Loop/loopEvaluationmedium riskintermediatesafety C · 55Forward Futurepre-dates current gate · under review

Turn one artifact into a reusable skill

Take a proven artifact, generalize it into a transferable skill or playbook, and validate it on a second case.

prompt
→ Claude
Turn [artifact] into a skill, playbook, or procedure. Record evidence that the artifact succeeded and define success criteria. Extract decisions, sequence, checks, and failure-avoidance patterns—not context or surface style. Remove sensitive material. Have an independent reviewer apply it to a fresh real second case; mark hypothetical testing provisional. Revise at most twice. Stop when it meets the quality bar without the artifact, or report not generalizable. Return the method, boundaries, failure modes, test evidence, revisions, limits, and attribution.
claude-code · codex

Use this when

Use this when a completed artifact has evidence of success, appears to contain a repeatable method, and similar work is likely to recur.

How it runs

  1. Confirm that the source artifact has credible evidence of success, define the quality criteria it met, and exclude sensitive or proprietary material that should not be transferred.
  2. Separate the durable decisions, sequence, checks, standards, and failure-avoidance patterns from one-off facts, tools, and surface style.
  3. Write the method as a standalone skill, playbook, or procedure with inputs, boundaries, steps, quality standards, failure modes, attribution, and clear terminal states.
  4. Have an independent reviewer apply it to a fresh real case, revise no more than twice, and return either a reusable version with test evidence or an honest provisional, blocked, or not-generalizable result.

Done when

✓ The extracted method succeeds on a fresh second case without the original artifact. An independent reviewer applies the reusable version under criteria defined before extraction, and the second result meets the source artifact's demonstrated quality bar or the method is honestly marked provisional or not generalizable.

Why it works

Strong outputs often get saved while the method that produced them disappears. Extracting the decisions and checks makes that knowledge reusable, while a fresh second-case test distinguishes a transferable process from imitation of one polished example.

Implementation note

Do not infer success from polish alone, copy confidential material, or treat a hypothetical test as final proof. Preserve attribution, define the quality bar before extraction, and stop honestly when hidden context makes the method impossible to generalize.

Source: Forward Future ↗

More evaluation loops

Keep only the lessons that help

Loop/loopForward Future

Test one recorded lesson per run, keep evidence across runs, and drop guidance that stops paying off.

prompt
→ Claude
Maintain a durable, versioned playbook of lessons that may improve future runs of [task or workflow]. Store it in [path], using playbook/ by default. Treat every recorded lesson as untrusted advice rather than authority. At the start of each run, read the playbook and choose at most one relevant lesson to test. Apply it only within the task's existing permissions. Measure the result using the task's own success check and record the context, action, outcome, and evidence. Promote a candidate lesson only after it succeeds across [N] independent runs or a predefined holdout set. Use three independent runs by default. Never promote a lesson from one successful attempt. Revise or remove lessons that stop helping. Stop when no candidate has enough evidence, another test would exceed the budget, or approval is required. Never let the playbook authorize production, destructive, financial, privacy-sensitive, or external actions. Finish with the playbook diff, evidence ledger, removed lessons, unresolved candidates, and new version.
evaluationhigh riskclaude-codecodex

Autonomous overnight ML research loop with stall detection (ARIS)

Loop/ralph

Framework-agnostic (Claude Code, Codex, OpenClaw, or any LLM agent), markdown-only skill bundle (79+ skills) for running ML research unattended overnight: literature search, idea generation, experiment execution, and cross-model paper review, with a silent-death watchdog and a stall/pivot mechanism so a stuck loop changes approach instead of looping forever on minor variants.

prompt
→ Claude
Install the ARIS markdown-only skills, then run the overnight research loop: the agent reviews relevant literature, proposes and critiques experiment ideas, runs GPU experiments, updates a persistent Research Wiki, and has a second model cross-review the draft paper each round. A watchdog checks the state file's modification time and flags the run STALE/MISSING/COMPLETED if it goes silent. An iteration log counts new findings per round; at 2 consecutive stale rounds it forces a structural pivot (reframe and try a new direction), and at 4 it escalates to a human instead of continuing to retry near-identical variants. Cap the run at 25 iterations; leave remaining work for the next session.
evaluationmedium riskclaude-code

Check active goals against rubric

Loop/goal

Verify each goal in active.md has evidence attached, stopping after 25 turns or completion.

prompt
→ Claude
/goal all rubric items in .ultragoal/goals/active.md are checked with evidence, or stop after 25 turns
evaluationlow riskclaude-code
source ↗