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.
npx agentmods add skills/pymodel/pythinker-cli/spec-driven-implementationnpx skills add PyModel/pythinker-cli --skill spec-driven-implementationgit clone --depth 1 https://github.com/PyModel/pythinker-cliWhat 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 | $0.00025 | $0.00233 |
| Opus 5 | $0.00013 | $0.00117 |
| Sonnet 5 | $0.00005 | $0.00047 |
| Haiku 4.5 | $0.00003 | $0.00023 |
Grade A, and why
spec-driven-implementation 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.
What it actually says
Spec-Driven Implementation
Use when the task references a product spec, tech spec, design doc, issue plan, or explicit acceptance criteria.
Workflow
- Read the relevant spec files or acceptance criteria.
- Extract required behavior, non-goals, constraints, and verification gates.
- Map the existing implementation and tests before editing.
- Produce a short implementation plan tied to the spec.
- Make surgical changes only for the requested behavior.
- Check the implementation against each requirement.
- Run the smallest meaningful verification gate and report any skipped gate.
Rules
- Do not implement unstated nice-to-haves.
- If the spec conflicts with existing code or another spec, stop and surface the conflict.
- Preserve public compatibility unless the spec explicitly changes it.
- Add or update tests when behavior changes and the project has relevant tests.
Output
Return:
SUMMARY
SPEC REQUIREMENTS
CHANGES
SPEC CHECK
VERIFICATION
RISKS
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.
- yesterday First seen · 39 lines · 25 tokens per session scan A 33cb2ba6c056
spec-driven-implementation is a skill published in the GitHub repository PyModel/pythinker-cli (20 stars, last pushed 4d ago), licensed Apache-2.0. It adds 25 tokens to every session and 233 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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