spec

A planning workflow that turns a rough idea, feature request, or problem into a structured implementation specification. It can also identify bug reports and send them to a separate debugging workflow.

In plain words
What is it for?
Use it to plan software features, research projects, design work, or business tasks before implementation, including researching comparable approaches and checking the local project.
Why use it?
It prevents building from an unclear request by organizing the problem, scope, decisions, files, tests, and unanswered questions first.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/griffinwork40/agent-framework/spec
Any agent
npx skills add griffinwork40/agent-framework --skill spec
Clone the repo
git clone --depth 1 https://github.com/griffinwork40/agent-framework

Made for: Claude Code, Codex.

Per session 41 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 472 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin 100% copy Near-identical to another mod in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00041 $0.00472
Opus 5 $0.00020 $0.00236
Sonnet 5 $0.00008 $0.00094
Haiku 4.5 $0.00004 $0.00047

Measured 2d ago against content hash 167e7cbb84de, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

spec scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

This is a copy

100% identical to spec — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

skills/spec/SKILL.md · 31 lines

What it actually says

Triage: bugs route to /diagnose

Before speccing, detect bug-shaped inputs: crashes, error stacks, regression reports ("worked yesterday", "used to work"), platform-specific failures ("broken on X", "doesn't work when…"), or user-report framing that implies root-cause-first (not design-first). If detected, stop and redirect: "This is a debugging task, not a spec task. Route to /diagnose instead — it will isolate the root cause, then /spec can scope the fix." Do not emit a spec.


Dispatch two sub-agents in parallel. One researches the web for prior art, comparable approaches, and patterns relevant to $ARGUMENT. The other inspects the local working directory for conventions, existing artifacts, and integration points relevant to the domain. When both return, synthesize a concise spec using the domain-appropriate schema below. Present to the user for confirmation before proceeding.

Output schema by domain:

Domain Spec fields
software problem, goals, non-goals, approach, key decisions, interface, file plan, test plan, open questions
research problem, hypothesis, methodology, prior art positioning, expected results, publication plan, open questions
design problem, user needs, constraints, solution space, prototype plan, success metrics, open questions
business opportunity, risk analysis, competitive landscape, go/no-go criteria, resource plan, open questions
(other) problem, goals, non-goals, approach, key decisions, deliverables, validation plan, open questions

When domain is unspecified, infer from $ARGUMENT and the working directory. If ambiguous, use the generic (other) schema.

Epistemic confidence

Include an Epistemic confidence section at the end of every spec: summarize coverage gaps from research, flag claims that will be hard to verify, and note where human judgment will be needed.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2d ago First seen · 31 lines · 41 tokens per session scan A 167e7cbb84de

Subscribe to this mod's changes

spec is a skill published in the GitHub repository griffinwork40/agent-framework (23 stars, last pushed 7d ago), licensed Apache-2.0. It adds 41 tokens to every session and 472 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to spec, differing in 0 lines, and is treated as a copy.

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