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 stanislavnianko/product-discovery-claude-skills --skill estimationgit clone --depth 1 https://github.com/stanislavnianko/product-discovery-claude-skillsWrote 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/stanislavnianko/product-discovery-claude-skills/estimation)<a href="https://agentmods.dev/skills/stanislavnianko/product-discovery-claude-skills/estimation"><img src="https://agentmods.dev/badge/skills/stanislavnianko/product-discovery-claude-skills/estimation/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/stanislavnianko/product-discovery-claude-skills/estimation"><img src="https://agentmods.dev/badge/skills/stanislavnianko/product-discovery-claude-skills/estimation.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 | $0.00069 | $0.01366 |
| Opus 5.5 | $0.00028 | $0.00546 |
| Sonnet 5.5 | $0.00014 | $0.00273 |
| Haiku 4.5 | $0.00007 | $0.00137 |
Grade A, and why
estimation 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 — 122 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Estimation
Part of the discovery-phase skill pack ·
scopinggroup · readsdiscovery-context.md(runprofile-builderfirst if missing).
Agency-style estimation: effort by discipline × phase, with low / expected / high ranges, and a list of assumptions that, if violated, invalidate the estimate. The output is the heart of any commercial proposal.
Step 1 — Read context
Read discovery-context.md (sections 1. Client → Stage, 3. Engagement → Mode and Budget context, 6. Constraints) and scope-doc.md.
If discovery-context.md is missing, ask the BA inline: "(a) greenfield or migration; (b) fixed-fee or T&M?" — tag the output [ASSUMED ENGAGEMENT]. If scope-doc.md is missing, ask for in/out scope bullets per area, or fall back to a class-of-magnitude estimate (S / M / L / XL) tagged [NO-SCOPE] — not for client use without a follow-up proper estimate. Never block; recommend profile-builder / feature-scoping for high-stakes work.
Step 2 — Pick estimation method
Match to engagement timeline + scope size:
| Method | When | Effort to produce |
|---|---|---|
| 3-point parametric | Standard for proposals; S/M/L scopes | 30-60 min |
| Bottom-up by user story | Detailed scope, large engagement | 2-4 hours |
| Reference-class ("we did X for Y, multiply by Z") | Repeat engagement, similar scope | 15-30 min |
| Phased gates | Long engagement; only first phase precise | 30 min for phase 1, range for rest |
Default to 3-point parametric for discovery-phase outputs.
Step 3 — Discipline × Phase matrix
Build the matrix. Adapt phases to engagement mode:
| Phase \ Discipline | BA | PM | Design | Dev (FE) | Dev (BE) | QA | DevOps | Total |
|---|---|---|---|---|---|---|---|---|
| Discovery follow-ups | ||||||||
| Design (wireframe → high-fi) | ||||||||
| Build phase 1 | ||||||||
| Build phase 2 | ||||||||
| QA / hardening | ||||||||
| Launch / handoff | ||||||||
| Total per discipline |
What ships with it
1 file 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.
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 · 122 lines · 69 tokens per session scan A 465614f1185c
estimation is a skill published in the GitHub repository stanislavnianko/product-discovery-claude-skills (1 stars, last pushed 5mo ago), licensed MIT. It adds 69 tokens to every session and 1,366 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 2026-08-31.
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