implement-specs

A workflow for implementing requirements written in checked-in specification files, issue plans, or acceptance criteria. It guides the work from reading and planning through implementation and verification.

In plain words
What is it for?
It helps locate and read specs, inspect existing code and tests, plan the work, implement the smallest needed change, run focused checks, and compare the result with the requirements.
Why use it?
It keeps code changes tied to agreed requirements and exposes gaps or conflicts before they become architectural problems.

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/pymodel/pythinker-cli/implement-specs
Any agent
npx skills add PyModel/pythinker-cli --skill implement-specs
Clone the repo
git clone --depth 1 https://github.com/PyModel/pythinker-cli

Made for: Claude Code, Codex.

Per session 20 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 207 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 $0.00020 $0.00207
Opus 5 $0.00010 $0.00103
Sonnet 5 $0.00004 $0.00041
Haiku 4.5 $0.00002 $0.00021

Measured yesterday against content hash fba2c9f0284d, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

implement-specs 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 yesterday.

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.

src/pythinker_code/skills/implement-specs/SKILL.md · 36 lines

What it actually says

Implement Specs

Use when the task is to implement existing spec files, issue plans, or acceptance criteria.

Workflow

  1. Locate and read the requested spec files.
  2. Extract required behavior, constraints, non-goals, and verification gates.
  3. Scout the existing code and tests before editing.
  4. Produce a concise plan tied to spec requirements.
  5. Implement the smallest viable change.
  6. Run focused tests or checks.
  7. Compare final behavior against the spec.

Rules

  • Keep changes tied directly to spec requirements.
  • Ask or stop if specs conflict or are underspecified in a way that changes architecture.
  • Preserve public compatibility unless the spec explicitly changes it.
  • Add tests when behavior changes and a matching test layer exists.

Output

SUMMARY
IMPLEMENTED REQUIREMENTS
CHANGES
VERIFICATION
SPEC GAPS OR FOLLOW-UPS
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. yesterday First seen · 36 lines · 20 tokens per session scan A fba2c9f0284d

Subscribe to this mod's changes

implement-specs is a skill published in the GitHub repository PyModel/pythinker-cli (20 stars, last pushed 4d ago), licensed Apache-2.0. It adds 20 tokens to every session and 207 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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