chore: auto-commit (799 archivos)

- .claude/CLAUDE.md
- .claude/commands/subagentes.md
- .claude/rules/INDEX.md
- .mcp.json
- bash/functions/cybersecurity/analyze_dns.md
- bash/functions/cybersecurity/audit_http_headers.md
- bash/functions/cybersecurity/audit_ssh_config.md
- bash/functions/cybersecurity/check_firewall.md
- bash/functions/cybersecurity/detect_suspicious_users.md
- bash/functions/cybersecurity/encrypt_file.md
- ...

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

cpp/functions/datascience/beta_dist.md

@@ -7,7 +7,7 @@ version: "1.0.0"
 purity: pure
 signature: "double lgamma_lanczos(double x); double log_beta(double a, double b); double beta_pdf(double x, double a, double b); double beta_cdf(double x, double a, double b); double beta_quantile(double p, double a, double b); double beta_mean(double a, double b); double beta_variance(double a, double b); double beta_std(double a, double b)"
 description: "Distribucion Beta(a,b) completa: log-Gamma (Lanczos), log B(a,b), pdf, cdf (incomplete beta via continued fraction), quantile (bisection), mean/var/std. Para inferencia Bayesiana Beta-Binomial (mcmc-bayes / mcmc-full)."
-tags: [beta, distribution, bayesian, lgamma, incomplete_beta, datascience]
+tags: [beta, distribution, bayesian, lgamma, incomplete_beta, datascience, pendiente-usar]
 uses_functions: []
 uses_types: []
 returns: []

cpp/functions/datascience/drawdown.md

@@ -7,7 +7,7 @@ version: "1.0.0"
 purity: pure
 signature: "DrawdownResult drawdown_max(const double* equity, size_t n); void drawdown_series(const double* equity, size_t n, double* out)"
 description: "Max drawdown sobre serie de equity/balance: peak-to-trough absoluto y porcentual + indices del peak y trough relevantes. drawdown_series llena un array con el underwater chart (peak_so_far - equity[i] en cada punto)."
-tags: [drawdown, equity, finance, underwater, montecarlo, datascience]
+tags: [drawdown, equity, finance, underwater, montecarlo, datascience, pendiente-usar]
 uses_functions: []
 uses_types: []
 returns: []

cpp/functions/datascience/mc_metropolis_hastings_gpu.md

@@ -7,7 +7,7 @@ version: "1.0.0"
 purity: impure
 signature: "McMetropolisHastingsGpu mc_mh_gpu_create(int m_chains, int n_samples_per_run, const std::string& target_log_pdf_glsl); void mc_mh_gpu_reset(...); void mc_mh_gpu_run(...); void mc_mh_gpu_readback_chains(...); void mc_mh_gpu_readback_accepts(...); void mc_mh_gpu_destroy(...)"
 description: "Metropolis-Hastings 1D paralelo en GPU: M cadenas independientes (1 thread = 1 chain). Target log-pdf inyectable como string GLSL (igual patron que gl_shader). Soporta u_user[16] para parametros sin recompilar."
-tags: [montecarlo, mcmc, metropolis, gpu, sampling, datascience]
+tags: [montecarlo, mcmc, metropolis, gpu, sampling, datascience, pendiente-usar]
 uses_functions: ["gl_loader_cpp_gfx", "gpu_ssbo_cpp_gfx", "gpu_compute_program_cpp_gfx", "gpu_dispatch_cpp_gfx", "gpu_rng_glsl_cpp_gfx"]
 uses_types: []
 returns: []

cpp/functions/datascience/mc_random_walk_2d_gpu.md

@@ -7,7 +7,7 @@ version: "1.0.0"
 purity: impure
 signature: "McRandomWalk2DGpu mc_rw2d_gpu_create(int m_walkers, int n_steps_per_run, const std::string& target_log_pdf_glsl); void mc_rw2d_gpu_reset(...); void mc_rw2d_gpu_run(...); void mc_rw2d_gpu_readback_trace(...); void mc_rw2d_gpu_readback_accepts(...); const Ssbo& mc_rw2d_gpu_trace_ssbo(const McRandomWalk2DGpu&); void mc_rw2d_gpu_destroy(...)"
 description: "Random walk 2D MH paralelo en GPU. Cada thread es un walker independiente; trace_xy se almacena como float[2*N] xy-interleaved compatible directamente con gpu_histogram_2d. Para mcmc-visualizer y joint posteriors."
-tags: [montecarlo, mcmc, random_walk, 2d, gpu, datascience]
+tags: [montecarlo, mcmc, random_walk, 2d, gpu, datascience, pendiente-usar]
 uses_functions: ["gl_loader_cpp_gfx", "gpu_ssbo_cpp_gfx", "gpu_compute_program_cpp_gfx", "gpu_dispatch_cpp_gfx", "gpu_rng_glsl_cpp_gfx"]
 uses_types: []
 returns: []

cpp/functions/datascience/mc_session_sim_gpu.md

@@ -7,7 +7,7 @@ version: "1.0.0"
 purity: impure
 signature: "McSessionSim mc_session_sim_create(int n_sessions, int max_tiers); void mc_session_sim_reseed(McSessionSim&, uint64 seed); void mc_session_sim_run(McSessionSim&, const McSessionParams&); void mc_session_sim_readback_summary(const McSessionSim&, float* out); void mc_session_sim_readback_tier_counts(const McSessionSim&, unsigned int* out); void mc_session_sim_destroy(McSessionSim&)"
 description: "N sesiones independientes de K spins en paralelo en GPU (1 thread = 1 sesion). Implementa el modelo variable-ratio escalonado de vr_tiered_lab: tiers (q, m), modes Pure/Pity/Streak, miss_streak, drawdown. Output SSBOs: summary[N*8] + tier_counts[N*max_tiers]."
-tags: [montecarlo, gpu, simulation, vr_tiered, sessions, datascience]
+tags: [montecarlo, gpu, simulation, vr_tiered, sessions, datascience, pendiente-usar]
 uses_functions: ["gl_loader_cpp_gfx", "gpu_ssbo_cpp_gfx", "gpu_compute_program_cpp_gfx", "gpu_dispatch_cpp_gfx", "gpu_rng_glsl_cpp_gfx"]
 uses_types: []
 returns: []

cpp/functions/datascience/metropolis_hastings.md

@@ -7,7 +7,7 @@ version: "1.0.0"
 purity: pure
 signature: "MHResult mh_run_1d(const std::function<double(double)>& log_pdf, double x0, double sigma, size_t n, double* out, Rng&); MHResult mh_run_nd(const std::function<double(const double*)>& log_pdf, const double* x0, const double* sigma, int d, size_t n, double* out, Rng&)"
 description: "Metropolis-Hastings 1D y d-dimensional con proposal Gaussian symmetric. Target log-pdf inyectable via std::function (no necesita normalizarse). Devuelve cadena en out[] y accept rate. Pareja CPU del mc_metropolis_hastings_gpu."
-tags: [mcmc, metropolis, hastings, sampling, bayesian, datascience]
+tags: [mcmc, metropolis, hastings, sampling, bayesian, datascience, pendiente-usar]
 uses_functions: ["rng_cpp_datascience"]
 uses_types: []
 returns: []

cpp/functions/datascience/rhat_ess.md

@@ -7,7 +7,7 @@ version: "1.0.0"
 purity: pure
 signature: "double rhat(const double* chains, size_t m, size_t n); double rhat_split(const double* chains, size_t m, size_t n); double ess_basic(const double* chains, size_t m, size_t n, size_t max_lag, double cutoff)"
 description: "Diagnosticos multi-chain MCMC: Gelman-Rubin R-hat (clasico y split), y Effective Sample Size basico. Cadenas en layout row-major chains[j*n + i]. Convergencia tipica R_hat < 1.01."
-tags: [mcmc, rhat, ess, gelman_rubin, convergence, datascience]
+tags: [mcmc, rhat, ess, gelman_rubin, convergence, datascience, pendiente-usar]
 uses_functions: ["autocorr_cpp_datascience"]
 uses_types: []
 returns: []

cpp/functions/datascience/samples_to_grid_2d.md

@@ -7,7 +7,7 @@ version: "1.0.0"
 purity: pure
 signature: "void samples_to_grid_2d_counts(const double* x, const double* y, size_t n, double xmin, double xmax, double ymin, double ymax, int nx, int ny, unsigned int* out_counts); void samples_to_grid_2d_density(...float* out_density); void counts_to_density(const unsigned int* counts, int nx, int ny, float* out_density)"
 description: "Binning 2D CPU para alimentar heatmap_cpp_viz / contour_cpp_viz / surface_plot_3d desde un set de samples (x[], y[]). Variante counts (uint, acumulable) y density (float [0,1] normalizado a max). Pareja CPU del gpu_histogram_2d."
-tags: [binning, histogram_2d, density, heatmap, contour, datascience]
+tags: [binning, histogram_2d, density, heatmap, contour, datascience, pendiente-usar]
 uses_functions: []
 uses_types: []
 returns: []

cpp/functions/datascience/stats_summary.md

@@ -7,7 +7,7 @@ version: "1.0.0"
 purity: pure
 signature: "double stats_sum(const double*, size_t); double stats_mean(const double*, size_t); double stats_min(const double*, size_t); double stats_max(const double*, size_t); double stats_variance(const double*, size_t, bool sample=true); double stats_std(const double*, size_t, bool sample=true); double stats_quantile(const double*, size_t, double p); double stats_quantile_sorted(const double*, size_t, double p); double stats_percentile(const double*, size_t, double pct); void stats_sort(const double*, size_t, double* out)"
 description: "Estadistica descriptiva pura sobre arrays double: sum (Kahan), mean, min, max, variance/std (Welford one-pass, sample/poblacional), quantile (R type-7) y percentile. stats_sort externalizable para evitar copias en queries multiples."
-tags: [stats, mean, variance, std, quantile, percentile, welford, datascience]
+tags: [stats, mean, variance, std, quantile, percentile, welford, datascience, pendiente-usar]
 uses_functions: []
 uses_types: []
 returns: []