Borrowing it
Nothing to install: this file belongs to w00fx/spec-anchored-agentic-development. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/w00fx/spec-anchored-agentic-development/main/.agents/skills/shape/SKILL.mdgit clone --depth 1 https://github.com/w00fx/spec-anchored-agentic-developmentWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/w00fx/spec-anchored-agentic-development/shape)<a href="https://agentmods.dev/skills/w00fx/spec-anchored-agentic-development/shape"><img src="https://agentmods.dev/badge/skills/w00fx/spec-anchored-agentic-development/shape/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/w00fx/spec-anchored-agentic-development/shape"><img src="https://agentmods.dev/badge/skills/w00fx/spec-anchored-agentic-development/shape.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00083 | $0.02360 |
| Opus 5.5 | $0.00033 | $0.00944 |
| Sonnet 5.5 | $0.00017 | $0.00472 |
| Haiku 4.5 | $0.00008 | $0.00236 |
Grade A, and why
shape 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 1mo 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.
How it starts
The opening of the file, as written. The whole thing — 195 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Identify the target from the argument and pick the mode:
- An idea or ongoing discussion → interview toward a capability
spec (the
to-specskill writes it). - A transcript (meeting, chat, voice note) → extract what was decided, interrogate the gaps.
- Existing code with no spec (brownfield) → archaeology: read the code, establish what it does, interrogate what it should do.
- An existing spec → grill-back: interrogate the document for
ambiguity and holes (the
to-specskill applies the refinements). - A task / issue → sharpen it until an agent could implement it without guessing.
All modes are one machine: interview relentlessly until the
frontier is empty and we reach a shared understanding — every branch
of the design tree visited, nothing left silently assumed; that is the
termination condition, not a checklist filled. Never write implementation code — and never write or
edit the spec file: that is the to-spec skill's job.
Interview mechanics
- Map the interview as a design tree and work it in rounds. Every decision branches into the decisions that hang off it. The frontier is every question whose prerequisites are already settled — askable now without guessing at answers you haven't heard. Ask the whole frontier in one round, numbered; then wait. A question whose answer depends on another question still open in this round belongs to a later round. Each round of answers reshapes the tree — settled decisions push the frontier outward; recompute and ask the next round.
- For each question, provide your recommended answer. The human confirms or corrects; composing from scratch is friction. The recommendation is a proposal — the human's answer is the truth.
- If a question can be answered by exploring the codebase, explore the codebase instead — dispatch the exploration and don't block the frontier on it: a running exploration is an unsettled prerequisite, so only the questions downstream of it wait; ask the rest of the round now. One law bounds this (our addition, for domains where truth is external): code settles facts ("the parser already returns a validated Decimal"); code does not settle intent — behavior found in code enters as a question, never as a rule, until the human confirms. Code can contain bugs that became structural.
- Numbers before prose. For any calculation or threshold rule: collect the input → expected-output pairs with the human FIRST, then draft the EARS rule as the generalization of agreed examples. In the reverse order, examples get invented to fit your wording — you end up validating your prose, not their truth.
- Stay inside one capability. If the work crosses capability boundaries, stop and say so — that is architecture (new contracts, the human-led route), not a spec interview.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 1mo ago First seen · 195 lines · 83 tokens per session scan A c9364c009942
shape is a skill published in the GitHub repository w00fx/spec-anchored-agentic-development (7 stars, last pushed 1mo ago), licensed MIT. It adds 83 tokens to every session and 2,360 once invoked, about $0.0003 per session on Opus 5.5. 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 2026-09-05.
Other skills, from other repositories
code-simplifier
Always-active simplicity rules: no speculative abstractions, one job per function, max 30 lines, flat over nested with early returns, descriptive names, comments explain why.
critical-review
4-phase adversarial review protocol: pre-flight analysis, adversarial execution check, self-correction loop, and [VALIDATION] report.
agent-delegation
Rules for when and how to spawn sub-agents: Explore for 3+ file reads, parallel agents for independent tasks, summaries only — raw output never enters main context.
memory-first
Lookup chain enforced before any file read or search: project and global memory, grep/glob, targeted read — stop at the first step that answers.
verbosity
Response verbosity protocol: reads the level from /.claude/memory/verbosity.md and applies the MIN, INFO or VERBOSE output rules to every reply.
project-bootstrap
A workflow for adapting the smyslokod-starter project template to a specific product idea. It chooses a depth of setup and uses the template's business and project files as starting context.