Borrowing it
Nothing to install: this file belongs to pavel-molyanov/molyanov-ai-dev. 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/pavel-molyanov/molyanov-ai-dev/main/.agents/skills/methodology/SKILL.mdgit clone --depth 1 https://github.com/pavel-molyanov/molyanov-ai-devWrote 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/pavel-molyanov/molyanov-ai-dev/methodology)<a href="https://agentmods.dev/skills/pavel-molyanov/molyanov-ai-dev/methodology"><img src="https://agentmods.dev/badge/skills/pavel-molyanov/molyanov-ai-dev/methodology/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/pavel-molyanov/molyanov-ai-dev/methodology"><img src="https://agentmods.dev/badge/skills/pavel-molyanov/molyanov-ai-dev/methodology.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Rogue Agent · line 217 Skill establishes unauthorized persistence across sessions via cron jobs, startup scripts, or state files. Session persistence allows an attacker to maintain access beyond the current interaction.Fix: Remove any persistence mechanisms (cron jobs, startup scripts, state files). Skills should not maintain state across sessions without explicit user consent.
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 | $0.00073 | $0.02918 |
| Opus 5.5 | $0.00029 | $0.01167 |
| Sonnet 5.5 | $0.00015 | $0.00584 |
| Haiku 4.5 | $0.00007 | $0.00292 |
Grade A, and why
methodology 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 6d 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 — 274 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI-First Development Methodology
Purpose
The methodology keeps project and feature work understandable across sessions while making the process proportional to the task. Durable project facts live in Project Knowledge, an approved user-spec is the contract for a planned feature, execution skills own their domain workflows, and fresh reviewer agents diagnose completed work without taking decisions away from the orchestrator or the user.
Operating Model
Requests route directly to skills by intent. The global commands/ source is currently empty;
feature planning, direct execution, initialization, documentation, and finalization do not depend
on command wrapper files. Request the workflow in plain language; historical shorthand such as
/new-user-spec or /done does not imply that an installed slash-command wrapper exists.
Choose the smallest path that fits the work:
| Need | Path |
|---|---|
| Small, well-defined change | Invoke the matching execution skill directly |
| Feature whose behavior or approach needs agreement | user-spec-planning → approval → execution → finalization |
| New repository | project-initialization → initial Project Knowledge → feature or ad-hoc work |
| Documentation-only work | documentation-writing with the evidence boundary named by the request |
| Review or audit only | Use the matching review skill or reviewer without modifying the artifact |
One request may activate several skills. For example, a UI feature with state changes uses both
layout-writing and code-writing; their verification and reviewers are coordinated in one
execution rather than treated as unrelated pipelines.
Planned Feature Lifecycle
user-spec-planning → explicit approval → new task: implement the approved spec
→ verified implementation commit → documentation-writing feature finalization
Plan the Feature
user-spec-planning owns the complete planning contract:
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.
- 6d ago Changed · -28 lines · -16 tokens per session 178426ecce7f
- 1mo ago First seen · 302 lines · 89 tokens per session scan A 6943e9310b03
methodology is a skill published in the GitHub repository pavel-molyanov/molyanov-ai-dev (300 stars, last pushed 6d ago), licensed MIT. It adds 73 tokens to every session and 2,918 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 2026-08-30.
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