Agent skill

Codd Fable Consult

by yohey-w in yohey-w/codd-dev

Consult Claude Fable 5 (Anthropic's most capable model for deep, long-horizon reasoning — or whichever model currently holds that role) for a genuinely hard, novel CoDD architectural design…

MITAuto-check passedDevelopment

Install Codd Fable Consult

skills CLI
$ npx skills add yohey-w/codd-dev --skill codd-fable-consult -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install yohey-w/codd-dev codd-fable-consult --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/yohey-w/codd-dev.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/codd-fable-consult .claude/skills/codd-fable-consult && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
codd-fable-consult
GitHub stars
115
Token cost
~3.8k tokens
SKILL.md length
1,457 words
Files
1
Skills in repo
11
Repo updated
First seen
Licence
MIT

At a glance

Consult Claude Fable 5 (Anthropic's most capable model for deep, long-horizon reasoning — or whichever model currently holds that role) for a genuinely hard, novel CoDD architectural design…

  • Works in 5 steps: Read the disclosure, if any, before the… → Verify the design actually reads the… → Do not implement blind. Skim the design… → …
  • Tasks that involve Architecture decision records
  • SKILL.md covers When to Use, Why the Naive Version Fails…, Invocation and Prompt Template, plus 4 more sections
  • Calls claude and git; reaches github.com

What it does

Codd Fable Consult is an agent skill from yohey-w/codd-dev. Consult Claude Fable 5 (Anthropic's most capable model for deep, long-horizon reasoning — or whichever model currently holds that role) for a genuinely hard, novel CoDD architectural design decision, where a quick mechanical patch would be wrong. Packages CoDD's philosophy, invariants, GitHub repo, and session-derived insights into a rigorous consultation prompt; reviews the returned design before any implementation begins. Use AFTER a bug/gap has already been precisely diagnosed — this is a design skill, not a…

Its SKILL.md is about 3.8k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Development, covering Architecture decision records and Creative writing and fiction. It works with GitHub. The repository describes itself as: CoDD: Coherence-Driven Development — Claude Code plugin for cross-artifact change impact analysis. The licence is MIT.

When your agent uses it

  • Tasks that involve Architecture decision records
  • Tasks that involve Creative writing and fiction

Example prompts

  • “/codd-fable-consult”

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

  1. Read the disclosure, if any, before the design. Fable 5 (or any consultant) may state what it could and couldn't access, and what it…
  2. Verify the design actually reads the real, current code, especially if local uncommitted changes exist that the consultation couldn't see…
  3. Do not implement blind. Skim the design for: does it end in one recommended path (not a menu)? Does it give adversarial cases, not just…
  4. Route the follow-up implementation through the project's normal discipline — full test suite, a regression test per adversarial case the…
  5. Report back to the user with the TL;DR and the single recommended path before starting implementation, even when the user has…

What it can do on your machine

Read from SKILL.md and the folder at commit 41b5f87. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • claude
    • git

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • github.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Codd Fable Consult loads about 3.8k tokens when it runs. Until then it costs about 138 tokens; SKILL.md has 1,457 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~138
When it runs · the whole SKILL.md, loaded when a task matches
~3.8k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from yohey-w/codd-dev at commit 41b5f87, republished under its MIT licence (© yohey-w). 1,457 words, ~3,831 tokens.

Download SKILL.mdSave it as .claude/skills/codd-fable-consult/SKILL.md (or your agent's skills folder).
name
codd-fable-consult
description
Consult Claude Fable 5 (Anthropic's most capable model for deep, long-horizon reasoning — or whichever model currently holds that role) for a genuinely hard, novel CoDD architectural design decision, where a quick mechanical patch would be wrong. Packages CoDD's philosophy, invariants, GitHub repo, and session-derived insights into a rigorous consultation prompt; reviews the returned design before any implementation begins. Use AFTER a bug/gap has already been precisely diagnosed — this is a design skill, not a diagnostic one.

CoDD × Fable — Strategic Design Consultation

Route a genuinely hard CoDD design fork to Claude Fable 5 instead of guessing, drive-by patching, or letting a subagent design in a vacuum. This skill exists because a real consultation this project ran (the Java/C#/C++ marker-authenticity delegated-assertion gap, 2026-07-01/02) produced a design far better than an ad-hoc prompt would have — but only once several specific ingredients were deliberately injected. Skip any of them and you get generic, ungrounded, or overfit advice instead.

When to Use

Only after diagnosis is already done and the case genuinely meets all of these:

  • A real bug/gap has been root-caused with concrete evidence (ideally from a live dogfood run, not a hypothetical) — not "something might be wrong," but "here is exactly what fails and why."
  • A quick mechanical patch would be wrong, not just tedious. Signs: the fix would hardcode a framework/instance-specific special case into shared code; the fix could be done three different reasonable ways with real tradeoffs; the fix spans multiple language adapters and a per-language patch would duplicate audited logic; there's a live risk of solving today's instance while creating tomorrow's overfit.
  • The decision is squarely inside CoDD's own stated hard invariants (generality-first, anti-false-green/anti-false-red, owner-not-a-bottleneck) such that getting it wrong would violate one of them, not just be suboptimal.

Do NOT use this for:

  • A bug with one obviously correct fix — just fix it, and cite codd/coverage_execution_coherence.py-style commits (this project has many) as the bar for a well-scoped, well-tested mechanical patch.
  • Exploratory "what should we build next" prioritization — that's a Gunshi/owner conversation, not a design-fork consultation.
  • Anything where the honest answer is "we don't have enough live evidence yet" — go get a live failure first (via a normal investigation agent), then come back here.

Why the Naive Version Fails (what this skill fixes)

A first attempt at this consultation used the Agent tool's subagent routing with model: "fable" and failed instantly ("Claude Fable 5 is currently unavailable") — that routing path was not reliable even when Fable 5 access itself was confirmed working via direct CLI. Always invoke Fable 5 via direct CLI, not the Agent tool's model parameter, until/unless that routing is independently reconfirmed (see Invocation below).

A second, more subtle failure mode: even a well-intentioned prompt that just describes the problem in prose gets you a plausible-sounding but ungrounded or genericized answer. The prompt structure below exists because each ingredient closed a specific gap observed in practice:

Missing ingredientWhat goes wrong without it
The actual GitHub URLFable 5's own sandboxed session may have filesystem access denied by its execution environment's own permission profile (observed directly: Read/ls/grep/git fetch all auto-denied in one real run) — with no repo URL, it either refuses or speculates about code it never saw. Handing it https://github.com/yohey-w/codd-dev up front let it self-recover by reading the real, current origin/main via web-accessible tools instead.
CoDD's philosophy stated explicitly, not impliedWithout explicit "generality-first: no language == in shared code" and "anti-false-green: nothing may let an AI certify its own homework," a generically-smart model will still reach for the locally-obvious fix (a per-language special case, or weakening a gate) because that reads as "pragmatic" absent the stated constraint.
"The design must stay generic" as an explicit, named requirementOtherwise you get a correct-looking fix for the ONE framework/instance you showed it (e.g., "special-case ArchUnit's .check()") without the model ever being asked to defend that choice against the next framework that shows up.
A concrete adversarial-case demandWithout asking for adversarial examples the design must still reject, you get a design that solves the reported case but has no evidence it preserves the anti-false-green invariant against a lightly-different attack.
"End with ONE recommended path"Absent this, capable models default to an evenhanded survey of options — useful for exploration, useless as an implementation ticket.
A staged/live-verification rollout askWithout this, the returned plan tends to be "ship everything at once" — this project's own discipline (a live dogfood rerun per increment, not just pytest green) needs to be requested, not assumed.

Invocation

Direct CLI, not the Agent tool. Write the prompt to a scratchpad file first — a multi-paragraph prompt inlined into a shell command is an escaping nightmare — then invoke via nohup ... & disown so the consultation survives independently of whatever is currently killing long-running run_in_background: true Bash tasks in this environment (observed, unresolved root cause as of 2026-07-02 — resuming/re-launching reliably recovers regardless of cause, and a fully-detached process sidesteps the whole question).

bash
# 1. Write the prompt (see template below) to a file.
#    Use the Write tool for this, not a heredoc in the same command.

# 2. Launch detached, capturing stdout/stderr separately:
nohup claude --print --model claude-fable-5 --effort max "$(cat /path/to/prompt.md)" \
  > /path/to/design_output.md \
  2> /path/to/design_output.stderr &
disown
echo "launched pid=$!"

# 3. There is no automatic completion notification for a nohup'd process —
#    you must poll. Check both that the process has actually exited AND that
#    the output file has real content before treating it as done:
ps aux | grep "claude.*fable-5" | grep -v grep   # empty = finished (or never started — check stderr too)
wc -l /path/to/design_output.md

If a quick sanity check is warranted first (confirming Fable 5 access itself, independent of the design question), a trivial low-cost probe works and returns fast: claude --print --model claude-fable-5 --effort low "reply with exactly the word: available".

Prompt Template

Fill in the bracketed sections. Keep every other section close to verbatim — they are the ingredients from the table above, not filler.

You are being consulted specifically because this is a genuinely hard, novel
architectural design problem in CoDD (a context-driven/coherence-driven
development tool) at [REPO: https://github.com/<owner>/codd-dev — grounded
reasoning only; if you cannot reach the local filesystem, read the actual
current code from this GitHub repo (origin/main) via whatever web-access
tools you have, and disclose which access path you actually used]. This is
NOT a "find and fix a bug" task — diagnosis is already done. This is a
"design the right generalizable solution" task.

## CoDD's non-negotiable invariants (violating any of these defeats the point of asking you)

- **Generality-first**: the language-free CORE never branches on
  `language == "python"` etc. Per-language behavior lives entirely in
  declarative YAML profiles (`codd/languages/profiles/*.yaml`) plus small,
  isolated adapter classes registered per language. Any fix you design MUST
  fit this shape — verify by grepping the actual code, don't assume.
- **Anti-false-green**: when an AI autonomously writes both source code and
  its own tests, nothing may let it certify its own homework. Gates exist to
  independently verify claims, never to trust self-report.
- **Anti-false-red**: a gate must not fail something that is actually fine —
  a standing false-red trains operators toward disabling the gate entirely,
  which is a worse outcome than the false-red itself.
- **Owner-is-not-a-bottleneck**: prefer designs where extending coverage
  (a new language, a new library, a new edge case) is a data/config edit
  reachable by anyone, not a core-code change requiring the maintainer.

## The specific gap (concrete, evidence-based — not hypothetical)

[DESCRIBE: the exact failure, with real file paths, function names, and
live evidence — what broke, on what real input, with what exact error
text. Cite the specific files/functions you've already read so Fable 5
grounds itself immediately rather than re-discovering them.]

[STATE: why a quick patch is wrong — what got tried or considered and
rejected already, and specifically what overfit/generality risk a naive
fix would create. If a prior investigation flagged this as a design
fork rather than a bug, quote that flag.]

## What I need from you

A concrete, opinionated, implementable design — not a survey of options with
no conclusion.

1. [The core mechanism question — e.g., "should this be N per-language
   implementations or a shared primitive with per-language plug-ins, and
   why, grounded in what's already in the code?"]
2. [Any depth/bound/stopping-rule question the design needs to answer
   honestly, not just practically.]
3. [The hardest sub-problem — usually "how do we handle the
   framework/instance-specific case generically without hardcoding it,"
   with the overfit risk stated explicitly and NOT to be dodged.]
4. **Anti-false-green integrity**: for anything you propose, give at least
   one adversarial example your design correctly REJECTS, alongside the
   real motivating example(s) it correctly ACCEPTS.
5. **Scope call**: should this ship as one unit, or is there a safe, smaller
   first increment that de-risks the rollout, gated by a LIVE rerun of the
   actual failing case (not just unit tests) before the next increment?
   Weigh in using CoDD's own stated invariants explicitly.

## Constraints

- Do not write or edit any code yet — sketch signatures/pseudocode if
  confident, but this is a design pass.
- Do not propose disabling or weakening the affected gate/check — that was
  already considered and rejected.
- Ground everything in the actual code. Read the real files; don't assume.

## Output

A design document, written directly in your response. Structure it however
best serves clarity, but it MUST end with a single clear recommended path
(not a menu with no pick) and an explicit, ordered implementation task list
for a follow-up task to execute against.
Show full SKILL.md (660 more words)Show less

After the Consultation Returns

  1. Read the disclosure, if any, before the design. Fable 5 (or any consultant) may state what it could and couldn't access, and what it grounded itself in. Treat an honest "I couldn't read your local files so I read GitHub instead" as a positive signal (it's telling you exactly how to weight its claims), not a defect. Treat suspiciously specific claims with NO stated grounding (exact internal variable names, oddly-precise numbers, citation-free specifics) as a reason for skepticism — cross-check anything load-bearing before trusting it, the way you would any other unverified research output.
  2. Verify the design actually reads the real, current code, especially if local uncommitted changes exist that the consultation couldn't see. A design grounded in a stale or public-only view of the repo may miss same-day local edits.
  3. Do not implement blind. Skim the design for: does it end in one recommended path (not a menu)? Does it give adversarial cases, not just happy-path cases? Does it avoid hardcoding an instance-specific special case into shared code? If any of these are missing, the consultation likely needs a follow-up round, not a rubber-stamp.
  4. Route the follow-up implementation through the project's normal discipline — full test suite, a regression test per adversarial case the design specified, no language == branches, staged/incremental landing gated by a live rerun per the design's own rollout plan, targeted git add (never -A).
  5. Report back to the user with the TL;DR and the single recommended path before starting implementation, even when the user has pre-authorized "implement what Fable 5 says" — a one-paragraph confirmation costs little and catches the case where the design, on reflection, doesn't actually fit.

Absolute Constraints

  1. Never skip the invariant-and-repo-URL preamble to save prompt length. Every ingredient in the table above was added because its absence produced a measurably worse result. This is not boilerplate to trim.
  2. Never accept a design that special-cases a specific framework/library/instance by name inside shared/dispatch code. If the hard sub-problem in your consultation is exactly this shape, the returned design must resolve it via declarative data (profile YAML) or an explicitly-argued generic mechanism — not a Python if name == "ArchUnit" (or equivalent) in core logic.
  3. Never implement a returned design without at least skimming it for the four checks in step 3 of "After the Consultation Returns."
  4. Never treat Fable 5's output as ground truth about its own execution environment or about anything it couldn't ground in actual code — its disclosed access limitations are real constraints on confidence, not modesty.

Troubleshooting

  • "Claude Fable 5 is currently unavailable" via the Agent tool — this is a known routing gap (subagent model: "fable" failed even when direct CLI access was confirmed working, 2026-07-02). Fall back to direct CLI invocation per the Invocation section; do not conclude Fable 5 itself is down without an independent direct-CLI probe.
  • The consultation process seems to vanish with no notification — expected for a nohup'd process; there is no task-notification. Poll the output file and ps directly.
  • The design references code that doesn't match your local tree — check whether local uncommitted changes exist that predate/postdate what the consultation could see (its disclosure should say what commit/branch it grounded on); re-verify the relevant sections before implementing rather than assuming staleness invalidates the whole design.

Why This Skill Exists

CoDD's hardest design questions are exactly the ones a fast, mechanical patch gets wrong — they're rare, but when they show up (a cross-language architectural gap, a genuine generality-vs-overfit tradeoff, a gate-design tradeoff touching the anti-false-green invariant), the cost of a bad call compounds across every future language/framework CoDD onboards. A single, well-grounded consultation with the right context injected is cheap insurance against that; an ungrounded or under-specified one is worse than not asking, because it produces false confidence. This skill is the checklist that keeps the consultation grounded, generic-by-construction, and honest about its own evidence — learned from the first real run of this pattern, not designed in the abstract.

© yohey-w, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/codd-fable-consult of yohey-w/codd-dev.

Open the folder on GitHubat commit 41b5f87

Compare with similar skills

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Works with

Questions about Codd Fable Consult

What does Codd Fable Consult do?

Consult Claude Fable 5 (Anthropic's most capable model for deep, long-horizon reasoning — or whichever model currently holds that role) for a genuinely hard, novel CoDD architectural design…. Codd Fable Consult is an agent skill from yohey-w/codd-dev. Consult Claude Fable 5 (Anthropic's most capable model for deep, long-horizon reasoning — or whichever model currently holds that role) for a genuinely hard, novel CoDD architectural design decision, where a quick mechanical patch would be wrong.

When should I use Codd Fable Consult?

Codd Fable Consult fits situations like: tasks that involve Architecture decision records; tasks that involve Creative writing and fiction.

How do I install Codd Fable Consult in Claude Code?

Run `npx skills add yohey-w/codd-dev --skill codd-fable-consult -a claude-code`. Or copy the skill folder (skills/codd-fable-consult in yohey-w/codd-dev) into .claude/skills/codd-fable-consult in your project. Claude Code loads it when a task matches its description.

How do I install Codd Fable Consult in Codex?

Run `npx skills add yohey-w/codd-dev --skill codd-fable-consult -a codex`. Or copy the skill folder (skills/codd-fable-consult in yohey-w/codd-dev) into .agents/skills/codd-fable-consult in your project. Codex loads it when a task matches its description.

Can I use Codd Fable Consult in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add yohey-w/codd-dev --skill codd-fable-consult -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/codd-fable-consult, .gemini/skills/codd-fable-consult, .github/skills/codd-fable-consult and .opencode/skills/codd-fable-consult in your project.

What does Codd Fable Consult need to run?

Going by SKILL.md and its folder, Codd Fable Consult needs the command-line tools its instructions call (claude and git).

Does Codd Fable Consult access the network?

SKILL.md names 1 domain. In commands or code: github.com; the agent is likely to contact it when it follows the instructions. This is read from the text; nothing was executed.

Is Codd Fable Consult safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Codd Fable Consult use?

Codd Fable Consult is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Codd Fable Consult use?

About 3.8k tokens (SKILL.md is roughly 15k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Codd Fable Consult?

Skills that share tags, products or a category with Codd Fable Consult: Design Doc Mermaid (SpillwaveSolutions/design-doc-mermaid, 176 stars), Human Writing (kylesnowschwartz/SimpleClaude, 114 stars), Review GitHub PR (NVIDIA/OpenShell, 16k stars) and Development Workflow (runceel/ReactiveProperty, 944 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Codd Fable Consult?

yohey-w (a GitHub user) maintains it in yohey-w/codd-dev, which has 115 GitHub stars. The repository holds 11 skills in this directory. The repository was last updated on September 14, 2026.

Source: yohey-w/codd-dev on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.