Fable as a skill thread - lets gather our knowledge together and refine
I published a small open-source repo for a workflow I’ve been using to coordinate coding agents on larger codebases:
https://github.com/sherlockholmesyes/fable-agent-orchestration
The basic idea is simple:
Don’t hand-code every change yourself, but also don’t let agents free-run and trust their summaries.
Instead, act as the conductor:
- Split the work into narrow slices.
- Launch build agents in isolated git worktrees.
- Require each agent to open a PR, not merge it.
- Validate each PR with two separate critics:
- - one checks whether the test/gate actually proves the task;
- - one reviews the code/change itself adversarially.
- Verify reviewer claims against the real diff, current code, and CI.
- Merge one PR at a time.
- Relaunch the next slice while other work is still running.
The repo includes a clean skill database under Apache-2.0:
| Skill | When to use | Why it matters |
|---|---|---|
| fable-orchestrator | Running many PRs with several agents | Keeps parallel work coordinated and merge-safe |
| autonomous-finish-loop | When reversible work remains | Prevents stopping on plans, promises, or tool noise |
| think-work-try | One risky implementation slice | Forces investigate -> build -> prove |
| one-slice-worker-cycle | Giving one agent a narrow task | Prevents vague broad PRs |
| two-critic-review-loop | Reviewing non-trivial PRs | Splits test review from code review |
| agent-pr-validator | Checking an agent-made PR | Compares claims to real diff and CI |
| adversarial-reviewer | Before trusting a change | Finds the strongest real objection |
| task-relative-test-gate | Verifying tests themselves | Stops fake-green tests |
| review-verifier | After a reviewer gives a verdict | Catches stale or wrong review feedback |
| orphaned-wip-adopter | Salvaging abandoned agent work | Reuses good WIP instead of rebuilding |
| agent-dispatch-packet | Delegating work to an agent | Turns vague goals into scoped, testable packets |
| peer-review-packet | Asking another model/person | Sends only clean, relevant context |
| fable-session-skill-miner | Mining agent sessions for reusable skills | Extracts procedures without leaking raw logs |
| external-workflow-adapter | Importing outside workflows | Keeps useful ideas, rejects bad assumptions |
| instruction-drift-control | Keeping agent instructions and fix logs in sync | Prevents stale duplicated guidance |
| investigate-before-fix | Before fixing a suspected root cause | Prevents patches for unproven diagnoses |
| long-run-continuity | Long multi-PR runs or context resets | Preserves queue, PRs, and residuals across breaks |
| easy-vs-right-check | When a step feels like progress | Catches convenient work that dodges the real task |
| periodic-retrospect | When stalled or after repeated cycles | Finds dropped threads and recurring failure patterns |
| seal-both-types | Designing typed contracts | Prevents forged valid-by-construction states |
The main lesson:
The bottleneck is not only “make the generator smarter.”
For large agent-driven work, the bigger win is often to strengthen the verifier:
claim-to-diff validation, fail-under-broken tests, independent review, and serialized merge discipline.
I also included a machine-readable `catalog.json` and schema so the skill set can grow into a more organized agent-orchestration library.
I also try to make a community around open source AI where I'd like to share and discuss more , big ambitious projects and PoC feel free to join.
https://element.wearein.space/
think-work-try
credits : https://github.com/anmoln7/agent-standard-oss/ skill: instruction-drift-control
сredits : https://github.com/rennf93/opus-fable-playbook skill: behavior-contract-harness
credits: https://github.com/bjgreenberg/senior-engineering-partner phase-aware-engineering-ladder