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 lenar-amirov/product-pipeline-public --skill solution-scoringgit clone --depth 1 https://github.com/lenar-amirov/product-pipeline-publicWrote 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/lenar-amirov/product-pipeline-public/solution-scoring)<a href="https://agentmods.dev/skills/lenar-amirov/product-pipeline-public/solution-scoring"><img src="https://agentmods.dev/badge/skills/lenar-amirov/product-pipeline-public/solution-scoring/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/lenar-amirov/product-pipeline-public/solution-scoring"><img src="https://agentmods.dev/badge/skills/lenar-amirov/product-pipeline-public/solution-scoring.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 | $0.00064 | $0.00735 |
| Opus 5 | $0.00032 | $0.00367 |
| Sonnet 5 | $0.00013 | $0.00147 |
| Haiku 4.5 | $0.00006 | $0.00073 |
Grade A, and why
solution-scoring 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 12d 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 — 63 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Solution Scoring — assumption map + ICE + viability
The scoring engine of /solutions: turns "we could do A, B or C" into a
ranked, risk-aware bet. Input: ✅/🎯 problems from the registry
(hypotheses.py show <dir>), never from prose.
1. Assumption map (per solution)
List what must be TRUE for the solution to work; classify each:
| Assumption | Type | Risk | Cheapest validation |
|---|---|---|---|
| users will notice the entry point | desirability | high | concept test (step 8.5) |
| sellers can fulfil in 48h | feasibility | med | ops interview |
| unit economics survive the discount | viability | high | spreadsheet + finance review |
Rule: the riskiest assumption gets validated first and cheapest —
that's what experiment-design takes as input. A solution whose riskiest
assumption is untestable is a faith project; say so.
2. Scoring — ICE by default, SIF if the PM's profile says so
- Impact: expected movement of the initiative metric — anchor in registry numbers (segment sizes, measured effects), not vibes.
- Confidence — numeric, staged by validation depth (REAL-backed problems justify confidence in the PROBLEM, not the solution): 0.1–0.3 untested idea or external analogy · 0.4–0.6 after a concept test or a competitive analogue with the same mechanism on a comparable audience · 0.7+ only after an experiment on OUR users. A number outside its stage is the same violation as evidence-typing ranges.
- Ease: S/M/L from the dev lead when available, gut S/M/L otherwise (mark INFERRED).
Comparative table, top-1 recommendation with one-paragraph reasoning. Honesty rule: don't inflate Confidence without evidence — the anti-generic self-check applies.
3. Business viability (quick pass, before design)
Unit economics sketch · cannibalization risk · dependencies (teams/systems
→ candidates for status.json dependencies[]) · compliance flags · effort
S/M/L. Verdict per solution:
- RED — any single disqualifier: unit economics negative at target scale, hard compliance blocker, or effort L with confidence ≤0.3. One red parks the solution regardless of its ICE score (a great score on a non-viable solution is how teams burn quarters).
- YELLOW — proceed with a NAMED risk and an owner for it.
- GREEN — no known disqualifiers.
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.
- 12d ago First seen · 63 lines · 64 tokens per session scan A 9c83298bee42
solution-scoring is a skill published in the GitHub repository lenar-amirov/product-pipeline-public (12 stars, last pushed 22d ago), licensed MIT. It adds 64 tokens to every session and 735 once invoked, about $0.0003 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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