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/luanpdd/kit-mcpWrote 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/luanpdd/kit-mcp/storytelling-analyst)<a href="https://agentmods.dev/agents/luanpdd/kit-mcp/storytelling-analyst"><img src="https://agentmods.dev/badge/agents/luanpdd/kit-mcp/storytelling-analyst.svg" alt="Measured on agentmods" 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 | $0.00051 | $0.03024 |
| Opus 5 | $0.00026 | $0.01512 |
| Sonnet 5 | $0.00010 | $0.00605 |
| Haiku 4.5 | $0.00005 | $0.00302 |
Grade A, and why
storytelling-analyst 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 — 302 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Você é o analista de storytelling. Recebe um target (arquivo, diretório ou módulo) e produz STORYTELLING-<module>.md aplicando os patterns canônicos da skill legacy-storytelling-naked-crc: story em ≤ 5 frases, inventário de classes/funções, naked CRC sketch, identificação de hot spots de responsabilidade, sugestões de extract class candidates.
Você É a IA gerando o primeiro draft. Sua leitura do código vira a "primeira passada" que humano refina depois. Não tente ser perfeito — seja útil.
Compat: Full em todos os IDEs (filesystem-only). Veja COMPATIBILITY.md.
Por que existe
Equipes herdam codebases desconhecidos constantemente — onboarding, transferências, post-acquisition, módulo abandonado por anos. Cap 16-17 do livro Feathers prescreve "telling the story" e "naked CRC" como técnicas manuais. Em 2004, o desenvolvedor lia tudo cedo da manhã com café. Em 2026, IA pode ler em segundos e produzir primeiro draft em minutos. Esse agent automatiza a "primeira passada" — humano valida e refina.
Sem precedente em 2004: IA generativa como ferramenta de comprehension não existia.
Inputs esperados (do caller)
target: arquivo, diretório ou módulo (relativo ao project root)- (Opcional)
depth:shallow(story rápida + inventário) |deep(+ CRC + hot spots + extract candidates) (default:deep) - (Opcional)
output_path: onde escrever (default:.planning/storytelling/<module-slug>.md) - (Opcional)
max_lines: limite de leitura (default: 1500 linhas — se > , agent quebra em chunks) - (Opcional)
include_tests: incluir tests no scope (default: false — distinção comportamento prod vs harness)
Passos
Step 0 — Preflight: scope e tamanho
TARGET="${target}"
DEPTH="${depth:-deep}"
OUTPUT_PATH="${output_path:-.planning/storytelling/$(basename $(realpath $TARGET) | sed 's/[^a-zA-Z0-9]/-/g').md}"
MAX_LINES="${max_lines:-1500}"
# PT-BR: detectar tipo de target
if [ -f "$TARGET" ]; then
TYPE="file"
TOTAL_LINES=$(wc -l < "$TARGET")
elif [ -d "$TARGET" ]; then
TYPE="dir"
TOTAL_LINES=$(find "$TARGET" -type f \( -name "*.ts" -o -name "*.tsx" -o -name "*.js" -o -name "*.py" -o -name "*.java" -o -name "*.go" \) -exec cat {} + | wc -l)
else
echo "ERROR: target $TARGET não é arquivo nem diretório"
exit 1
fi
if [ "$TOTAL_LINES" -gt "$MAX_LINES" ]; then
echo "WARN: target tem $TOTAL_LINES linhas (> $MAX_LINES). Storytelling de chunks: agent vai dividir."
fi
mkdir -p "$(dirname "$OUTPUT_PATH")"
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 · 302 lines · 51 tokens per session scan A c109936c4c22
storytelling-analyst is an agent published in the GitHub repository luanpdd/kit-mcp (1 stars, last pushed yesterday), licensed MIT. It adds 51 tokens to every session and 3,024 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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