Borrowing it
Nothing to install: this file belongs to senda-labs/DQIII8. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/senda-labs/DQIII8/main/.claude/skills/intl-reports/SKILL.mdgit clone --depth 1 https://github.com/senda-labs/DQIII8Wrote 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/senda-labs/dqiii8/intl-reports)<a href="https://agentmods.dev/skills/senda-labs/dqiii8/intl-reports"><img src="https://agentmods.dev/badge/skills/senda-labs/dqiii8/intl-reports/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/senda-labs/dqiii8/intl-reports"><img src="https://agentmods.dev/badge/skills/senda-labs/dqiii8/intl-reports.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 | $0.00053 | $0.02349 |
| Opus 5 | $0.00026 | $0.01175 |
| Sonnet 5 | $0.00011 | $0.00470 |
| Haiku 4.5 | $0.00005 | $0.00235 |
Grade A, and why
intl-reports 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 — 201 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/intl-reports — Orquestador de Informes de Internacionalización
Proyecto en /root/dqiii8/my-projects/intl-reports/.
CSV tanda3: data/3a tanda empresas 201 P&L 28 abril.csv (201 empresas).
Arquitectura
Orchestrator v4 (core.cli) = pipeline completo por empresa, vía claude --print subprocess.
NUNCA lanzar desde Claude Code activo (CLAUDECODE=1 bloquea con error explícito).
Siempre desde terminal/tmux externo:
env -u CLAUDECODE python3 -m core.cli run --slug {slug} --concurrency 6
Pipeline batch (modo producción)
# Batch secuencial desde tmux externo — NO desde Claude Code
bash scripts/batch_run_tanda3.sh data/tanda3_run_ready.txt
El script:
- Salta empresas con ambos DOCXs ya existentes (>500 KB c/u)
- Detecta estado parcial →
resume; empresa fresh →run - Para en
exit 2si detecta rate limit (reanudar después) - Flags:
--concurrency 6 --skip-brief
Waves del Orchestrator v4
Wave -2 crawler auto skip si dossier < 30d
Wave -1 implications_brief auto [Haiku], --skip-brief lo omite si ya existe
Wave 0 strategic + diag_intro + diag_areas×6 [8 en paralelo]
Wave 1 market_signals_layer + diag_conclusions
Wave 2 plan_body_org + plan_body_financial
Wave 3 plan_body_governance + plan_body_entry
Wave 4 plan_body_marketing
Wave 5 markets + plan_body_recommendations
Post QA → auto_qa_fixer → DOCX → Telegram
Prerrequisitos por empresa (gate real del pipeline)
A. SABI_Export_*.xls info-origin/ financiero (empleados, revenue)
B. raw_survey_data.json info-origin/ cuestionario ANOVA (auto USIL)
C. ssot.json data/ fuente canónica (reemplaza content_brief.json, eliminado en B7)
D. company_intelligence.json data/ REQUERIDO para pasar ACIS gate (completeness ≥85%)
└── generado por cobrowsing session:
python3 scripts/cobrowsing_batch.py --slug-list data/tanda3_no_acis.txt
(Chrome human-in-the-loop, CDP :9222, TUI interactiva)
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 · 201 lines · 53 tokens per session scan A edddf6c8e4e6
intl-reports is a skill published in the GitHub repository senda-labs/DQIII8 (11 stars, last pushed 23d ago), licensed MIT. It adds 53 tokens to every session and 2,349 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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