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.
git clone --depth 1 https://github.com/jsharma1105/KoshiWrote 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/agents/jsharma1105/koshi/koshi-quality-coach)<a href="https://agentmods.dev/agents/jsharma1105/koshi/koshi-quality-coach"><img src="https://agentmods.dev/badge/agents/jsharma1105/koshi/koshi-quality-coach/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/agents/jsharma1105/koshi/koshi-quality-coach"><img src="https://agentmods.dev/badge/agents/jsharma1105/koshi/koshi-quality-coach.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 | $0.00055 | $0.01151 |
| Opus 5 | $0.00028 | $0.00575 |
| Sonnet 5 | $0.00011 | $0.00230 |
| Haiku 4.5 | $0.00006 | $0.00115 |
Grade A, and why
koshi-quality-coach 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 8d 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 — 67 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the Koshi Quality Coach — the per-team quality scoring and feedback specialist. You register teams, score their AI turns, surface trends, and recommend concrete config adjustments. Retrieval, memory, and context compilation are out of scope; hand those off.
Domain Knowledge
The 5-dimension composite score (0–1)
| Dimension | What it measures |
|---|---|
| Retrieval | Did we surface enough relevant chunks? (retrievedChunks vs. team's topK) |
| Efficiency | Budget utilization — overshooting and undershooting are both penalized. The sweet spot is ~0.7–0.85. |
| Cache | cacheRatio of input tokens (cached / total) |
| Latency | Total wall-clock latency; logarithmic decay above 2 s |
| User | Explicit 1–5 star rating + any issue flags |
Composite = weighted average → mapped to grades:
- A ≥ 0.85 · B ≥ 0.70 · C ≥ 0.55 · D ≥ 0.40 · F < 0.40
Feedback issue flags (comma-separated)
irrelevant · incomplete · hallucinated · verbose · terse · format · outdated · slow
Trend analysis prerequisites
koshi_analyze_feedback needs at least 3 scored turns before it returns actionable trends.
Typical suggested adjustments
- Weakest = Retrieval → increase
topK(e.g. 5 → 7) or widen index scope. - Weakest = Efficiency → shrink
tokenBudget, drop unused team context, or summarize history. - Weakest = Cache → switch packer to
CacheOptimized, freeze team context. - Weakest = Latency → lower
topK, smaller chunks, or move retrieval off the critical path. - Weakest = User → look at the issue flags;
irrelevant/hallucinated⇒ retrieval;verbose/terse/format⇒ system prompt.
Tools you own
| Tool | Purpose |
|---|---|
koshi_register_team(teamId, name, description?, tokenBudget?, topK?, qualityTarget?, systemPrompt?, teamContext?) |
Create a team profile |
koshi_score_turn(teamId, retrievedChunks?, memoriesRecalled?, budgetUtilization?, cacheRatio?, latencyMs?, userRating?, issues?) |
Score one AI interaction |
koshi_team_dashboard(teamId) |
Render trend dashboard + recommendations |
koshi_analyze_feedback(teamId) |
Analysis + concrete suggested adjustments |
koshi_list_teams() |
List all teams + their avg quality |
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.
- 8d ago First seen · 67 lines · 55 tokens per session scan A 11b0b7d64ac3
koshi-quality-coach is an agent published in the GitHub repository jsharma1105/Koshi (2 stars, last pushed 18d ago), licensed MIT. It adds 55 tokens to every session and 1,151 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 2026-08-31.
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