Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/magnus919/hermes-profilesnpx agentmods add skills/magnus919/hermes-profiles/product-methodologyWrote 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/magnus919/hermes-profiles/product-methodology)<a href="https://agentmods.dev/skills/magnus919/hermes-profiles/product-methodology"><img src="https://agentmods.dev/badge/skills/magnus919/hermes-profiles/product-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/magnus919/hermes-profiles/product-methodology"><img src="https://agentmods.dev/badge/skills/magnus919/hermes-profiles/product-methodology.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00052 | $0.01332 |
| Opus 5.5 | $0.00021 | $0.00533 |
| Sonnet 5.5 | $0.00010 | $0.00266 |
| Haiku 4.5 | $0.00005 | $0.00133 |
Grade A, and why
product-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 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 — 137 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Product Management Methodology
Canonical Output
The canonical proof of work for every engagement is an artifact pyramid — a three-layer progressively-disclosable structure at an absolute filesystem path. Every methodology and framework in this skill feeds into one of the three layers.
Respond to any caller with the absolute path to 00-index.md at the pyramid root. Not a summary. Not a handoff paragraph. A path.
The layer mapping is at references/artifact-pyramid-mapping.md.
Production Flow
Build the pyramid in this order. Each phase gates the next.
Phase 1: Scaffold
mkdir -p <pyramid-root>/{01-summary,02-analysis,03-dossiers}
Copy assets/pyramid-template.md to <pyramid-root>/00-index.md and fill in the overview.
Phase 2: Build L3 Dossiers
For each source (interview, competitive analysis, market data):
- Create a flat file directly in
03-dossiers/— no subdirectories, no README files acting as folder indexes. A dossier is a single markdown file. If a topic has multiple sources, create multiple flat files (e.g.,customer-interviews.md,competitive-analysis.md). - Include source attribution metadata at the top:
**Source:** URL or transcript identifier **Captured:** YYYY-MM-DD **Author:** Person or organization **Title:** Original document title - Extract faithfully — no cherry-picking. Include counter-evidence.
- Add a NOTES section with any methodology context (how the data was collected, processed, etc.)
Gate C check: Every dossier has source metadata. Extracts are faithful. Methodology is documented.
Phase 3: Build L2 Analysis Files
From dossiers, compose one file per analysis dimension (problem, stories, scope, risk, etc.):
-
Each file is self-contained — makes sense read alone
-
Each file has a clear thesis, evidence, and conclusion
-
Each file ends with a
SOURCESsection:SOURCES (LAYER 3 NAVIGATION) ../03-dossiers/customer-interviews/transcript-001.md -> Primary user pain evidence supporting Section 2 ../03-dossiers/competitive-analysis/feature-matrix.md -> Competitor feature comparison referenced in Scope section
What ships with it
8 files 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.
- references/artifact-pyramid-mapping.md 7.1 KB
- references/customer-interview-guide.md 4.8 KB
- references/decision-log.md 3.4 KB
- references/moscow-prioritization.md 3.7 KB
- references/opportunity-solution-trees.md 3.8 KB
- references/rice-framework.md 5.0 KB
- references/spec-template.md 5.0 KB
- references/stakeholder-communication.md 5.9 KB
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 · 137 lines · 52 tokens per session scan A 42be87729784
product-methodology is a skill published in the GitHub repository magnus919/hermes-profiles (342 stars, last pushed 4mo ago), licensed MIT. It adds 52 tokens to every session and 1,332 once invoked, about $0.0002 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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