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 challengegit 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/challenge)<a href="https://agentmods.dev/skills/lenar-amirov/product-pipeline-public/challenge"><img src="https://agentmods.dev/badge/skills/lenar-amirov/product-pipeline-public/challenge/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/challenge"><img src="https://agentmods.dev/badge/skills/lenar-amirov/product-pipeline-public/challenge.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 | $0.00081 | $0.00633 |
| Opus 5 | $0.00041 | $0.00316 |
| Sonnet 5 | $0.00016 | $0.00127 |
| Haiku 4.5 | $0.00008 | $0.00063 |
Grade A, and why
challenge 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 — 57 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Challenge — /challenge [gate1|gate2|<file>]
The cheapest place to lose a gate is in rehearsal. Attack the PM's deck the way the real audience will — armed with the registry, which knows exactly where the evidence is thin.
0. Preconditions first
Run python3 tools/scripts/validate-evidence.py --gate <dir> — it checks
three things: ≥2 hypotheses confirmed REAL, zero registry violations
(ranges, unreconciled data_inconsistency), Frame complete (metric /
baseline / target / kill criteria). If GATE BLOCKED — stop: report the
blockers and the fastest way to clear each one.
Rehearsing a deck built on unreconciled numbers wastes the PM's time.
1. Load the ammunition
- The deck:
output/presentation.md(Gate 1) oroutput/gate2-presentation.md - The registry:
hypotheses.py show <dir>— statuses, confidences, flags, sources per hypothesis CONTEXT.md— the promised metric/target- Last decisions.md entries — anything promised earlier and not delivered
2. Attack in three personas (in order)
CFO / sponsor — attacks the money and the sizing:
- Where does the effect estimate come from? Which registry source backs each number on the sizing slide?
- What's the cost of being wrong? Kill criteria defined?
- "Why this and not the other initiative competing for the same team?"
VP Product — attacks the problem-solution link:
- Which slide claims rest on SYNTHETIC/INFERRED evidence presented as fact? (cross-check every proof slide against the registry — this is the highest-yield attack)
- Refuted or flagged hypotheses: does the deck quietly rely on any?
- Segment sizes: do funnel numbers on different slides agree?
Skeptic engineer / analyst — attacks the data:
- Metric definitions: same metric, same definition on every slide?
- data_inconsistency flags: "these two numbers contradict — which is true?"
- Baseline windows and seasonality; sample sizes for any test claims.
3. Report
For each hit: slide/claim → the attack question → severity (fatal / painful / cosmetic) → how to fix before the gate (patch the slide, move to speaker notes, reconcile the number, downgrade the claim). Fatal = presentation should not go out; list fatals first. End with the 3 questions most likely to actually be asked, and suggested answers backed by registry sources.
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 · 57 lines · 81 tokens per session scan A 56a75f16e14a
challenge is a skill published in the GitHub repository lenar-amirov/product-pipeline-public (12 stars, last pushed 22d ago), licensed MIT. It adds 81 tokens to every session and 633 once invoked, about $0.0004 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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