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 skills add ljucask/pureinn-product-development --skill pm-tech-feasibilitygit clone --depth 1 https://github.com/ljucask/pureinn-product-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/ljucask/pureinn-product-development/pm-tech-feasibility)<a href="https://agentmods.dev/skills/ljucask/pureinn-product-development/pm-tech-feasibility"><img src="https://agentmods.dev/badge/skills/ljucask/pureinn-product-development/pm-tech-feasibility/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/ljucask/pureinn-product-development/pm-tech-feasibility"><img src="https://agentmods.dev/badge/skills/ljucask/pureinn-product-development/pm-tech-feasibility.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 | $0.00042 | $0.03404 |
| Opus 5 | $0.00021 | $0.01702 |
| Sonnet 5 | $0.00008 | $0.00681 |
| Haiku 4.5 | $0.00004 | $0.00340 |
Grade A, and why
pm-tech-feasibility 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 10d 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 — 357 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PM - Tech Feasibility Report
Agent mode (--agent)
Supports --agent: runs autonomously in a subagent, drafts the artifact from existing inputs, and returns a short summary + coverage note.
- No flag → interactive (default); if inputs are heavy, offer agent mode.
--agent→ obey. First check inputs are complete. Anything missing: do NOT invent it - mark[ASSUMED - what/why]in the output and summary. Never hallucinate to fill a gap.
What this skill does
Takes raw research input (Perplexity deep research output, ChatGPT analysis, Tech Lead notes, domain knowledge) and produces a structured Tech Feasibility Report.
This is a "bring your data" skill - Claude cleans, structures, and formalizes the input. No AI hallucination of technical facts.
Dependencies
Recommended before running:
pm-project-charter- tech constraints, non-negotiables, and budget cap are defined there
Produces artifacts used by:
pm-problem-validation- tech feasibility is Track A inputpm-domain-model- tech stack context informs domain modelingcommon-ground- tech feasibility report is a key input for tech stack finalization
Step 0: Current state check
Check for existing artifacts:
- Tech Feasibility Report
Also check: does a Project Charter exist? Cross-reference tech constraints and budget cap stated there.
Look for: sections marked TBD, open questions not yet resolved, stack recommendation that conflicts with known constraints, AI cost modeling missing if product has AI components.
Interaction: Group related questions (2-4 per round) and confirm before moving on. For any A/B/C/D choice, use the AskUserQuestion tool with one option marked (Recommended) - never print options as plain text. Keep open-ended questions free-text (don't fake options). If the user is unsure, propose 3-4 concrete options plus "Other". Surface an assumption the moment you make one; never fabricate to fill a gap. (Full standard: CLAUDE.md.)
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.
- 10d ago First seen · 357 lines · 42 tokens per session scan A d50436d06473
pm-tech-feasibility is a skill published in the GitHub repository ljucask/pureinn-product-development (2 stars, last pushed yesterday), licensed MIT. It adds 42 tokens to every session and 3,404 once invoked, about $0.0002 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 2026-08-31.
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