shape-concept

A workflow for turning an approved product intake into a short, agreed description of what the product should do. It keeps settled decisions, assumptions, evidence, exclusions, and open questions separate.

In plain words
What is it for?
Use it after product intake when the team needs canonical product-intent documents, or when an explicitly requested product change requires reshaping that intent.
Why use it?
It prevents implementation planning from quietly changing the product idea or mixing confirmed requirements with guesses.

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/dnlbox/ai-protocol/shape-concept
Any agent
npx skills add dnlbox/ai-protocol --skill shape-concept
Clone the repo
git clone --depth 1 https://github.com/dnlbox/ai-protocol

Made for: Claude Code, Codex.

Per session 17 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 547 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found 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 $0.00017 $0.00547
Opus 5 $0.00009 $0.00273
Sonnet 5 $0.00003 $0.00109
Haiku 4.5 $0.00002 $0.00055

Measured 2d ago against content hash 91e808361f42, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

shape-concept 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.

.agents/skills/shape-concept/SKILL.md · 76 lines

How it starts

The opening of the file, as written. The whole thing — 76 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Shape concept

Use this skill after concept intake or when accepted product intent must change deliberately.

Start

  • Read docs/intake/concept-intake.md.
  • Read every existing file in docs/concept/.
  • Require intake status Ready for concept shaping for initial shaping.
  • Require an explicit product-change request for re-shaping.
  • Treat placeholder files as scaffolding.
  • Treat accepted concept files as product authority.
  • Treat intake as historical input during re-shaping.
  • Return to concept intake when the intake is materially incomplete.

Document shape

  • Choose the smallest useful concept document set.
  • Give each concept file one ownership boundary.
  • Keep docs/concept/README.md as the concept index.
  • Use status Shaping during initial drafting.
  • Use status Re-shaping during deliberate revision.
  • Avoid a fixed document taxonomy.
  • Avoid one file per minor topic.
  • Avoid duplicating a decision across files.

Drafting

  • Synthesise the intake by meaning.
  • Separate settled intent from assumptions.
  • Separate evidence-backed findings from product judgements.
  • Separate explicit exclusions from open questions.
  • Cite factual claims with links or local sources.
  • Mark unsupported product judgements as hypotheses.
  • Preserve compatible human additions.
  • Preserve unresolved non-blocking questions.
  • Keep each discussion round focused on one tension.
  • Ask for one material product decision at a time.
  • Conduct research only when the user requests it.
  • Fold approved research into the relevant concept file.
  • Avoid detached research dumps.

Boundaries

  • Write only within docs/concept/.
  • Update intake status only when the stage changes.
  • Avoid choosing frameworks during concept shaping.
  • Avoid choosing schemas during concept shaping.
  • Avoid choosing deployment topology during concept shaping.
  • Avoid turning open product questions into implementation discretion.
  • Stop before implementation planning.
  • Stop before product implementation.

Read the full file on GitHub · 76 lines

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 · 76 lines · 17 tokens per session scan A 91e808361f42

Subscribe to this mod's changes

shape-concept is a skill published in the GitHub repository dnlbox/ai-protocol (18 stars, last pushed 23d ago), licensed MIT. It adds 17 tokens to every session and 547 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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