agentleFS
markoblogomarkoblogo/abvx-agent-skills/skills/loop-hardening-contract/SKILL.md

loop-hardening-contract

Skill16 starsChanged 5 months agoMITView on GitHub

Harden repeated delivery loops with evidence-based harness stripping, immutable runtime-path sprint packets, and broken-window revalidation without automatic revert. Use for Cardputer, browser, CI, runtime, or production work split into repeated sprints.

The file

We split this skill into the rules it gives an agent

9 rules. About 0 are common, 8 are distinctive, and 1 is very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈8

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 1

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    Use this contract after loop-readiness-review when repeated delivery needs tighter execution evidence.

    line 12
  2. Distinctive:

    Remove harness complexity only through an isolated, one-component experiment:

    line 16
  3. Distinctive:

    Never strip sandboxing, budgets, stop conditions, human gates, root verification, or protected-data boundaries.

    line 24
  4. Distinctive:

    stop conditions and budget.

    line 36
  5. Distinctive:

    Before a new sprint, re-run the latest accepted result through its real runtime path:

    line 43
  6. Distinctive:

    Keep the contract opt-in and bounded.

    line 66
  7. Distinctive:

    Do not auto-revert, auto-commit, auto-merge, deploy, release, or publish.

    line 67
  8. Distinctive:

    Do not use source inspection as runtime-path evidence.

    line 68
  9. Distinctive:

    Prefer a one-shot checklist when the work is not genuinely repeated.

    line 70

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Loop Hardening Contract

Use this contract after loop-readiness-review when repeated delivery needs tighter execution evidence. It is a review and packet format, not a runner or scheduler.

1. Harness Stripping

Remove harness complexity only through an isolated, one-component experiment:

  1. Record the component, its intended failure mode, and its operating cost.
  2. Capture a representative baseline for completion, verification, cost, and failures.
  3. Disable exactly one non-safety component between runs, never during a live run.
  4. Repeat the same bounded case and classify REMOVE, RESTORE, HARMFUL, or INCONCLUSIVE.
  5. Retain the evidence and human settlement decision.

Never strip sandboxing, budgets, stop conditions, human gates, root verification, or protected-data boundaries.

2. Runtime-Path Sprint Packet

Freeze this packet before execution:

  • sprint_id and version;
  • one concrete deliverable;
  • 3–7 observable acceptance predicates;
  • the actual runtime path to exercise;
  • explicit out-of-scope items;
  • owned files or write boundary;
  • stop conditions and budget;
  • verification commands, device/browser steps, and expected evidence.

The executor cannot accept its own result. A mid-run requirement change creates a new packet version with a reason and explicit human confirmation.

3. Broken-Window Revalidation

Before a new sprint, re-run the latest accepted result through its real runtime path:

  • STILL_GREEN: the accepted predicates still pass;
  • REOPENED: a previously accepted predicate now fails;
  • INCONCLUSIVE: the required runtime or evidence is unavailable.

On REOPENED, stop new work, retain the evidence, reopen the result, and propose a bounded repair. Do not auto-revert. A revert may erase useful subsequent work or hide the causal change and therefore requires an inspected, human-approved settlement.

Compilation is not Cardputer device proof. Unit tests are not browser or production proof when the accepted claim names those runtime paths.

Output

Return:

  • sprint packet version;
  • revalidation status and evidence;
  • harness experiment and classification, if any;
  • root verification result;
  • settlement: accept, revise, repair, restore, or inconclusive;
  • remaining risks.

Guardrails

  • Keep the contract opt-in and bounded; do not install Loopkit or add scheduling.
  • Do not auto-revert, auto-commit, auto-merge, deploy, release, or publish.
  • Do not use source inspection as runtime-path evidence.
  • Root verifies integration; executor prose is evidence, not acceptance.
  • Prefer a one-shot checklist when the work is not genuinely repeated.

Pair With

  • loop-readiness-review for readiness level, state, isolation, and human gates.
  • bounded-evaluation for representative fixtures and comparison rubrics.
  • bug-evidence-protocol for captured red-to-green command evidence.
  • git-native-context-contract cpat records for recurring failure prevention.
  • bounded-orchestration-contract when execution is delegated across disjoint file scopes.

Provenance

Adapted from Archive228/loopkit sprint, broken-window, and harness-evolution ideas. The ABVX version removes the autonomous runner, transcript scraping, self-declared completion, automatic rollback, broad installation, and unpinned runtime dependencies.

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.