis** — what testable claim does this approach make?

Tier 1: generate inline.
Tier 2+: spawn parallel surgeon agents, one per approach.
Tier 3: spawn full council (researcher + scout in parallel → dreamer → surgeons).

```bash
agentdb emit command "forge-heat" "" '{"approaches":N,"tier":N,"hypotheses":["H-FORGE-1","H-FORGE-2"]}'
```

1. Write failing tests FIRST (red). Edge cases before happy paths.
2. Implement minimal code to pass (green).
3. Refactor while green.
4. Run full suite: tests + lint + types.
5. **Measure**: record metrics that will feed TEMPER phase (tokens, time, error count, coverage).

Inner loop (max 5 strikes per approach):
  - failing → fix implementation, not tests
  - passing → proceed to quench
  - **stuck after 3 strikes → [D1 — non-repair classifier] CLASSIFY before re-trying:**
    - **APPROACH-GAP** (the strategy is wrong, but the correct behavior is knowable) →
      switch to next candidate approach from heat phase (existing behavior).
    - **CONTRACT-GAP** (the spec is genuinely ambiguous / the correct behavior is
      undeterminable, OR the same failure survives a tactic that should have fixed it —
      the model cannot conceptually repair it) → **STOP. Do NOT burn anneals.** Escalate:
      name the exact ambiguous/un-fixable criterion and hand to the human (or flag the
      contract for redesign). Switching approaches cannot fix a contract/concept gap.
    Why: detection ≠ repair (Ayoob, n=30 — a gate detected every failure but retries kept
    the wrong action when the model couldn't grasp the distinction). Re-annealing a
    contract-gap wastes 3 full cycles on something no approach repairs.

```bash
agentdb emit command "forge-hammer" "" '{"approach":"X","strikes":N,"tests_passing":N,"metrics":{}}'
```

<entropy_injection>
  Spawn adversary (or self-adversary for tier 1). Their mandate:
  DON'T ask "is this valid?" ASK "can I destroy this?"

  **Coordination checks FIRST** (H093 — 4.3x more impactful than code quality):
  - Did agents touch overlapping files?
  - Did agents claim completion without evidence?
  - Did scope drift beyond contract?
  - Is there duplicate work across branches?

  Then code quality attack vectors:
  - What input breaks this?
  - What race condition exists?
  - What happens at 10x scale?
  - What edge case was missed?
  - What assumption is wrong?
  - What security hole exists?

  The adversary writes a SPECIFIC failing test or proof, not a vague concern.
</entropy_injection>

<integrity_measure>
  Score: 0.0 (shattered) to 1.0 (antifragile).
  - >= 0.8: SURVIVED. Proceed to TEMPER.
  - >= 0.6: CRACKED. Fixable flaws. Back to hammer with adversary feedback.
  - < 0.6: SHATTERED. Approach is fundamentally flawed. Anneal.
</integrity_measure>

```bash
agentdb emit command "forge-quench" "" '{"integrity":0.X,"verdict":"survived|cracked|shattered"}'
```

After quench survives, run experiments on the forged output:

1. **Measure what changed**: diff size, token impact, test count, coverage delta.
2. **Test the hypothesis from HEAT**: does the measured outcome match the predicted outcome?
   If yes → record as supporting evidence. If no → record what actually happened.
3. **Cross-reference with AgentDB**: does this change interact with known patterns or failures?
   Query: `SELECT insight FROM learnings WHERE domain = '{task_domain}' AND type = 'failure'`
   Any match = potential regression risk. Investigate before shipping.
4. **Discover emergent patterns**: what surprised you? What worked that shouldn't have?
   What failed that should have worked? These are the seeds of new hypotheses.
5. **Self-correct**: if temper reveals a problem quench missed, go back to hammer.
   If temper reveals an opportunity, seed a hypothesis for the next forge cycle.

<emergence>
The temper phase is where the forge develops intelligence across runs:
- Cycle 1: baseline measurements, hypothesis formation
- Cycle 2: compare against cycle 1 measurements, detect trends
- Cycle 3+: patterns emerge — the forge knows which approaches work for which problems
- Cross-session: temper learnings feed into HEAT phase of future forge runs
</emergence>

```bash
# Record measurements
agentdb learn pattern "forge-temper: {what worked and why}" "{metrics}"

# Seed hypothesis if emergent pattern found
agentdb learn pattern "forge-hypothesis: {testable claim}" "{evidence from this cycle}"

# Record cycle metrics for cross-session learning
agentdb emit command "forge-temper" "" '{"cycle":N,"hypothesis_confirmed":true|false,"emergent":["..."],"metrics":{}}'
```

If temper passes → proceed to SHIP.
If temper reveals issue → back to HAMMER with temper feedback.

1. Record WHY it shattered: agentdb learn failure "approach X failed because Y"
2. Penalize this approach: mark as explored-and-failed.
3. **Experiment on the failure**: WHY did this approach shatter? Is it a pattern?
   Check: does this shatter reason match any prior forge failures?
   If pattern found → skip entire class of approaches, not just this variant.
4. Return to HEAT with the shatter reason + pattern analysis as new constraints.
5. The next approach MUST be structurally different.

This prevents hammering a fundamentally flawed approach into submission.
Sometimes the metal needs a different alloy, not more force.

Max anneals: 3. After 3 structural failures → STOP.
"Tried 3 distinct approaches. All shattered. Here's why. Human decision needed."

```bash
agentdb learn failure "forge-shatter: approach X because Y" "{diff, test output, adversary verdict}"
agentdb emit command "forge-anneal" "" '{"reason":"X","approaches_exhausted":N,"pattern_match":"..."}'
```

Profile-gated:
- local: commit to main
- github: commit + push
- github-oss: feature branch → PR → self-review via /kernel:review
- github-production: feature branch → PR → request review

```bash
agentdb learn pattern "forge-shipped: {approach}, integrity {score}, {key metric}" "{evidence}"
agentdb emit command "forge-ship" "" '{"iterations":N,"integrity":0.X,"approach":"X","temper_findings":[]}'
agentdb write-end '{"command":"forge","iterations":N,"tests":N,"integrity":0.X,"shipped":true,"emergent_hypotheses":N}'
```

Council communicates via agentdb. Each reads prior agents' output. You orchestrate the sequence and handle annealing.

<loop_control> continue_if: progress (tests improving, integrity increasing, temper passing) anneal_if: integrity < 0.6 temper_back_to_hammer_if: temper reveals missed issue stop_if: 3 anneals OR 10 iterations OR scope creep on_stop: audit trail (approaches+scores, shatter reasons, temper findings, emergent hypotheses, learnings) </loop_control>

<experimental_behavior> The forge is not just a build tool — it's a learning system.

Within a single run:

• Each cycle generates data (metrics, failures, successes)
• TEMPER phase analyzes that data and forms hypotheses
• Subsequent cycles test those hypotheses
• Emergent patterns are captured as learnings

Across runs:

• HEAT reads prior forge learnings (forge-temper, forge-shatter, forge-shipped)
• Approaches that worked before are preferred (but not blindly repeated)
• Shatter patterns are avoided (but not blindly excluded — context matters)
• The forge gets smarter with each use, not just from code but from self-measurement

What makes this different from plain iteration:

• Plain iteration: try → fail → try again
• Forge with temper: try → measure → hypothesize → test hypothesis → learn → try smarter
• The difference is the hypothesis step: the forge doesn't just retry, it asks WHY </experimental_behavior>