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Claude Code
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Codex CLI
Muse
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OpenClaw
Manus
Grok
Grok Bot+18 agentsFable Mode Pro
Staged execution for Claude: plan, prove every stage with a check that can fail, and get a VERIFY.md audit trail.
$5
Fable Mode Pro
Example session with this skill installed
Use fable-mode: build a validator for my skills repo that checks every SKILL.md has valid YAML frontmatter with name and description, and that each skill's folder name matches its name field. Include tests.
- Read your context and instructions
- Compiled the fable mode pro
Summary
Built scripts/validate_skills.py + 33 tests for a 7-skill repo. All checks pass. The double-check panel then found 4 real bugs the first version shipped with, every one invisible to "looks right" review. All fixed and re-verified cold.
Stage map
| Stage | Output | Failable check |
|---|---|---|
| 1 Inventory | Every SKILL.md + install.sh mapping | find lists 7 files; root skill installs as fable-mode |
| 2 Spec | Name/description rules | Fetched agentskills.io/specification and read it, not recalled |
| 3 Tests first | 23 tests | Run before validator exists → fails (red) |
| 4 Validator | validate_skills.py | 23/23 pass; real repo: 7 checked, 0 failed |
| 5 Gate | double-check panel | Fresh Haiku + Sonnet checkers, claims only, no reasoning |
Delegation: stages 1–2 independent but each one quick read, so run inline. Spawning would cost more than it saves.
Spec rules enforced
name: required, 1–64 chars,a-z 0-9 -, no leading/trailing hyphen, no--, must match folderdescription: required, 1–1024 chars, non-emptycompatibility: 1–500 chars if present- Warnings: unknown fields, body over 500 lines
Real repo result
PASS SKILL.md (name: fable-mode)
PASS double-check/SKILL.md (name: double-check)
PASS execution-guardrails/SKILL.md (name: execution-guardrails)
PASS fable-fable/SKILL.md (name: fable-fable)
PASS fable-haiku/SKILL.md (name: fable-haiku)
PASS fable-opus/SKILL.md (name: fable-opus)
PASS fable-sonnet/SKILL.md (name: fable-sonnet)
7 skill(s) checked, 0 failed
Headroom check: fable-fable description is 970/1024 chars, 54 from failing.
What the cold check caught
The 23-test version passed everything. Then the Sonnet checker probed edge cases:
| Bug | Input | Before | After |
|---|---|---|---|
| BOM rejected | file starts EF BB BF | false FAIL | PASS |
| Crash | invalid UTF-8 bytes | traceback, run aborts | FAIL: "not valid UTF-8 (byte 37)" |
| Silent truncation | indented --- in block-scalar description | description cut to "hi", no error | full description parsed |
| Duplicate keys | two name: lines | last one silently wins | FAIL: "duplicate key 'name'" |
One test of mine also passed for the wrong reason: it asserted "no errors", and the truncation bug produces no error. Tightened to check the parsed text, confirmed red, then fixed.
Plus 6 tests for error paths nobody had tested (non-string name, trailing hyphen, non-mapping and empty frontmatter, whitespace description, empty/non-string compatibility).
Ran 33 tests in 0.032s
OK
Verification
Double-check: 5 claims + 7 requirements.
FIXED after FAIL: 4 bugs + 6 untested error paths. Re-check round: 6/6 confirmed.
Kept by choice: ASCII-only names, documented in the script.
Checkers: 1× Haiku, 1× Sonnet, 1 Haiku re-check
Next steps
- Run
python3 scripts/validate_skills.pyin CI or a pre-commit hook. - Trim
fable-fable's description before it crosses 1024.
Connects securely to your tools. The creator never sees your data.
About this skill
Complex, multi-file work fails when a model rushes to an answer and calls it done because it "looks right." Fable Mode Pro makes Claude plan the work in stages, prove each stage with a check that can actually fail, and hand you a written record of every check it ran.
What Pro adds
- Its own command (fable-mode-pro):
/fable-mode-proruns the full loop with everything below always on. Regular/fable-modestays in the bundle and stays regular, so you choose per task. - VERIFY.md audit trail (fable-audit): every run logs its stage map, each check's exact command and output, what failed and how it was fixed, and what's still open. Logged as the work happens, never written up afterward. Hand it to a reviewer, a client, or your future self.
- Ready-made checks (fable-checks): recipes for code (pytest, Jest, Vitest, Go, red-then-green, type checks, diff scope), data (nulls, duplicate keys, join fan-out, reconciliation), research (claim tracing, attribution, freshness), documents (docx, pdf, xlsx totals, pptx) and web (render, console errors, links, forms). Each one guards against a "0 failures" result from a run that checked nothing.
- SKILL.md validator: checks every skill in a folder against the Agent Skills spec. 33 tests.
The core loop (included)
- Stage map before any work: numbered stages with expected outputs.
- Parallel delegation to subagents where the runtime supports it.
- Failable verification at every stage: a test that runs, a source actually fetched, a file read back.
- Skeptical self-review, then the double-check gate: fresh checker agents verify the finished work against its claims.
- Tier runners (fable-opus, fable-sonnet) and always-on execution-guardrails.
How to use
- Install with
./install.sh(Claude Code), or upload each skill folder in the Claude app. - Say
/fable-mode-pro(or "fable mode pro") for the full Pro run. Say/fable-modefor the regular loop. - Pro runs hand you the deliverable plus
VERIFY.md.
Free vs Pro
The core loop is free on GitHub (mrtooher/fable-mode). Pro adds the fable-mode-pro command, fable-audit, fable-checks and the validator: the parts that turn "it says it checked" into a record you can read.
Known limitations
Doesn't raise the model's reasoning ceiling. Built and tested for Claude Code; parallel delegation and the checker panel need subagent support. At most two full replans per run, then it stops and asks you.
How to use
Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.
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Verified clean 9 days ago
- Passed all security checks, Safe to install
Requires
Node.js